task_type
stringclasses 1
value | dataset
stringclasses 1
value | input
stringlengths 11
389
| output
stringlengths 43
461
| situation
stringclasses 1
value | label
stringclasses 1
value | extra
stringclasses 1
value | instruction
stringclasses 1
value |
|---|---|---|---|---|---|---|---|
generation
|
absa-quad
|
['I found the food to be outstanding , particulary the salmon dish I had .']
|
[['food', 'food quality', 'positive', 'outstanding'], ['salmon dish', 'food quality', 'positive', 'outstanding']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['My wife had the fried shrimp which are huge and loved it .']
|
[['fried shrimp', 'food style_options', 'positive', 'huge'], ['fried shrimp', 'food quality', 'positive', 'loved']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Try sushimi cucumber roll .']
|
[['sushimi cucumber roll', 'food quality', 'positive', 'Try']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Amazing food .']
|
[['food', 'food quality', 'positive', 'Amazing']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['I do not recommend .']
|
[['NULL', 'restaurant general', 'negative', 'do not recommend']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Truly the mark of an attentive waiter .']
|
[['waiter', 'service general', 'positive', 'attentive']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
["Wouldn 't recomend it for dinner !"]
|
[['NULL', 'restaurant general', 'negative', "Wouldn 't recomend"]]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
["The pizza was delivered cold and the cheese was n't even fully melted !"]
|
[['pizza', 'food quality', 'negative', 'cold'], ['cheese', 'food quality', 'negative', "was n't even fully melted"]]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['the last time i walked by it looked pretty empty . hmmm .']
|
[['NULL', 'restaurant miscellaneous', 'negative', 'empty']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
["I 'd be horrified if my staff were turning away customers so early and so rudely !"]
|
[['staff', 'service general', 'negative', 'horrified']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
["Which lets face it ... .at times it 's a good thing ."]
|
[['NULL', 'service general', 'positive', 'good']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['The waiter delivered our food while holding what appeared to be a plastic bag of garbage in one hand .']
|
[['waiter', 'service general', 'negative', 'garbage']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['You can not go wrong with this place .']
|
[['place', 'restaurant general', 'positive', 'wrong']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['I am so coming back here again , as much as I can .']
|
[['NULL', 'restaurant general', 'positive', 'coming back']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['We paid a fixed pricce but got nothing ! !']
|
[['NULL', 'service general', 'negative', 'got nothing']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['We ordered the chicken casserole , but what we got were a few small pieces of chicken , all dark meat and on the bone .']
|
[['chicken casserole', 'food quality', 'negative', 'all dark meat and on the bone '], ['chicken casserole', 'food style_options', 'negative', 'small']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['When I got there the place was packed but they made sure to seat me quickly .']
|
[['NULL', 'service general', 'positive', 'seat me quickly ']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['The sushi was awful !']
|
[['sushi', 'food quality', 'negative', 'awful']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Slightly on the pricey side but worth it !']
|
[['NULL', 'restaurant prices', 'negative', 'pricey'], ['NULL', 'restaurant general', 'positive', 'worth']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['The staff is no nonsense .']
|
[['staff', 'service general', 'positive', 'no nonsense']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['its alright']
|
[['NULL', 'restaurant general', 'neutral', 'alright']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['All of the pizzas are terrific and the price is even better !']
|
[['pizzas', 'food quality', 'positive', 'terrific'], ['NULL', 'restaurant prices', 'positive', 'even better']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Cute place , nice wait staff but would never go there again .']
|
[['wait staff', 'service general', 'positive', 'nice'], ['place', 'ambience general', 'positive', 'Cute'], ['place', 'restaurant general', 'negative', 'never go']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['The entree was bland and small , dessert was not inspired .']
|
[['entree', 'food quality', 'negative', 'bland'], ['entree', 'food style_options', 'negative', 'small'], ['dessert', 'food quality', 'negative', 'not inspired']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Our family never expected such incredible entertainment in a restaurant .']
|
[['entertainment', 'ambience general', 'positive', 'incredible']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['noodles with shrimp and chicken and coconut juice is the MUST !']
|
[['noodles with shrimp and chicken and coconut juice', 'food quality', 'positive', 'MUST']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['The scallion pancakes and fried dumplings were nothing out of the ordinary .']
|
[['scallion pancakes', 'food quality', 'neutral', 'ordinary'], ['fried dumplings', 'food quality', 'neutral', 'ordinary']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['I really liked this place .']
|
[['place', 'restaurant general', 'positive', 'liked']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Super YUMMY Pizza !']
|
[['Pizza', 'food quality', 'positive', 'YUMMY']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['I have NEVER been disappointed in the Red Eye .']
|
[['Red Eye', 'restaurant general', 'positive', 'NEVER been disappointed']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Will absolutely visit again .']
|
[['NULL', 'restaurant general', 'positive', 'visit again']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['I ordered the vitello alla marsala and I was pretty impressed .']
|
[['vitello alla marsala', 'food quality', 'positive', 'impressed']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Quick and friendly service .']
|
[['service', 'service general', 'positive', 'Quick'], ['service', 'service general', 'positive', 'friendly']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Once we finally got a table , despite indicating we wanted an alla carte menu we were pushed into a table that was only price fixed !']
|
[['NULL', 'service general', 'negative', 'pushed']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Warning : You may find it difficult to dine at other Japanese restaurants after a visit to Mizu !']
|
[['Mizu', 'restaurant general', 'positive', 'difficult']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['What a great place !']
|
[['place', 'restaurant general', 'positive', 'great']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Myagi is one of my favorite restaurants in the City ; the place the negative reviews describe sound like they were somewhere else .']
|
[['Myagi', 'restaurant general', 'positive', 'favorite']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Cool atmosphere , the fire place in the back really ads to it but needs a bit more heat throughout on a cold night .']
|
[['atmosphere', 'ambience general', 'positive', 'Cool'], ['fire place', 'ambience general', 'positive', 'really ads to it']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['The atmosphere is nothing special , but it feels like a Sushi establishment in Tokyo .']
|
[['atmosphere', 'ambience general', 'positive', 'nothing special']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['The tuna and wasabe potatoes are excellent .']
|
[['tuna', 'food quality', 'positive', 'excellent'], ['wasabe potatoes', 'food quality', 'positive', 'excellent']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Have never had a problem with service save a missing rice once .']
|
[['service', 'service general', 'positive', 'problem']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['We were worried we would have trouble getting in , but somehow managed to have a short wait .']
|
[['wait', 'service general', 'positive', 'short']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Inside is a little cramped , but to be expected .']
|
[['NULL', 'ambience general', 'neutral', 'to be expected']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['I was pleasantly surprised to find this gem in Hoboken .']
|
[['NULL', 'restaurant general', 'positive', 'pleasantly surprised']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['The appetizers we ordered were served quickly - an order of fried oysters and clams were delicious but a tiny portion ( maybe 3 of each ) .']
|
[['fried oysters and clams', 'food quality', 'positive', 'delicious'], ['fried oysters and clams', 'food style_options', 'negative', 'tiny portion'], ['NULL', 'service general', 'positive', 'served quickly']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
["I ca n't wait for summer , when they serve outside on their gigantic patio ."]
|
[['patio', 'ambience general', 'positive', 'gigantic']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Keep up the good work guys !']
|
[['NULL', 'restaurant general', 'positive', 'good']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['stick with the chicken , beef , and lamb dishes .']
|
[['chicken', 'food quality', 'positive', 'stick'], ['beef', 'food quality', 'positive', 'stick'], ['lamb dishes', 'food quality', 'positive', 'stick']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['A little overpriced but worth it once you take a bite .']
|
[['NULL', 'food prices', 'negative', 'overpriced'], ['NULL', 'food quality', 'positive', 'worth']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Over all it was a very nice romantic place .']
|
[['place', 'restaurant general', 'positive', 'nice romantic'], ['place', 'ambience general', 'positive', 'nice romantic']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Rather than preparing vegetarian dish , the chef presented me with a plate of steamed vegetables ( minus sauce , seasoning , or any form or aesthetic presentation ) .']
|
[['vegetarian dish', 'food style_options', 'negative', 'minus sauce , seasoning , or any form or aesthetic presentation']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Good , fast service .']
|
[['service', 'service general', 'positive', 'Good'], ['service', 'service general', 'positive', 'fast']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['A big disappointment , all around .']
|
[['NULL', 'restaurant general', 'negative', 'disappointment']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['This is the BEST Shabu-Shabu Restaurant in the Try-State Area .']
|
[['Shabu-Shabu Restaurant', 'restaurant general', 'positive', 'BEST']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['My boyfriend and I went there to celebrate my birthday the other night and all I can say is that it was magnificent .']
|
[['NULL', 'restaurant general', 'positive', 'magnificent']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['The place was nice and calm .']
|
[['place', 'ambience general', 'positive', 'nice'], ['place', 'ambience general', 'positive', 'calm']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['The lobster sandwich is good and the spaghetti with Scallops and Shrimp is great .']
|
[['lobster sandwich', 'food quality', 'positive', 'good'], ['spaghetti with Scallops and Shrimp', 'food quality', 'positive', 'great']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Planet Thailand has always been a hit with me , I go there usually for the sushi , which is great , the thai food is excellent too .']
|
[['sushi', 'food quality', 'positive', 'great'], ['thai food', 'food quality', 'positive', 'excellent'], ['Planet Thailand', 'restaurant general', 'positive', 'hit']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['would NOT go back .']
|
[['NULL', 'restaurant general', 'negative', 'NOT go back']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['We were charged full price .']
|
[['NULL', 'service general', 'negative', 'full price']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Will comeback for sure , wish they have it here in LA . .']
|
[['NULL', 'restaurant general', 'positive', 'comeback']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['You can not go wrong at the Red Eye Grill .']
|
[['Red Eye Grill', 'restaurant general', 'positive', 'can not go wrong']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['All in all , the food was great ( except for the dessserts ) .']
|
[['food', 'food quality', 'positive', 'great']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['When family came in he gave them apps to test their palets , and then ordered for them .']
|
[['NULL', 'service general', 'positive', 'ordered for them']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['There is no excuse for such lousy service !']
|
[['service', 'service general', 'negative', 'lousy']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Save your money and your time and go somewhere else .']
|
[['NULL', 'restaurant general', 'negative', 'go somewhere else']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['We were very disappointed .']
|
[['NULL', 'food quality', 'negative', 'disappointed']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Also , waiters try to push more food on you , like suggest things as if they are complimentary when they actually cost $ .']
|
[['waiters', 'service general', 'negative', 'push']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['The place itself is beautiful the bar scene seems to be happening .']
|
[['place', 'ambience general', 'positive', 'beautiful']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['For the people who want great food plus great service , Roxy is a place to AVOID !']
|
[['food', 'food quality', 'negative', 'great'], ['service', 'service general', 'negative', 'great']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['When we stumbled on Leon , we thought that we had found quite the gem BUT , we were certainly wrong .']
|
[['Leon', 'restaurant general', 'negative', 'certainly wrong']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['The pizza is yummy and I like the atmoshpere .']
|
[['pizza', 'food quality', 'positive', 'yummy'], ['atmoshpere', 'ambience general', 'positive', 'like']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['The Seafood Dynamite is also otherworldly .']
|
[['Seafood Dynamite', 'food quality', 'positive', 'otherworldly']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['this place had ALL the trimmings and i mean all .']
|
[['trimmings', 'food style_options', 'positive', 'ALL']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['I had the best ravioli ever .']
|
[['ravioli', 'food quality', 'positive', 'best']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['I can not imagine better Indian food in all of the city .']
|
[['Indian food', 'food quality', 'positive', 'better']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Cozy romantic atomosphere with only around 15 tables at most .']
|
[['atomosphere', 'ambience general', 'positive', 'Cozy romantic']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Other guests enjoyed pizza , santa fe chopped salad and fish and chips .']
|
[['pizza', 'food quality', 'positive', 'enjoyed'], ['santa fe chopped salad', 'food quality', 'positive', 'enjoyed'], ['fish and chips', 'food quality', 'positive', 'enjoyed']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Best Taiwanese food in NY !']
|
[['Taiwanese food', 'food quality', 'positive', 'Best']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['The food was mediocre at best but it was the horrible service that made me vow never to go back .']
|
[['food', 'food quality', 'negative', 'mediocre'], ['service', 'service general', 'negative', 'horrible']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['If you are looking for a good quality , cheap eats - this is the place .']
|
[['eats', 'food quality', 'positive', 'good quality'], ['eats', 'food prices', 'positive', 'cheap']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['We ordered some beef and noodle soup dishes from the Thai section of the menu but nothing we got was Thai .']
|
[['beef and noodle soup dishes', 'food quality', 'negative', 'nothing we got was Thai']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
["The bibimbap was average , but the stone bowl was n't even close to sizzling ."]
|
[['bibimbap', 'food quality', 'neutral', 'average'], ['stone bowl', 'food quality', 'negative', "was n't even close to sizzling"]]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
["This place , which is only a few months old , is perhaps Queens ' biggest secret !"]
|
[['place', 'restaurant general', 'positive', "Queens ' biggest secret"]]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Food was good not great not worth the wait or another visit']
|
[['Food', 'food quality', 'neutral', 'good not great not worth the wait or another visit']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Not sure where the previous reviewer , lonk , dined , but Saul is in a great neighborhood and has great food !']
|
[['neighborhood', 'location general', 'positive', 'great'], ['food', 'food quality', 'positive', 'great']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['My friend got the mushroom pizza which tasted better .']
|
[['mushroom pizza', 'food quality', 'positive', 'better']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['I paid just about $ 60 for a good meal , though : )']
|
[['meal', 'food quality', 'positive', 'good'], ['meal', 'food prices', 'positive', 'good']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Nice view of river and NYC .']
|
[['view of river and NYC', 'location general', 'positive', 'Nice']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['I found it on a cold night , the perfect spot to warm up .']
|
[['spot', 'restaurant miscellaneous', 'positive', 'perfect']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['I was very disappointed with this restaurant .']
|
[['restaurant', 'restaurant general', 'negative', 'disappointed']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['MMmmm ... it was delicious .']
|
[['NULL', 'food quality', 'positive', 'delicious']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
["I 'd definitely go back again ."]
|
[['NULL', 'restaurant general', 'positive', 'go back']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['The omlette for brunch is great ...']
|
[['omlette for brunch', 'food quality', 'positive', 'great']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['The fried dumplings are GREAT !']
|
[['fried dumplings', 'food quality', 'positive', 'GREAT']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['If you are going for the food , it will not be worth it .']
|
[['food', 'food quality', 'negative', 'worth']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Never again !']
|
[['NULL', 'restaurant general', 'negative', 'Never']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
["First of all , this place is *not* romantic , as claimed by Citysearch 's editorial review ."]
|
[['place', 'ambience general', 'negative', '*not* romantic']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Got a date ? Go here !']
|
[['NULL', 'restaurant miscellaneous', 'positive', 'Go here']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['My fav was the sassy lassi ...']
|
[['sassy lassi', 'drinks quality', 'positive', 'fav']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
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