With the increase of biased information available online , the importance of analysis and detection of such content has also significantly risen . In this paper , we aim to quantify different kinds of social biases using word embeddings . Towards this goal we train such embeddings on two politically biased MediaWiki instances , namely RationalWiki and Conservapedia . However , exploitation of corpus data for this purpose is complicated due to the diverse functions of reflexive markers crossing the domain of reflexivity and reciprocity . The list of verbs identified by the previous procedure is thus further used in an automatic experiment , applying word embeddings for detecting semantically similar verbs . These candidate verbs have been manually verified and annotation of their reflexive and reciprocal constructions has been integrated into the valency lexicon of Czech verbs VALLEX . Given a cluster of scientific documents where one is a reference paper ( RP ) and the remaining documents are papers citing the reference , two tasks are proposed : ( i ) to identify which sentences in the reference paper are being cited and why they are cited , and ( ii ) to produce a citation-based summary of the reference paper using the information in the cluster . Our systems are based on both supervised ( LSTM and convolutional neural networks ) and unsupervised techniques using word embedding representations and features computed from the linguistic and semantic analysis of the documents . Our system is based on a list of sentiment seed words adapted for tweets . The sentiment relations between seed words and other terms are captured by cosine similarity between the word embedding representations ( word2vec ) . These seed words are extracted from datasets of annotated tweets available online . We will show that our approach has superior performance when the perception error rate ( PER ) is higher , and competitive performance when the PER is lower . We will also explore the possibility of using importance sampling techniques to handle observed action distributions with traditional word vector embeddings . We will show that although such approaches can give good recognition accuracy , they take significantly longer training time and their performance will degrade significantly at higher perception error rate . Visualization can aid in such interpretation of embeddings . In this paper , we examine the role for visualization in working with word vector embeddings . We provide a literature survey to catalogue the range of tasks where the embeddings are employed across a broad range of applications . Both methods obtain higher recall scores compared with the baseline , with a loss in precision . Besides higher recall , the word embeddings based approach achieves higher F-measure than the baseline and the tf.rf based methods . We also show that incorporating gene name and interaction keyword identification leads to improved precision and F-measure scores for all three evaluated methods . Bengio:2003:NPL:944919.944966 are pioneers of neural language models . To address the curse of dimensionality in language modeling , they proposed a method using word embeddings and a feed - forward neural network ( FFNN ) . They demonstrated that their approach outperformed n - gram language models , but FFNN can only handle fixed - length contexts . Next , in Table [ reference ] , we show that fine - tuning the word embeddings also improves results , again for both the in - domain task and cross - domain tasks ( the ablation results are based on a smaller model with a 128 + 256 2 - layer biLSTM ) . Hence , all our models were trained with word embeddings being fine - tuned . The last ablation in Table [ reference ] shows that a classifier with two layers of relu is preferable than other options . subsection : Experimental Setup We use pre - trained - Glove vectors to initialize our word embedding vectors . All hidden states of BiLSTMs during input encoding and inference have dimensions ( and ) . The explosion of user generated content on the Web , especially the fact that millions of users , on a daily basis , express their opinions on products and services to blogs , wikis , social networks , message boards , etc . , render the reliable , automated export of sentiments and opinions from unstructured text crucial for several commercial applications . In this paper , we present a novel hybrid vectorization approach for textual resources that combines a weighted variant of the popular Word2Vec representation ( based on Term Frequency-Inverse Document Frequency ) representation and with a Bag- of-Words representation and a vector of lexicon-based sentiment values . The proposed text representation approach is assessed through the application of several machine learning classification algorithms on a dataset that is used extensively in literature for sentiment detection . The goal of this study is to demonstrate how network science and graph theory tools and concepts can be effectively used for exploring and comparing semantic spaces of word embeddings and lexical databases . Specifically , we construct semantic networks based on word2vec representation of words , which is “ learnt ” from large text corpora ( Google news , Amazon reviews ) , and “ human built ” word networks derived from the well-known lexical databases : WordNet and Moby Thesaurus . We compare “ global ” ( e.g. , degrees , distances , clustering coefficients ) and “ local ” ( e.g. , most central nodes and community-type dense clusters ) characteristics of considered networks . To be able to visualize the learning process , we had to record all the weights from every nth iteration . For experiments we used three neural networks : a neural network for digit recognition ( DR ) , a neural network for word2vec representation of words ( WV ) , and a neural network which learned the simple logical gate XOR , ( XR ) . For the purpose of further comparisons , we also constructed one example of a damaged network ( DR ’ , WV ’ , XR ’ ) for each of the three original networks ( DR , WV , XR ) . This approach significantly improves the relevance of the generated poems by representing each line of the poem not only in a context-sensitive manner but also in a holistic way that is highly related to the given keyword and the learned topic . The proposed augmented word2vec model further improves the rhythm and symmetry . We also present a straightforward evaluation metric RHYTHM score to automatically measure the rule-consistency of generated poems . This approach significantly improves the relevance of the generated poems by representing each line of the poem not only in a context-sensitive manner but also in a holistic way that is highly related to the given keyword and the learned topic . A proposed augmented word2vec model further improves the rhythm and symmetry . Tests show that the generated poems by our approach are mostly satisfying with regulated rules and consistent themes , and 73.42 % of them receive an Overall score no less than 3 ( the highest score is 5 ) . We present the first to our knowledge experimental quality evaluation of vector space models for text representations of educational course programme documents . More specifically we compare the quality of various popular algorithms : TF-IDF , LSA , LDA , averaged word2vec , paragraph2vec . The evaluation is carried out using various algorithms of clustering and classification on our experimental corpus of educational course programme , used by Russian universities . The traditional duplicate long text detection algorithms are hard to be applied in the current situations , so more effective duplicate detection algorithm for short text is needed . Based on the bag-of-words model Word2vec , this paper proposes a kind of duplicate detection algorithm with semantic embedded for short text . Words are embedded into vectors which are as input elements in Simhash algorithm to acquire 64 bits sequence , then compare two sequences with Hamming distances and return result filtered by preset threshold value . Subsequently , a more superior improvement is proposed where we add weighted idea into . The results are compared with the unweighted Word2vec method and the traditional TF-IDF method . Experiments are carried out on the SICK corpus , and its result shows that the weighted Word2vec method achieves higher accuracy and recall rate . However , semantic features have largely been overlooked in existing statistical-model-based question classification algorithms . In this paper , we propose a context-aware hybrid model based on a statistical-model PGM and a semantic language model word2vec . The experimental evaluations demonstrate the capability of the proposed model . In this research paper we have discussed how distributed representations are being used in streaming data clustering , and how to build a sequential evolution model for a streaming news website . Firstly , the stream news data collected from the web are cut to several slices chronologically ; in addition , word2vec models are built in different time periods for every slice of data ; lastly , compare the relationship of any target word in sequential word2vec models and analyze the evolutional procedure of the target word . An experimental result shows that our method is able to emerge the trend of the relationship between target words which ca n't be easily measured through traditional methods . Two alternative methods do not involve the NLP technology . They construct feature vectors using vector representation of words ( Word2Vec method ) or using a frequency of topics derived from the raw text . These four approaches are evaluated using 3 corpora with 5 , 34 and 25 subject categories respectively and with a different level of class discrimination . Bag - of - means on word embedding . We also have an experimental model that uses k - means on word2vec learnt from the training subset of each dataset , and then use these learnt means as representatives of the clustered words . We take into consideration all the words that appeared more than 5 times in the training subset . Among the large number of recent works on word - based ConvNets for text classification , one of the differences is the choice of using pretrained or end - to - end learned word representations . We offer comparisons with both using the pretrained word2vec embedding and using lookup tables . The embedding size is 300 in both cases , in the same way as our bag - of - means model . The problem of data sparsity is addressed by integrating features based on word clusters . More specifically , we use Brown clusters released by the authors of , as well as word clusters built from word2vec embeddings using k - means clustering . We find that the use of these features helps in reducing generalization error , by bringing the effective size of the vocabulary from typically 50 K words down to a few hundred word clusters . We use 2 - layer encoder and decoder LSTMs with 300 hidden units per layer and dropout with a rate of 0.3 between LSTM layers . We replace singleton words in the training set with an UNK token , normalize digits to a single symbol , and initialize word embeddings for both source and target words from the publicly available word2vec embeddings . We use simple 0 / 1 costs in defining the function . subsection : Value - shared Weighting We first train word embeddings with the Word2Vec tool by Mikolov et al . with the English Wikipedia dump to construct QA matching matrices . Given a question sentence and an answer sentence , we compute the dot product of the normalized word embeddings of all term pairs to construct the QA matching matrix as defined in Section [ reference ] . For sentence / document level sentiment classification , previous studies mostly have two steps . They first learn continuous word vector embeddings from data . Afterwards , semantic compositional approaches are used to compute the vector of a sentence / document from the vectors of its constituents based on the principle of compositionality . section : Introduction Pre - trained word representations word2vec , Pennington2014GloveGV are a key component in many neural language understanding models . Among the closest related work , ALE uses Fisher Vectors ( FV ) as input and binary attributes / hierarchies as output embeddings . Similarly , DeviSe uses CNN features as input and Word2Vec representations as output embeddings . In this work , we benefit from both ideas : ( 1 ) We use SoA image features , i.e. FV and CNN , ( 2 ) among others , we also use attributes and Word2Vec as output embeddings . paragraph : Pre - training embeddings We used the word2vec toolkit to pre - train the word embeddings . We created our training corpus by selecting lowercased English Wikipedia text and obtained 100 - dimensional Skip - gram word embeddings trained with the context window size 1 , the negative sampling method ( 15 negative samples ) , and the sub - sampling method ( of the sub - sampling coefficient ) . For the testing and implementation purpose , we developed a technique to find topics for not factorized documents from LDA and lda2vec . As our finding , lda2vec gave 85.66 % accuracy over topics for test documents where accuracy for LDA was 62.45 % Which makes lda2vec more reliable for the implementation purpose . We evaluate on a NLU task of natural language queries in EHRs divided in slot-filling and intent classification sub-tasks . On the slot-filling task , we obtain a F-score of 0.76 with the ELMo representation ; and on the classification task , a mean F-score of 0.71 . Our results show that this method could be used to develop a baseline system . We investigate the role of shallow syntax-aware representations for NLP tasks using two techniques . First , we enhance the ELMo architecture to allow pretraining on predicted shallow syntactic parses , instead of just raw text , so that contextual embeddings make use of shallow syntactic context . Our second method involves shallow syntactic features obtained automatically on downstream task data . Within framework of the research for this article a new state-of-the-art ELMO model of the words representation to improve the quality of classical models for solving the problem of binary classification in different interpretations is considered . The article contains a description of various methods applied to the processing of source texts for their transformation into the format necessary for many models , a description of their advantages and disadvantages , principles for constructing and operating the context-dependent representation of ELMo with a detailed description of the algorithm for using it within the target model . For a competent assessment of the results obtained , all experiments are carried out using a real dataset including news articles from various sources in China and India . Comparative analysis includes consideration of the results of adding an ELMo model to standard target models of solving a problem in comparison with using Word2Vec . A comparison is also made for different problem statements the classification of whole texts , individual sentences and the finding of specific passages . An F1 score of 94.10 % was the best achieved by constituent parser using only the dataset provided . With the help of external datasets such as German Wikipedia , pre-trained ELMo models are used along with self-attention networks achieving 95.87 % F1 score . Then the Att-BiLSTM-CRF model is built to jointly extract the entities and their relations with our extraction rules . Moreover , the contextualized ELMo representations pre-trained on biomedical text are used to further improve the performance . Experimental results on biomedical corpora show that our method can significantly improve the performance of overlapping relation extraction and achieves the state-of-the-art performance . subsection : Ablation To investigate the contributions of ELMo representations and biaffine scorer in our end - to - end model , we conduct a series of ablation studies on the CoNLL - 2005 and CoNLL - 2008 WSJ test sets , unless otherwise stated . Table [ reference ] compares F scores of he2018jointly he2018jointly and our model without ELMo representations . Next , it uses contextualized word representations , ELMo PetersNIGCLZ18 , which is a character - level two - layer bidirectional language model pre - trained on a large - scale corpus . ELMo representations allow our model to use morphological clues to form robust representations for out - of - vocabulary words unseen in training . Then , the concatenation of the word and contextualized embedding vectors is passed to a two - layer highway network SrivastavaGS15 that is shared for the question and passages . For this reason , we call them ELMo ( Embeddings from Language Models ) representations . Unlike previous approaches for learning contextualized word vectors Peters2017SemisupervisedST , McCann2017LearnedIT , ELMo representations are deep , in the sense that they are a function of all of the internal layers of the biLM . More specifically , we learn a linear combination of the vectors stacked above each input word for each end task , which markedly improves performance over just using the top LSTM layer . Improvements with ELMo are largest for smaller training sets and significantly reduce the amount of training data needed to reach a given level of performance . In the SRL case , the ELMo model with 1 % of the training set has about the same F as the baseline model with 10 % of the training set . subsection : Visualization of learned weights The forget gate bias is initialized to 1 for all LSTMs , with all other gates initialized to 0 , as per Jzefowicz2015AnEE . Table [ reference ] compares test set F1 scores of our ELMo augmented implementation of He2017DeepSR with previous results . Our single model score of 84.6 F1 represents a new state - of - the - art result on the CONLL 2012 Semantic Role Labeling task , surpassing the previous single model result by 2.9 F1 and a 5 - model ensemble by 1.2 F1 . Unlike our other models , we do not use an auxiliary language modeling loss during finetuning , as the ELMo language model is bidirectional . Finetuning the ELMo model substantially improves accuracy on our tasks , thus we include only finetuned ELMo results . paragraph : Multihead vs. Single Head Finetuning Decoders subsection : Parsing , POS and predicate detection We first report the labeled and unlabeled attachment scores ( LAS , UAS ) of our parsing models on the CoNLL - 2005 and 2012 test sets ( Table [ reference ] ) with GloVe ( ) and ELMo ( ) embeddings . D & M achieves the best scores . Word embeddings trained on a global corpus fail to capture the nuances of query-specific language present in the answer passages . We also found out that IWE word embeddings capture more semantic information than CBOW word embeddings when trained on local data with a limited vocabulary . Our experiments show that both the IWE model components contribute to the improved quality of word embeddings and consequently better QE terms . The results overall show all classical and deep learning models to outperform our baseline , which demonstrates the suitability of the corpus for both dialect and country-of-origin identification tasks . I am making the corpus and the trained CBOW word embeddings freely available for research purposes . The approach is based on the classification of vector representations of texts , generated using various neural language models : averaged word2vec , SIF-weighted averaged word2vec , TF-IDF-weighted averaged word2vec , paragraph2vec . Experimentally , the best quality was shown by the averaged word2vec ( CBOW ) model . Our study has two different data sets , English and Turkish . BOW and Word2Vec models were applied to each data set in the case where twitter feeds were not get roots and get roots were extracted . In this study , which is implemented in the Python programming language , the success percentages are compared by applying the scikit-learn classification algorithms , Linear SVM and Logistic Regression . To solve the above challenge , we propose a novel word embedding topic model by combining the topic model and the continuous bag-of-words mode ( Cbow ) method in word embedding method , named Cbow Topic Model ( CTM ) , for topic detection and summary in social networks . We conduct similar word clustering of the target social network text dataset by introducing the classic Cbow word vectorization method , which can effectively learn the internal relationship between words and reduce the dimensionality of the input texts . We employ the topic model-to-model short text for effectively weakening the sparsity problem of social network texts . The idea of word embeddings is explored and the main deep learning method for representing text , the neural word embedding is described with the famous Word2vec algorithm , in both the Skip-gram and CBOW variant . A CBOW Word2vec architecture is explored in detail and presented in Python code . This code presupposes a text as a list , but this code was written and explained in the previous chapters , and PCA is used in the code to reduce the dimensionality of the vectors to enable an easy display . The Open Street Map ( OSM ) data were utilized to generate parcels , which served as basic analytical units . Point of Interest ( POI ) data combined with a Continuous Bag-of-Words (CBoW)-based Word2Vec model was utilized to acquire UIL information . The entropy-weight Technique for Order Preference by Similarity to Ideal Solution method , combined with economic and environmental UIL indicators obtained from remote sensing images , ground observation data , and statistical data , was used to evaluate UIL use efficiency , and the spatial distribution and utilization degree of UIL within Beijing ’s fifth ring road was analyzed . Additionally , it is not possible to create embeddings for new ( out-of-vocabulary ) words on the spot . Based on an intuitive interpretation of the continuous bag-of-words ( CBOW ) word2vec model 's negative sampling training objective in terms of predicting context based similarities , we motivate an extension of the model we call context encoders ( ConEc ) . By multiplying the matrix of trained word2vec embeddings with a word 's average context vector , out-of-vocabulary ( OOV ) embeddings and representations for a word with multiple meanings can be created based on the word 's local contexts .
With the increase of biased information available online , the importance of analysis and detection of such content has also significantly risen . In this paper , we aim to quantify different kinds of social biases using word embeddings . Towards this goal we train such embeddings on two politically biased MediaWiki instances , namely RationalWiki and Conservapedia .
However , exploitation of corpus data for this purpose is complicated due to the diverse functions of reflexive markers crossing the domain of reflexivity and reciprocity . The list of verbs identified by the previous procedure is thus further used in an automatic experiment , applying word embeddings for detecting semantically similar verbs . These candidate verbs have been manually verified and annotation of their reflexive and reciprocal constructions has been integrated into the valency lexicon of Czech verbs VALLEX .
Given a cluster of scientific documents where one is a reference paper ( RP ) and the remaining documents are papers citing the reference , two tasks are proposed : ( i ) to identify which sentences in the reference paper are being cited and why they are cited , and ( ii ) to produce a citation-based summary of the reference paper using the information in the cluster . Our systems are based on both supervised ( LSTM and convolutional neural networks ) and unsupervised techniques using word embedding representations and features computed from the linguistic and semantic analysis of the documents .
Our system is based on a list of sentiment seed words adapted for tweets . The sentiment relations between seed words and other terms are captured by cosine similarity between the word embedding representations ( word2vec ) . These seed words are extracted from datasets of annotated tweets available online .
We will show that our approach has superior performance when the perception error rate ( PER ) is higher , and competitive performance when the PER is lower . We will also explore the possibility of using importance sampling techniques to handle observed action distributions with traditional word vector embeddings . We will show that although such approaches can give good recognition accuracy , they take significantly longer training time and their performance will degrade significantly at higher perception error rate .
Visualization can aid in such interpretation of embeddings . In this paper , we examine the role for visualization in working with word vector embeddings . We provide a literature survey to catalogue the range of tasks where the embeddings are employed across a broad range of applications .
Both methods obtain higher recall scores compared with the baseline , with a loss in precision . Besides higher recall , the word embeddings based approach achieves higher F-measure than the baseline and the tf.rf based methods . We also show that incorporating gene name and interaction keyword identification leads to improved precision and F-measure scores for all three evaluated methods .
Bengio:2003:NPL:944919.944966 are pioneers of neural language models . To address the curse of dimensionality in language modeling , they proposed a method using word embeddings and a feed - forward neural network ( FFNN ) . They demonstrated that their approach outperformed n - gram language models , but FFNN can only handle fixed - length contexts .
Next , in Table [ reference ] , we show that fine - tuning the word embeddings also improves results , again for both the in - domain task and cross - domain tasks ( the ablation results are based on a smaller model with a 128 + 256 2 - layer biLSTM ) . Hence , all our models were trained with word embeddings being fine - tuned . The last ablation in Table [ reference ] shows that a classifier with two layers of relu is preferable than other options .
subsection : Experimental Setup We use pre - trained - Glove vectors to initialize our word embedding vectors . All hidden states of BiLSTMs during input encoding and inference have dimensions ( and ) .
The explosion of user generated content on the Web , especially the fact that millions of users , on a daily basis , express their opinions on products and services to blogs , wikis , social networks , message boards , etc . , render the reliable , automated export of sentiments and opinions from unstructured text crucial for several commercial applications . In this paper , we present a novel hybrid vectorization approach for textual resources that combines a weighted variant of the popular Word2Vec representation ( based on Term Frequency-Inverse Document Frequency ) representation and with a Bag- of-Words representation and a vector of lexicon-based sentiment values . The proposed text representation approach is assessed through the application of several machine learning classification algorithms on a dataset that is used extensively in literature for sentiment detection .
The goal of this study is to demonstrate how network science and graph theory tools and concepts can be effectively used for exploring and comparing semantic spaces of word embeddings and lexical databases . Specifically , we construct semantic networks based on word2vec representation of words , which is “ learnt ” from large text corpora ( Google news , Amazon reviews ) , and “ human built ” word networks derived from the well-known lexical databases : WordNet and Moby Thesaurus . We compare “ global ” ( e.g. , degrees , distances , clustering coefficients ) and “ local ” ( e.g. , most central nodes and community-type dense clusters ) characteristics of considered networks .
To be able to visualize the learning process , we had to record all the weights from every nth iteration . For experiments we used three neural networks : a neural network for digit recognition ( DR ) , a neural network for word2vec representation of words ( WV ) , and a neural network which learned the simple logical gate XOR , ( XR ) . For the purpose of further comparisons , we also constructed one example of a damaged network ( DR ’ , WV ’ , XR ’ ) for each of the three original networks ( DR , WV , XR ) .
This approach significantly improves the relevance of the generated poems by representing each line of the poem not only in a context-sensitive manner but also in a holistic way that is highly related to the given keyword and the learned topic . The proposed augmented word2vec model further improves the rhythm and symmetry . We also present a straightforward evaluation metric RHYTHM score to automatically measure the rule-consistency of generated poems .
This approach significantly improves the relevance of the generated poems by representing each line of the poem not only in a context-sensitive manner but also in a holistic way that is highly related to the given keyword and the learned topic . A proposed augmented word2vec model further improves the rhythm and symmetry . Tests show that the generated poems by our approach are mostly satisfying with regulated rules and consistent themes , and 73.42 % of them receive an Overall score no less than 3 ( the highest score is 5 ) .
We present the first to our knowledge experimental quality evaluation of vector space models for text representations of educational course programme documents . More specifically we compare the quality of various popular algorithms : TF-IDF , LSA , LDA , averaged word2vec , paragraph2vec . The evaluation is carried out using various algorithms of clustering and classification on our experimental corpus of educational course programme , used by Russian universities .
The traditional duplicate long text detection algorithms are hard to be applied in the current situations , so more effective duplicate detection algorithm for short text is needed . Based on the bag-of-words model Word2vec , this paper proposes a kind of duplicate detection algorithm with semantic embedded for short text . Words are embedded into vectors which are as input elements in Simhash algorithm to acquire 64 bits sequence , then compare two sequences with Hamming distances and return result filtered by preset threshold value . Subsequently , a more superior improvement is proposed where we add weighted idea into . The results are compared with the unweighted Word2vec method and the traditional TF-IDF method . Experiments are carried out on the SICK corpus , and its result shows that the weighted Word2vec method achieves higher accuracy and recall rate .
However , semantic features have largely been overlooked in existing statistical-model-based question classification algorithms . In this paper , we propose a context-aware hybrid model based on a statistical-model PGM and a semantic language model word2vec . The experimental evaluations demonstrate the capability of the proposed model .
In this research paper we have discussed how distributed representations are being used in streaming data clustering , and how to build a sequential evolution model for a streaming news website . Firstly , the stream news data collected from the web are cut to several slices chronologically ; in addition , word2vec models are built in different time periods for every slice of data ; lastly , compare the relationship of any target word in sequential word2vec models and analyze the evolutional procedure of the target word . An experimental result shows that our method is able to emerge the trend of the relationship between target words which ca n't be easily measured through traditional methods .
Two alternative methods do not involve the NLP technology . They construct feature vectors using vector representation of words ( Word2Vec method ) or using a frequency of topics derived from the raw text . These four approaches are evaluated using 3 corpora with 5 , 34 and 25 subject categories respectively and with a different level of class discrimination .
Bag - of - means on word embedding . We also have an experimental model that uses k - means on word2vec learnt from the training subset of each dataset , and then use these learnt means as representatives of the clustered words . We take into consideration all the words that appeared more than 5 times in the training subset .
Among the large number of recent works on word - based ConvNets for text classification , one of the differences is the choice of using pretrained or end - to - end learned word representations . We offer comparisons with both using the pretrained word2vec embedding and using lookup tables . The embedding size is 300 in both cases , in the same way as our bag - of - means model .
The problem of data sparsity is addressed by integrating features based on word clusters . More specifically , we use Brown clusters released by the authors of , as well as word clusters built from word2vec embeddings using k - means clustering . We find that the use of these features helps in reducing generalization error , by bringing the effective size of the vocabulary from typically 50 K words down to a few hundred word clusters .
We use 2 - layer encoder and decoder LSTMs with 300 hidden units per layer and dropout with a rate of 0.3 between LSTM layers . We replace singleton words in the training set with an UNK token , normalize digits to a single symbol , and initialize word embeddings for both source and target words from the publicly available word2vec embeddings . We use simple 0 / 1 costs in defining the function .
subsection : Value - shared Weighting We first train word embeddings with the Word2Vec tool by Mikolov et al . with the English Wikipedia dump to construct QA matching matrices . Given a question sentence and an answer sentence , we compute the dot product of the normalized word embeddings of all term pairs to construct the QA matching matrix as defined in Section [ reference ] .
For sentence / document level sentiment classification , previous studies mostly have two steps . They first learn continuous word vector embeddings from data . Afterwards , semantic compositional approaches are used to compute the vector of a sentence / document from the vectors of its constituents based on the principle of compositionality .
section : Introduction Pre - trained word representations word2vec , Pennington2014GloveGV are a key component in many neural language understanding models .
Among the closest related work , ALE uses Fisher Vectors ( FV ) as input and binary attributes / hierarchies as output embeddings . Similarly , DeviSe uses CNN features as input and Word2Vec representations as output embeddings . In this work , we benefit from both ideas : ( 1 ) We use SoA image features , i.e. FV and CNN , ( 2 ) among others , we also use attributes and Word2Vec as output embeddings .
paragraph : Pre - training embeddings We used the word2vec toolkit to pre - train the word embeddings . We created our training corpus by selecting lowercased English Wikipedia text and obtained 100 - dimensional Skip - gram word embeddings trained with the context window size 1 , the negative sampling method ( 15 negative samples ) , and the sub - sampling method ( of the sub - sampling coefficient ) .
For the testing and implementation purpose , we developed a technique to find topics for not factorized documents from LDA and lda2vec . As our finding , lda2vec gave 85.66 % accuracy over topics for test documents where accuracy for LDA was 62.45 % Which makes lda2vec more reliable for the implementation purpose .
We evaluate on a NLU task of natural language queries in EHRs divided in slot-filling and intent classification sub-tasks . On the slot-filling task , we obtain a F-score of 0.76 with the ELMo representation ; and on the classification task , a mean F-score of 0.71 . Our results show that this method could be used to develop a baseline system .
We investigate the role of shallow syntax-aware representations for NLP tasks using two techniques . First , we enhance the ELMo architecture to allow pretraining on predicted shallow syntactic parses , instead of just raw text , so that contextual embeddings make use of shallow syntactic context . Our second method involves shallow syntactic features obtained automatically on downstream task data .
Within framework of the research for this article a new state-of-the-art ELMO model of the words representation to improve the quality of classical models for solving the problem of binary classification in different interpretations is considered . The article contains a description of various methods applied to the processing of source texts for their transformation into the format necessary for many models , a description of their advantages and disadvantages , principles for constructing and operating the context-dependent representation of ELMo with a detailed description of the algorithm for using it within the target model . For a competent assessment of the results obtained , all experiments are carried out using a real dataset including news articles from various sources in China and India . Comparative analysis includes consideration of the results of adding an ELMo model to standard target models of solving a problem in comparison with using Word2Vec . A comparison is also made for different problem statements the classification of whole texts , individual sentences and the finding of specific passages .
An F1 score of 94.10 % was the best achieved by constituent parser using only the dataset provided . With the help of external datasets such as German Wikipedia , pre-trained ELMo models are used along with self-attention networks achieving 95.87 % F1 score .
Then the Att-BiLSTM-CRF model is built to jointly extract the entities and their relations with our extraction rules . Moreover , the contextualized ELMo representations pre-trained on biomedical text are used to further improve the performance . Experimental results on biomedical corpora show that our method can significantly improve the performance of overlapping relation extraction and achieves the state-of-the-art performance .
subsection : Ablation To investigate the contributions of ELMo representations and biaffine scorer in our end - to - end model , we conduct a series of ablation studies on the CoNLL - 2005 and CoNLL - 2008 WSJ test sets , unless otherwise stated . Table [ reference ] compares F scores of he2018jointly he2018jointly and our model without ELMo representations .
Next , it uses contextualized word representations , ELMo PetersNIGCLZ18 , which is a character - level two - layer bidirectional language model pre - trained on a large - scale corpus . ELMo representations allow our model to use morphological clues to form robust representations for out - of - vocabulary words unseen in training . Then , the concatenation of the word and contextualized embedding vectors is passed to a two - layer highway network SrivastavaGS15 that is shared for the question and passages .
For this reason , we call them ELMo ( Embeddings from Language Models ) representations . Unlike previous approaches for learning contextualized word vectors Peters2017SemisupervisedST , McCann2017LearnedIT , ELMo representations are deep , in the sense that they are a function of all of the internal layers of the biLM . More specifically , we learn a linear combination of the vectors stacked above each input word for each end task , which markedly improves performance over just using the top LSTM layer .
Improvements with ELMo are largest for smaller training sets and significantly reduce the amount of training data needed to reach a given level of performance . In the SRL case , the ELMo model with 1 % of the training set has about the same F as the baseline model with 10 % of the training set . subsection : Visualization of learned weights
The forget gate bias is initialized to 1 for all LSTMs , with all other gates initialized to 0 , as per Jzefowicz2015AnEE . Table [ reference ] compares test set F1 scores of our ELMo augmented implementation of He2017DeepSR with previous results . Our single model score of 84.6 F1 represents a new state - of - the - art result on the CONLL 2012 Semantic Role Labeling task , surpassing the previous single model result by 2.9 F1 and a 5 - model ensemble by 1.2 F1 .
Unlike our other models , we do not use an auxiliary language modeling loss during finetuning , as the ELMo language model is bidirectional . Finetuning the ELMo model substantially improves accuracy on our tasks , thus we include only finetuned ELMo results . paragraph : Multihead vs. Single Head Finetuning Decoders
subsection : Parsing , POS and predicate detection We first report the labeled and unlabeled attachment scores ( LAS , UAS ) of our parsing models on the CoNLL - 2005 and 2012 test sets ( Table [ reference ] ) with GloVe ( ) and ELMo ( ) embeddings . D & M achieves the best scores .
Word embeddings trained on a global corpus fail to capture the nuances of query-specific language present in the answer passages . We also found out that IWE word embeddings capture more semantic information than CBOW word embeddings when trained on local data with a limited vocabulary . Our experiments show that both the IWE model components contribute to the improved quality of word embeddings and consequently better QE terms .
The results overall show all classical and deep learning models to outperform our baseline , which demonstrates the suitability of the corpus for both dialect and country-of-origin identification tasks . I am making the corpus and the trained CBOW word embeddings freely available for research purposes .
The approach is based on the classification of vector representations of texts , generated using various neural language models : averaged word2vec , SIF-weighted averaged word2vec , TF-IDF-weighted averaged word2vec , paragraph2vec . Experimentally , the best quality was shown by the averaged word2vec ( CBOW ) model .
Our study has two different data sets , English and Turkish . BOW and Word2Vec models were applied to each data set in the case where twitter feeds were not get roots and get roots were extracted . In this study , which is implemented in the Python programming language , the success percentages are compared by applying the scikit-learn classification algorithms , Linear SVM and Logistic Regression .
To solve the above challenge , we propose a novel word embedding topic model by combining the topic model and the continuous bag-of-words mode ( Cbow ) method in word embedding method , named Cbow Topic Model ( CTM ) , for topic detection and summary in social networks . We conduct similar word clustering of the target social network text dataset by introducing the classic Cbow word vectorization method , which can effectively learn the internal relationship between words and reduce the dimensionality of the input texts . We employ the topic model-to-model short text for effectively weakening the sparsity problem of social network texts .
The idea of word embeddings is explored and the main deep learning method for representing text , the neural word embedding is described with the famous Word2vec algorithm , in both the Skip-gram and CBOW variant . A CBOW Word2vec architecture is explored in detail and presented in Python code . This code presupposes a text as a list , but this code was written and explained in the previous chapters , and PCA is used in the code to reduce the dimensionality of the vectors to enable an easy display .
The Open Street Map ( OSM ) data were utilized to generate parcels , which served as basic analytical units . Point of Interest ( POI ) data combined with a Continuous Bag-of-Words (CBoW)-based Word2Vec model was utilized to acquire UIL information . The entropy-weight Technique for Order Preference by Similarity to Ideal Solution method , combined with economic and environmental UIL indicators obtained from remote sensing images , ground observation data , and statistical data , was used to evaluate UIL use efficiency , and the spatial distribution and utilization degree of UIL within Beijing ’s fifth ring road was analyzed .
Additionally , it is not possible to create embeddings for new ( out-of-vocabulary ) words on the spot . Based on an intuitive interpretation of the continuous bag-of-words ( CBOW ) word2vec model 's negative sampling training objective in terms of predicting context based similarities , we motivate an extension of the model we call context encoders ( ConEc ) . By multiplying the matrix of trained word2vec embeddings with a word 's average context vector , out-of-vocabulary ( OOV ) embeddings and representations for a word with multiple meanings can be created based on the word 's local contexts .
Identifying Biases in Politically Biased Wikis through Word Embeddings Towards a Semi-Automatic Detection of Reflexive and Reciprocal Constructions and Their Representation in a Valency Lexicon LaSTUS-TALN+INCO @ CL-SciSumm 2019 LSIS at SemEval-2017 Task 4: Using Adapted Sentiment Similarity Seed Words For English and Arabic Tweet Polarity Classification Recognizing Plans by Learning Embeddings from Observed Action Distributions Interactive Analysis of Word Vector Embeddings Automatic query generation using word embeddings for retrieving passages describing experimental methods Direct Output Connection for a High-Rank Language Model Shortcut-Stacked Sentence Encoders for Multi-Domain Inference DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference Machine Learning Sentiment Prediction based on Hybrid Document Representation Graph-based exploration and clustering analysis of semantic spaces Visualizations of the training process of neural networks Generating Thematic Chinese Poetry with Conditional Variational Autoencoder Generating Thematic Chinese Poetry using Conditional Variational Autoencoders with Hybrid Decoders Comparison of Vector Space Representations of Documents for the Task of Matching Contents of Educational Course Programmes Duplicate short text detection based on Word2vec PGM-WV: A context-aware hybrid model for heuristic and semantic question classification in question-answering system Streaming news sequential evolution model based on distributed representations Bag-of-Words, Bag-of-Topics and Word-to-Vec Based Subject Classification of Text Documents in Polish - A Comparative Study Character-level Convolutional Networks for Text Classification Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces Sequence-to-Sequence Learning as Beam-Search Optimization aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model Effective LSTMs for Target-Dependent Sentiment Classification Deep contextualized word representations Evaluation of output embeddings for fine-grained image classification A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks Topic Modelling: A Comparison of The Performance of Latent Dirichlet Allocation and LDA2vec Model on Bangla Newspaper Natural language understanding for task oriented dialog in the biomedical domain in a low ressources context, NIPS Workshop Shallow Syntax in Deep Water ELMo Word Representations For News Protection Deep learning techniques applied to constituency parsing of German A neural network-based joint learning approach for biomedical entity and relation extraction from biomedical literature Dependency or Span, End-to-End Uniform Semantic Role Labeling Multi-style Generative Reading Comprehension Deep contextualized word representations Practical Text Classification With Large Pre-Trained Language Models Linguistically-Informed Self-Attention for Semantic Role Labeling Word Embedding Models for Query Expansion in Answer Passage Retrieval Habibi - a multi Dialect multi National Arabic Song Lyrics Corpus Information extraction using neural language models for the case of online job listings analysis Sentiment Analysis of Turkish and English Twitter Feeds Using Word2Vec Model A word embedding topic model for topic detection and summary in social networks Neural Language Models The Identification and Use Efficiency Evaluation of Urban Industrial Land Based on Multi-Source Data Context encoders as a simple but powerful extension of word2vec
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["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Medicine"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["History","Computer Science"] ["Computer Science"] ["Computer Science","Medicine"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Engineering"] ["Computer Science"] ["Economics"] ["Computer Science","Mathematics"]
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HT LREC BIRNDL@SIGIR SemEval@ACL EMNLP RepEval@EMNLP NAACL-HLT MIPRO IJCAI AIST (Supplement) DepCoS-RELCOMEX NIPS ArXiv EMNLP CIKM COLING NAACL-HLT 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) EMNLP CLEF (Working Notes) AAAI ACL NAACL-HLT ArXiv EMNLP LREC SIU Rep4NLP@ACL
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