For online reviews , this analysis deals with the identification of positive and negative reviews to help the consumer and the distributor in the decision-making process . In text analysis tasks , such as text classification and sentiment analysis , the appropriate choice of term weighting schemes will have a huge impact on the effectiveness of the analysis . This paper explores the effect of using term weighting scheme in the sentiment classification of online movie reviews . This paper investigates a novel algorithm-EGA-SVM for text classification problem by combining support vector machines ( SVM ) with elitist genetic algorithm ( GA ) . The new algorithm uses EGA , which is based on elite survival strategy , to optimize the parameters of SVM . We reduce the distributions mismatch by setting up a test statistic between the two domains based on the Maximum Mean Discrepancy ( MMD ) algorithm and minimize the Type II error , given an upper bound on error I. Simultaneously , we minimize the structural risk functional . In order to highlight the advantages of the proposed method , we tackle a text classification problem on 20 Newsgroups dataset and Email Spam dataset . The results demonstrate that our method exhibits outstanding performance . This paper introduces a link-based approach to classification , which can be used in isolation or in conjunction with text-based classification . Various large-scale experimental results indicate that link-based classification is on par with text-based classification , and the combination of the two offers the best of both worlds . This paper presents a new method of text classifying by using the latent semantic analysis(LSA)and Kohonen network . The Kohonen network is applied to realize text classifying , which has a defect of training too slowly , and so supervising method is applied to training network . Therefore , the speed and precision of classifying are improved . The main distinctive features of communication in social media are defined , and also the analysis of causes that can disimprove the accuracy of automated detection of abusive content is performed . The stages of processing the natural language text data are analysed , in particular the detection of abusive content , concerning the possibility of its modification for considering the peculiarities of communication in social media and other aspects that can influence the final result of text data classification . It is defined , that peculiar features of messages and special aspects of communication in social media such as the existence of symbols and numbers that can lead to confusion in message apprehension , the existence of emoji , context ( at a level of social media users ’ connections ) and context ( at a level of social media messages ) can be concerned in the process of automated detection of abusive content in social media text messages . In the field of natural language processing ( NLP ) , the most prevalent neural approach to obtaining sentence representations is to use recurrent neural networks ( RNNs ) , where words in a sentence are processed in a sequential and recurrent manner . Along with their intuitive design , RNNs have shown outstanding performance across various NLP tasks e.g. language modeling , machine translation , text classification , and parsing . Among several variants of the original RNN , gated recurrent architectures such as long short - term memory ( LSTM ) and gated recurrent unit ( GRU ) have been accepted as de - facto standard choices for RNNs due to their capability of addressing the vanishing and exploding gradient problem and considering long - term dependencies . Experimental results on SNLI , MultiNLI and Quora benchmarks have demonstrated better performance of aESIM model than that of the baseline ESIM and the other state - of - the - art models . We believe that the architecture of Bi - aLSTM has potentially to be used in other NLP tasks such as text classification , machine translation and so on . This paper is organized as follows . In the transductive variant of our method , the class labels are determined by both the learned embeddings and input feature vectors , while in the inductive variant , the embeddings are defined as a parametric function of the feature vectors , so predictions can be made on instances not seen during training . On a large and diverse set of benchmark tasks , including text classification , distantly supervised entity extraction , and entity classification , we show improved performance over many of the existing models . RevisitingSemi - SupervisedLearningwithGraphEmbeddings section : Introduction Text categorization is the task of automatically assigning pre - defined categories to documents written in natural languages . Several types of text categorization have been studied , each of which deals with different types of documents and categories , such as topic categorization to detect discussed topics ( e.g. , sports , politics ) , spam detection , and sentiment classification to determine the sentiment typically in product or movie reviews . So I am going to used Optimized Rule Mining with Artificial Bee Colony ( ABC ) algorithm recently introduced optimization algorithms to simulate intelligent foraging behavior of honey bee swarm was proposed by Karboga and Ozturk . We will customize the ABC Algorithm to improve accuracy of Document Classification . Support vector machine ( SVM ) has been demonstrated its excellent performance in terms of solving document classification problem . In this paper , SVM with one-against-all structure is applied to solve Web page classification problems with multi-class . The task we intend to solve is to classify large and content-wise heterogeneous document streams on a layered nine-category system , which distinguishes medical from non-medical texts and sorts medical texts into various subgenres . While the document classification problem is often dealt with using computationally powerful and , hence , costly classifiers ( e.g. , Bayesian ones ) , we have gathered empirical evidence that a much simpler approach based on n-gram-statistics achieves a comparable level of classification performance . Feature selection is of paramount concern in document classification process which improves the efficiency and accuracy of text classifier . Vector Space Model is used to represent the “ Bag of Word ” BOW of the documents with term weighting phenomena . The most successful classifiers are MLP and SVM . In document classification process , it is observed that n-grams give higher accuracy rates than authorship attributes . Nevertheless , using n-gram and authorship attributes together , gives better results than when each is used alone . Therefore the optimal defense is to automatically classify the security label of the unstructured text and enforce the appropriate protection mechanism without degrading services or daily tasks . Many researchers have focused on document-based classification with artificially labeled “ confidential documents ” for which security labels are assigned to the entire document when in reality only a portion of the document is sensitive . The drawback of this type of whole-document based security labeling is that it blocks the accesses of legitimate users to non-sensitive portions of the informa- Based on the model , two TDT algorithms incorporating topic transition , i.e. topic discovering and document classifying , are provided to show the application of the proposed model . Experiments on two real-world document collections are done with the two algorithms , and performance comparison with other similar algorithm shows that the accuracy can achieve 93 % for topic discovering in Reuters-21578 , and 97.3 % in document classifying . Furthermore , topic transition discovered by the algorithm on a dataset which was collected from a BBS website is consistent with the manual analysis results . The second focus was in using parallelized classification algorithms to maintain document set classification schemes in a dynamic environment . The hypothesis was that parallel processing could be used to reduce the large storage requirements ( several hundred megabytes ) of ancillary data required for document set classification . This was verified by having the algorithm produce data internally as required . A subset of the TIPSTER corpus , specifically , articles from the Wall Street Journal , is used . Document set classification was performed without the large storage requirement ( potentially as high as 522 M ) for ancillary data matrices . In all cases , the time performance of the parallel system was an improvement over sequential system times , and produced the same clustering and classification scheme . Document classification system by the input document matches the category model and automatically input document class document classification as determined in advance . Enter the contents of the document change over time and the category model is also degraded . Even manually compiled thesauri such as WordNet can be overcrowded and noisy . We propose a flexible framework for efficient ontology extraction in document classification purposes . In this work we developed a set of ontology extraction rules . gillick2015multilingual further showed that it is possible to train a recurrent neural network on unicode bytes , instead of characters or words , to perform part - of - speech tagging and named entity recognition . These previous works suggest the possibility of applying neural networks for the task of machine translation , which is often considered a substantially more difficult problem compared to document classification and language modelling . subsection : Challenges and Questions The reason that LINE - SGDs are slightly slower is that a threshold - cutting technique has to be applied to prevent the gradients from exploding . Document Classification . Another way to evaluate the quality of the word embeddings is to use the word vectors to compute document representation , which can be evaluated with document classification tasks . section : Introduction Text classification is an important task in Natural Language Processing with many applications , such as web search , information retrieval , ranking and document classification . Recently , models based on neural networks have become increasingly popular . document : Cutting the Error by Half : Investigation of Very Deep CNN and Advanced Training Strategies for Document Image Classification We present an exhaustive investigation of recent Deep Learning architectures , algorithms , and strategies for the task of document image classification to finally reduce the error by more than half . section : Introduction An important step of the is the classification of the documents . An early classification of documents helps to process the subsequent processes in such as information extraction , text recognition etc . Due to its fundamental importance , this area has been explored extensively . section : Introduction Deep Convolutional Neural Networks ( DCNNs ) had been the method of choice for document recognition since LeCun1998 , but have only recently become the mainstream of high - level vision research . Over the past two years DCNNs have pushed the performance of computer vision systems to soaring heights on a broad array of high - level problems , including image classification KrizhevskyNIPS2013 , sermanet2013overfeat , simonyan2014very , szegedy2014going , papandreou2014untangling , object detection girshick2014rcnn , fine - grained categorization zhang2014part , among others . Although today there is a general consensus on how to generate fixed length representations of individual words which preserve their meaning , the same can not be said for sequences of words in sentences , paragraphs , or documents . In this work , we study the encoders commonly used to generate fixed length representations of natural language sequences , and analyze their effectiveness across a variety of high and low level tasks including sentence classification and question answering . Additionally , we propose novel improvements to the existing Skip-Thought and End-to-End Memory Network architectures and study their performance on both the original and auxiliary tasks . Is it Possible ? . This paper provides an analysis of the main methods for sentence classification in scientific papers and evaluates the feasibility of this technique in unstructured papers in Software Engineering area , in order to automatically find the study results in this area . Tests conducted with the existing methods using unstructured Test Software papers showed results far below those reported by the authors with input sets composed by structured health papers . Abstract Sentiment summarization is the process of automatically creating a compressed version of the opinionated information expressed in a text . This paper presents a machine learning-based approach to summarize user 's opinion expressed in reviews using : ( 1 ) Sentiment knowledge to calculate a sentence sentiment score as one of the features for sentence-level classification . It integrates multiple strategies to tackle the following problems : sentiment shifter , the types of sentences and word coverage limit . Despite their simplicity , they are limited in many tasks ; they ignore semantics of words and loss ordering of words . In this paper , we propose a simple and efficient neural language model for sentence-level classification task . Our model employs Recurrent Neural Network Language Model ( RNN-LM ) . Based on our observation that sentences are the basic units for route parts , we extract sentences from HTML documents using both the natural language knowledge and HTML tag information . Additionally , we study the sentence classification problem in route direction documents and its sequential nature . Several machine learning methods are compared and analyzed . In READ-IT readability assessment is carried out with respect to both documents and sentences where the latter represents an important novelty of the proposed approach creating the prerequisites for aligning the readability assessment step with the text simplification process . READ-IT shows a high accuracy in the document classification task and promising results in the sentence classification scenario . Different solutions to these issues have been tested by using and refining READ-IT , the first advanced readability assessment tool for Italian , which combines traditional raw text features with lexical , morpho-syntactic and syntactic information . In READ-IT readability assessment is carried out with respect to both documents and sentences , with the latter constituting an important novelty of the proposed approach : READ-IT shows a high accuracy in the document classification task and promising results in the sentence classification scenario . By comparing the results of two versions of READ-IT , adopting a classification- versus ranking-based approach , we also show that readability assessment is strongly influenced by textual genre ; for this reason a genre-oriented notion of readability is needed . Experimental results on five datasets for two typical NLP tasks show proposed models both obtain significant improvement compared with RecNN and TreeLSTM consistently . Our TG-HTreeLSTM outperforms all existing RecNN-based models and achieves or is competitive with state-of-the-art on four sentence classification benchmarks . The effectiveness of our models is also demonstrated by qualitative analysis . We found significant differences between easy and difficult text for average chain length and the average number of cross chains . For the sentence classification study , we compared the lexical chain features to standard bag-of-words features on a range of classifiers : logistic regression , naïve Bayes , decision trees , linear and RBF kernel SVM , and random forest . The lexical chain features performed significantly better than the bag-of-words baseline across all classifiers with the best classifier achieving an accuracy of ∼90 % ( compared to 78 % for bag-of-words ) . Our approach has been evaluated on four sentiment classification datasets and compared with a wide range of baselines . Experimental results show that : ( 1 ) sentence type classification can improve the performance of sentence-level sentiment analysis ; ( 2 ) the proposed approach achieves state-of-the-art results on several benchmarking datasets . We show in Figure 2 the prediction accuracies on the 6 - way question classification task using different filter configurations . Note that we also observe the similar phenomenon in the sentiment classification task . For each filter configuration , we report in Figure 2 the best result under extensive grid - search on hyperparameters . document : A Multi - sentiment - resource Enhanced Attention Network for Sentiment Classification Deep learning approaches for sentiment classification do not fully exploit sentiment linguistic knowledge . Despite its usefulness , to date , the sentiment linguistic knowledge has been underutilized in most recent deep neural network models ( e.g. , CNNs and LSTMs ) . In this work , we propose a Multi - sentiment - resource Enhanced Attention Network ( MEAN ) for sentence - level sentiment classification to integrate many kinds of sentiment linguistic knowledge into deep neural networks via multi - path attention mechanism . Specifically , we first design a coupled word embedding module to model the word representation from character - level and word - level semantics . The grConv architecture uses gating mechanism to control the information flow from children to parent . grConv and its variants are also applied to sentence classification tasks [ reference ][ reference ] . Neural tree indexer ( NTI ) [ reference ] utilizes soft hierarchical structures by using Tree - LSTM instead of grConv . section : Sentiment Analysis To evaluate the performance of our model in single - sentence classification , we conducted experiments on Stanford Sentiment Treebank ( SST ) [ reference ] dataset . Attention mechanism can then be applied to both structure and node function . We implemented and evaluated a binary - tree model of NTI , showing the model achieved the state - of - the - art performance on three different NLP tasks : natural language inference , answer sentence selection , and sentence classification , outperforming state - of - the - art recurrent and recursive neural networks . section : Introduction To this end , we extend the MT - DNN model originally proposed in liu2015mtl by incorporating BERT as its shared text encoding layers . As shown in Figure 1 , the lower layers ( i.e. , text encoding layers ) are shared across all tasks , while the top layers are task - specific , combining different types of NLU tasks such as single - sentence classification , pairwise text classification , text similarity , and relevance ranking . Similar to the BERT model , MT - DNN is trained in two stages : pre - training and fine - tuning . For concreteness , we describe them using the NLU tasks defined in the GLUE benchmark as examples . paragraph : Single - Sentence Classification : Given a sentence , the model labels it using one of the pre - defined class labels . Unlike the BERT model bert2018 that learns the representation via pre - training and adapts it to each individual task via fine - tuning , MT - DNN learns the representation using multi - task objectives . paragraph : Single - Sentence Classification Output : Suppose that is the contextual embedding ( ) of the token [ CLS ] , which can be viewed as the semantic representation of input sentence . We evaluate SPINN on the task of natural language inference . NLI is a sentence pair classification task , in which a model reads two sentences ( a premise and a hypothesis ) , and outputs a judgment of entailment , contradiction , or neutral , reflecting the relationship between the meanings of the two sentences . Below is an example sentence pair and judgment from the SNLI corpus which we use in our experiments : For detailed experimental settings , we refer readers to the supplemental material . For the NLI and PI tasks , there exists recent work specializing in sentence pair classification . However in this work we confine our model to the architecture that encodes each sentence using a shared encoder without any inter - sentence interaction , in order to focus on the effectiveness of the models in extracting semantics . paragraph : Previous CNN We focus on the sentence classification studies due to its relation to text categorization . Kim14 studied fine - tuning of pre - trained word vectors to produce input to parallel CNN . Fig [ reference ] ) were 10.13 and 9.37 , respectively , which is no better than SVM bow2 . Since excellent performances were reported on short sentence classification , we presume that their model is optimized for short sentences , but not for text categorization in general . paragraph : Performance dependency section : Our Method In this section , we discuss our novel methodology behind solving the sentiment classification problem . First we discuss the overview of our method and then we discuss the whole method in details , step by step . This study will also discuss the differences between the concept of fake news , stance and intent identification . We anticipate that the proposed roadmap , if implemented , will be useful in the classification of intent as it relates to hate speech in bilingual twitter post . The proposed model has the potential to improve intent classification and that could be useful in hate speech detection , which can avert social or security problems . All evaluations are performed on a set of publicly available citation analysis datasets . Evaluation results reveal that ImpactCite achieves a new state-of-the-art performance for both citation intent and sentiment classification by outperforming the existing approaches by 3.44 % and 1.33 % in F1-score . Therefore , we emphasize ImpactCite ( XLNet-based solution ) for both tasks to better understand the impact of a citation . Embedding into semantically meaningful vectors and computing their distances in vector space provides a simple , but effective way to measure semantic similarities . With the proposed framework , we demonstrate three key areas where the embedding model can be effective : visualization , distance based semantic search , similarity-based intent classification and re-ranking .
For online reviews , this analysis deals with the identification of positive and negative reviews to help the consumer and the distributor in the decision-making process . In text analysis tasks , such as text classification and sentiment analysis , the appropriate choice of term weighting schemes will have a huge impact on the effectiveness of the analysis . This paper explores the effect of using term weighting scheme in the sentiment classification of online movie reviews .
This paper investigates a novel algorithm-EGA-SVM for text classification problem by combining support vector machines ( SVM ) with elitist genetic algorithm ( GA ) . The new algorithm uses EGA , which is based on elite survival strategy , to optimize the parameters of SVM .
We reduce the distributions mismatch by setting up a test statistic between the two domains based on the Maximum Mean Discrepancy ( MMD ) algorithm and minimize the Type II error , given an upper bound on error I. Simultaneously , we minimize the structural risk functional . In order to highlight the advantages of the proposed method , we tackle a text classification problem on 20 Newsgroups dataset and Email Spam dataset . The results demonstrate that our method exhibits outstanding performance .
This paper introduces a link-based approach to classification , which can be used in isolation or in conjunction with text-based classification . Various large-scale experimental results indicate that link-based classification is on par with text-based classification , and the combination of the two offers the best of both worlds .
This paper presents a new method of text classifying by using the latent semantic analysis(LSA)and Kohonen network . The Kohonen network is applied to realize text classifying , which has a defect of training too slowly , and so supervising method is applied to training network . Therefore , the speed and precision of classifying are improved .
The main distinctive features of communication in social media are defined , and also the analysis of causes that can disimprove the accuracy of automated detection of abusive content is performed . The stages of processing the natural language text data are analysed , in particular the detection of abusive content , concerning the possibility of its modification for considering the peculiarities of communication in social media and other aspects that can influence the final result of text data classification . It is defined , that peculiar features of messages and special aspects of communication in social media such as the existence of symbols and numbers that can lead to confusion in message apprehension , the existence of emoji , context ( at a level of social media users ’ connections ) and context ( at a level of social media messages ) can be concerned in the process of automated detection of abusive content in social media text messages .
In the field of natural language processing ( NLP ) , the most prevalent neural approach to obtaining sentence representations is to use recurrent neural networks ( RNNs ) , where words in a sentence are processed in a sequential and recurrent manner . Along with their intuitive design , RNNs have shown outstanding performance across various NLP tasks e.g. language modeling , machine translation , text classification , and parsing . Among several variants of the original RNN , gated recurrent architectures such as long short - term memory ( LSTM ) and gated recurrent unit ( GRU ) have been accepted as de - facto standard choices for RNNs due to their capability of addressing the vanishing and exploding gradient problem and considering long - term dependencies .
Experimental results on SNLI , MultiNLI and Quora benchmarks have demonstrated better performance of aESIM model than that of the baseline ESIM and the other state - of - the - art models . We believe that the architecture of Bi - aLSTM has potentially to be used in other NLP tasks such as text classification , machine translation and so on . This paper is organized as follows .
In the transductive variant of our method , the class labels are determined by both the learned embeddings and input feature vectors , while in the inductive variant , the embeddings are defined as a parametric function of the feature vectors , so predictions can be made on instances not seen during training . On a large and diverse set of benchmark tasks , including text classification , distantly supervised entity extraction , and entity classification , we show improved performance over many of the existing models . RevisitingSemi - SupervisedLearningwithGraphEmbeddings
section : Introduction Text categorization is the task of automatically assigning pre - defined categories to documents written in natural languages . Several types of text categorization have been studied , each of which deals with different types of documents and categories , such as topic categorization to detect discussed topics ( e.g. , sports , politics ) , spam detection , and sentiment classification to determine the sentiment typically in product or movie reviews .
So I am going to used Optimized Rule Mining with Artificial Bee Colony ( ABC ) algorithm recently introduced optimization algorithms to simulate intelligent foraging behavior of honey bee swarm was proposed by Karboga and Ozturk . We will customize the ABC Algorithm to improve accuracy of Document Classification .
Support vector machine ( SVM ) has been demonstrated its excellent performance in terms of solving document classification problem . In this paper , SVM with one-against-all structure is applied to solve Web page classification problems with multi-class .
The task we intend to solve is to classify large and content-wise heterogeneous document streams on a layered nine-category system , which distinguishes medical from non-medical texts and sorts medical texts into various subgenres . While the document classification problem is often dealt with using computationally powerful and , hence , costly classifiers ( e.g. , Bayesian ones ) , we have gathered empirical evidence that a much simpler approach based on n-gram-statistics achieves a comparable level of classification performance .
Feature selection is of paramount concern in document classification process which improves the efficiency and accuracy of text classifier . Vector Space Model is used to represent the “ Bag of Word ” BOW of the documents with term weighting phenomena .
The most successful classifiers are MLP and SVM . In document classification process , it is observed that n-grams give higher accuracy rates than authorship attributes . Nevertheless , using n-gram and authorship attributes together , gives better results than when each is used alone .
Therefore the optimal defense is to automatically classify the security label of the unstructured text and enforce the appropriate protection mechanism without degrading services or daily tasks . Many researchers have focused on document-based classification with artificially labeled “ confidential documents ” for which security labels are assigned to the entire document when in reality only a portion of the document is sensitive . The drawback of this type of whole-document based security labeling is that it blocks the accesses of legitimate users to non-sensitive portions of the informa-
Based on the model , two TDT algorithms incorporating topic transition , i.e. topic discovering and document classifying , are provided to show the application of the proposed model . Experiments on two real-world document collections are done with the two algorithms , and performance comparison with other similar algorithm shows that the accuracy can achieve 93 % for topic discovering in Reuters-21578 , and 97.3 % in document classifying . Furthermore , topic transition discovered by the algorithm on a dataset which was collected from a BBS website is consistent with the manual analysis results .
The second focus was in using parallelized classification algorithms to maintain document set classification schemes in a dynamic environment . The hypothesis was that parallel processing could be used to reduce the large storage requirements ( several hundred megabytes ) of ancillary data required for document set classification . This was verified by having the algorithm produce data internally as required .
A subset of the TIPSTER corpus , specifically , articles from the Wall Street Journal , is used . Document set classification was performed without the large storage requirement ( potentially as high as 522 M ) for ancillary data matrices . In all cases , the time performance of the parallel system was an improvement over sequential system times , and produced the same clustering and classification scheme .
Document classification system by the input document matches the category model and automatically input document class document classification as determined in advance . Enter the contents of the document change over time and the category model is also degraded .
Even manually compiled thesauri such as WordNet can be overcrowded and noisy . We propose a flexible framework for efficient ontology extraction in document classification purposes . In this work we developed a set of ontology extraction rules .
gillick2015multilingual further showed that it is possible to train a recurrent neural network on unicode bytes , instead of characters or words , to perform part - of - speech tagging and named entity recognition . These previous works suggest the possibility of applying neural networks for the task of machine translation , which is often considered a substantially more difficult problem compared to document classification and language modelling . subsection : Challenges and Questions
The reason that LINE - SGDs are slightly slower is that a threshold - cutting technique has to be applied to prevent the gradients from exploding . Document Classification . Another way to evaluate the quality of the word embeddings is to use the word vectors to compute document representation , which can be evaluated with document classification tasks .
section : Introduction Text classification is an important task in Natural Language Processing with many applications , such as web search , information retrieval , ranking and document classification . Recently , models based on neural networks have become increasingly popular .
document : Cutting the Error by Half : Investigation of Very Deep CNN and Advanced Training Strategies for Document Image Classification We present an exhaustive investigation of recent Deep Learning architectures , algorithms , and strategies for the task of document image classification to finally reduce the error by more than half .
section : Introduction An important step of the is the classification of the documents . An early classification of documents helps to process the subsequent processes in such as information extraction , text recognition etc . Due to its fundamental importance , this area has been explored extensively .
section : Introduction Deep Convolutional Neural Networks ( DCNNs ) had been the method of choice for document recognition since LeCun1998 , but have only recently become the mainstream of high - level vision research . Over the past two years DCNNs have pushed the performance of computer vision systems to soaring heights on a broad array of high - level problems , including image classification KrizhevskyNIPS2013 , sermanet2013overfeat , simonyan2014very , szegedy2014going , papandreou2014untangling , object detection girshick2014rcnn , fine - grained categorization zhang2014part , among others .
Although today there is a general consensus on how to generate fixed length representations of individual words which preserve their meaning , the same can not be said for sequences of words in sentences , paragraphs , or documents . In this work , we study the encoders commonly used to generate fixed length representations of natural language sequences , and analyze their effectiveness across a variety of high and low level tasks including sentence classification and question answering . Additionally , we propose novel improvements to the existing Skip-Thought and End-to-End Memory Network architectures and study their performance on both the original and auxiliary tasks .
Is it Possible ? . This paper provides an analysis of the main methods for sentence classification in scientific papers and evaluates the feasibility of this technique in unstructured papers in Software Engineering area , in order to automatically find the study results in this area . Tests conducted with the existing methods using unstructured Test Software papers showed results far below those reported by the authors with input sets composed by structured health papers .
Abstract Sentiment summarization is the process of automatically creating a compressed version of the opinionated information expressed in a text . This paper presents a machine learning-based approach to summarize user 's opinion expressed in reviews using : ( 1 ) Sentiment knowledge to calculate a sentence sentiment score as one of the features for sentence-level classification . It integrates multiple strategies to tackle the following problems : sentiment shifter , the types of sentences and word coverage limit .
Despite their simplicity , they are limited in many tasks ; they ignore semantics of words and loss ordering of words . In this paper , we propose a simple and efficient neural language model for sentence-level classification task . Our model employs Recurrent Neural Network Language Model ( RNN-LM ) .
Based on our observation that sentences are the basic units for route parts , we extract sentences from HTML documents using both the natural language knowledge and HTML tag information . Additionally , we study the sentence classification problem in route direction documents and its sequential nature . Several machine learning methods are compared and analyzed .
In READ-IT readability assessment is carried out with respect to both documents and sentences where the latter represents an important novelty of the proposed approach creating the prerequisites for aligning the readability assessment step with the text simplification process . READ-IT shows a high accuracy in the document classification task and promising results in the sentence classification scenario .
Different solutions to these issues have been tested by using and refining READ-IT , the first advanced readability assessment tool for Italian , which combines traditional raw text features with lexical , morpho-syntactic and syntactic information . In READ-IT readability assessment is carried out with respect to both documents and sentences , with the latter constituting an important novelty of the proposed approach : READ-IT shows a high accuracy in the document classification task and promising results in the sentence classification scenario . By comparing the results of two versions of READ-IT , adopting a classification- versus ranking-based approach , we also show that readability assessment is strongly influenced by textual genre ; for this reason a genre-oriented notion of readability is needed .
Experimental results on five datasets for two typical NLP tasks show proposed models both obtain significant improvement compared with RecNN and TreeLSTM consistently . Our TG-HTreeLSTM outperforms all existing RecNN-based models and achieves or is competitive with state-of-the-art on four sentence classification benchmarks . The effectiveness of our models is also demonstrated by qualitative analysis .
We found significant differences between easy and difficult text for average chain length and the average number of cross chains . For the sentence classification study , we compared the lexical chain features to standard bag-of-words features on a range of classifiers : logistic regression , naïve Bayes , decision trees , linear and RBF kernel SVM , and random forest . The lexical chain features performed significantly better than the bag-of-words baseline across all classifiers with the best classifier achieving an accuracy of ∼90 % ( compared to 78 % for bag-of-words ) .
Our approach has been evaluated on four sentiment classification datasets and compared with a wide range of baselines . Experimental results show that : ( 1 ) sentence type classification can improve the performance of sentence-level sentiment analysis ; ( 2 ) the proposed approach achieves state-of-the-art results on several benchmarking datasets .
We show in Figure 2 the prediction accuracies on the 6 - way question classification task using different filter configurations . Note that we also observe the similar phenomenon in the sentiment classification task . For each filter configuration , we report in Figure 2 the best result under extensive grid - search on hyperparameters .
document : A Multi - sentiment - resource Enhanced Attention Network for Sentiment Classification Deep learning approaches for sentiment classification do not fully exploit sentiment linguistic knowledge .
Despite its usefulness , to date , the sentiment linguistic knowledge has been underutilized in most recent deep neural network models ( e.g. , CNNs and LSTMs ) . In this work , we propose a Multi - sentiment - resource Enhanced Attention Network ( MEAN ) for sentence - level sentiment classification to integrate many kinds of sentiment linguistic knowledge into deep neural networks via multi - path attention mechanism . Specifically , we first design a coupled word embedding module to model the word representation from character - level and word - level semantics .
The grConv architecture uses gating mechanism to control the information flow from children to parent . grConv and its variants are also applied to sentence classification tasks [ reference ][ reference ] . Neural tree indexer ( NTI ) [ reference ] utilizes soft hierarchical structures by using Tree - LSTM instead of grConv .
section : Sentiment Analysis To evaluate the performance of our model in single - sentence classification , we conducted experiments on Stanford Sentiment Treebank ( SST ) [ reference ] dataset .
Attention mechanism can then be applied to both structure and node function . We implemented and evaluated a binary - tree model of NTI , showing the model achieved the state - of - the - art performance on three different NLP tasks : natural language inference , answer sentence selection , and sentence classification , outperforming state - of - the - art recurrent and recursive neural networks . section : Introduction
To this end , we extend the MT - DNN model originally proposed in liu2015mtl by incorporating BERT as its shared text encoding layers . As shown in Figure 1 , the lower layers ( i.e. , text encoding layers ) are shared across all tasks , while the top layers are task - specific , combining different types of NLU tasks such as single - sentence classification , pairwise text classification , text similarity , and relevance ranking . Similar to the BERT model , MT - DNN is trained in two stages : pre - training and fine - tuning .
For concreteness , we describe them using the NLU tasks defined in the GLUE benchmark as examples . paragraph : Single - Sentence Classification : Given a sentence , the model labels it using one of the pre - defined class labels .
Unlike the BERT model bert2018 that learns the representation via pre - training and adapts it to each individual task via fine - tuning , MT - DNN learns the representation using multi - task objectives . paragraph : Single - Sentence Classification Output : Suppose that is the contextual embedding ( ) of the token [ CLS ] , which can be viewed as the semantic representation of input sentence .
We evaluate SPINN on the task of natural language inference . NLI is a sentence pair classification task , in which a model reads two sentences ( a premise and a hypothesis ) , and outputs a judgment of entailment , contradiction , or neutral , reflecting the relationship between the meanings of the two sentences . Below is an example sentence pair and judgment from the SNLI corpus which we use in our experiments :
For detailed experimental settings , we refer readers to the supplemental material . For the NLI and PI tasks , there exists recent work specializing in sentence pair classification . However in this work we confine our model to the architecture that encodes each sentence using a shared encoder without any inter - sentence interaction , in order to focus on the effectiveness of the models in extracting semantics .
paragraph : Previous CNN We focus on the sentence classification studies due to its relation to text categorization . Kim14 studied fine - tuning of pre - trained word vectors to produce input to parallel CNN .
Fig [ reference ] ) were 10.13 and 9.37 , respectively , which is no better than SVM bow2 . Since excellent performances were reported on short sentence classification , we presume that their model is optimized for short sentences , but not for text categorization in general . paragraph : Performance dependency
section : Our Method In this section , we discuss our novel methodology behind solving the sentiment classification problem . First we discuss the overview of our method and then we discuss the whole method in details , step by step .
This study will also discuss the differences between the concept of fake news , stance and intent identification . We anticipate that the proposed roadmap , if implemented , will be useful in the classification of intent as it relates to hate speech in bilingual twitter post . The proposed model has the potential to improve intent classification and that could be useful in hate speech detection , which can avert social or security problems .
All evaluations are performed on a set of publicly available citation analysis datasets . Evaluation results reveal that ImpactCite achieves a new state-of-the-art performance for both citation intent and sentiment classification by outperforming the existing approaches by 3.44 % and 1.33 % in F1-score . Therefore , we emphasize ImpactCite ( XLNet-based solution ) for both tasks to better understand the impact of a citation .
Embedding into semantically meaningful vectors and computing their distances in vector space provides a simple , but effective way to measure semantic similarities . With the proposed framework , we demonstrate three key areas where the embedding model can be effective : visualization , distance based semantic search , similarity-based intent classification and re-ranking .
Term weighting scheme effect in sentiment analysis of online movie reviews A Hybrid Algorithm for Text Classification Problem Optimal kernel choice for domain adaption learning Web Content Categorization Using Link Information A New Method of Text Categorization Based on LSA and Kohonen Network Метод автоматизованого визначення наявності образливого вмісту текстових повідомлень у соціальних мережах Cell-aware Stacked LSTMs for Modeling Sentences Attention Boosted Sequential Inference Model Revisiting Semi-Supervised Learning with Graph Embeddings Effective Use of Word Order for Text Categorization with Convolutional Neural Networks USER INTERESTED OPTIMIZED RULE MINING WITH ARTIFICIAL BEE COLONY IN DOCUMENT CLASSIFICATION Multi-class SVM with negative data selection for Web page classification Pumping Documents Through a Domain and Genre Classification Pipeline Efficient Feature Selection and Domain Relevance Term Weighting Method for Document Classification Author Attribution of Turkish Texts by Feature Mining Towards Automating Big Texts Security Classification Incorporating topic transition in topic detection and tracking algorithms Parallel clustering and classification of monolithic and non-monolithic document bases Clustering and classification of large document bases in a parallel environment Classification evaluation system, method, and program Automatic Ontology Extraction for Document Classification A Character-level Decoder without Explicit Segmentation for Neural Machine Translation LINE: Large-scale Information Network Embedding Bag of Tricks for Efficient Text Classification Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image Classification Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs Comparison and Fine-grained Analysis of Sequence Encoders for Natural Language Processing Automatic Results Identification in Software Engineering Papers. Is it Possible? Machine learning-based multi-documents sentiment-oriented summarization using linguistic treatment Deep learning for sentence classification Extracting Route Directions from Web Pages READ–IT: Assessing Readability of Italian Texts with a View to Text Simplification Assessing document and sentence readability in less resourced languages and across textual genres Learning to Compose over Tree Structures via POS Tags Using Lexical Chains to Identify Text Difficulty: A Corpus Statistics and Classification Study Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN A C-LSTM Neural Network for Text Classification A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification Learning to Compose Task-Specific Tree Structures Neural Tree Indexers for Text Understanding Multi-Task Deep Neural Networks for Natural Language Understanding A Fast Unified Model for Parsing and Sentence Understanding Cell-aware Stacked LSTMs for Modeling Sentences Effective Use of Word Order for Text Categorization with Convolutional Neural Networks Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modeling Bi-lingual Intent Classification of Twitter Posts: A Roadmap ImpactCite: An XLNet-based method for Citation Impact Analysis Semantic vector learning for natural language understanding
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ACML ArXiv ICML HLT-NAACL LREC ICIC (1) ACL WWW '15 EACL 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) ICLR ICCSA Workshops WebDB ArXiv ACL AAAI EACL ACL ACL ACML HLT-NAACL Knowl. Based Syst. SEDA
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