Cyberbullying commonly happened to each and every person on social platforms . In this paper we propose a framework to detect cyberbullying messages in the form of text data using deep neural networks and word embeddings . We stack together the state-of-the-art Bert and Glove embeddings to improve the performance of the classifier . Yet , they elude humans intuition . In this paper we are making the attempt to explain the arithmetic difference between two document embeddings by a series of word embeddings . We present an algorithm that iteratively picks words from a vocabulary that closes the topical gap between the documents . One of the assumptions of such representations is that words with similar properties have similar representation , allowing for better generalization from subsequent models . In the standard setting , two kinds of training corpora are used : a very large unlabeled corpus for learning the word embedding representations ; and an in-domain training corpus with gold labels for training classifiers on the target NLP task . Because of the amount of data required to learn embeddings , they are trained on large corpus of written text . This paper describes different approaches to detect malicious content in email interactions through a combination of machine learning and natural language processing tools . Specifically , several neural network designs are tested on word embedding representations to detect suspicious messages and separate them from non-suspicious , benign email . The proposed approaches are trained and tested on distinct email collections , including datasets constructed from publicly available corpora ( such as Enron , APWG , etc . ) as well as several smaller , non-public datasets used in recent government evaluations . Integrating and connecting all of these components , \sysname supports a range of analysis workflows that help understand similarities and differences between embedding spaces . We assess our approach by applying it in several use cases , including understanding corpora differences via word vector embeddings , and understanding algorithmic differences in generating embeddings . There has not been a rigorous evaluation regarding the relative importance of each component to different text-representation-based tasks ; i.e. , how important is the modeling capacity of word embeddings alone , relative to the added value of a compositional function ? In this paper , we conduct an extensive comparative study between Simple Word Embeddings-based Models ( SWEMs ) , with no compositional parameters , relative to employing word embeddings within RNN/CNN-based models . Surprisingly , SWEMs exhibit comparable or even superior performance in the majority of cases considered . Nowadays , writings take many forms of social media posts , micro-blogs , news articles , customer review , etc . , and the content of these short-texts can be a useful resource for text mining to discover an unhide various aspects , including emotions . The previously presented models mainly adopted word embedding vectors that represent rich semantic/syntactic information and those models can not capture the emotional relationship between words . Recently , some emotional word embeddings are proposed but it requires semantic and syntactic information vice versa . Many approaches were based on the emotional words or lexicon in order to detect emotions . While the word embedding vectors like Word2Vec were successfully employed in many NLP approaches . Also , word mover 's distance ( WMD ) is a method introduced recently to calculate the distance between two documents based on the embedded words . We considered the task of Cross-Topic Authorship Attribution and made experiments on The Guardian corpus . Experimental results show that our method outperforms word-based embeddings and character n-gram based linear models , which are among the most effective approaches for identifying the writing style of an author . Internal represention of words has gained a renewed interest with the apparition and extent of word embedders . It is crucial in many fields of research , information retrieval , natural language processing , document indexing , knowledge extraction . Given a set of instances of some relation , the relation induction task is to predict which other word pairs are likely to be related in the same way . While it is natural to use word embeddingsfor this task , standard approaches based on vector translations turn out to perform poorly . To address this issue , we propose two probabilistic relation induction models . In most neural natural language understanding ( NLU ) systems , the requisite background knowledge is implicitly encoded in the models ’ parameters . That is , what background knowledge is present has been learned from task supervision and also by pre - training word embeddings ( where distributional properties correlate with certain kinds of useful background knowledge , such as semantic relatedness ) . However , acquisition of background knowledge from static training corpora is limiting for two reasons . We note that when using an off - the - shelf dropout layer in this context , it is important to remove automatic rescaling functionality from the implementation if it is present . In many experiments , we also apply ordinary dropout between layers , including between word embeddings and the first QRNN layer . Densely - Connected Layers We further train a set of simple yet effective ranking models based on feed - forward neural networks . We study their effectiveness under various learning scenarios ( point - wise and pair - wise models ) and using different input representations ( i.e. , from encoding query - document pairs into dense / sparse vectors to using word embedding representation ) . We train our networks using tens of millions of training instances and evaluate it on two standard collections : a homogeneous news collection ( Robust ) and a heterogeneous large - scale web collection ( ClueWeb ) . Among these , distributed word representations or embeddings Mikolov:2013 , Pennington:2014 have attracted a lot of attention . Word embedding vectors have been applied to term re - weighting in IR models Zheng:2015 , Rekabsaz:2017 , query expansion Diaz:2016 , Zamani:2016a , Rekabsaz:2016 , query classification Liu:2015 , Zamani:2016b , etc . The main shortcoming of most of the approaches in this category is that the objective of the trained neural network differs from the objective of these tasks . For instance , the word embedding vectors proposed in Mikolov:2013 , Pennington:2014 are trained based on term proximity in a large corpus , which is different from the objective in most IR tasks . To overcome this issue , some approaches try to learn representations in an end - to - end neural model for learning a specific task like entity ranking for expert finding VanGysel:2016:www or product search VanGysel:2016:cikm . However , the encoding of the words is also crucial and a better encoding layer can lead to substantial difference to the final performance . Many powerful methods only represent their words in two ways , word - level embeddings and character - level embeddings . They use pre - train vectors , like GloVe , to do the word - level embeddings , which ignore syntactic information and name entity of the words . Table [ reference ] shows the performance of our model and its ablations on the SQuAD dev set . Both char - level and word - level embeddings contribute towards the model ’s performance . In summarization , one of the key challenges is to identify the key concepts and key entities in the document , around which the story revolves . In order to accomplish this goal , we may need to go beyond the word - embeddings - based representation of the input document and capture additional linguistic features such as parts - of - speech tags , named - entity tags , and TF and IDF statistics of the words . We therefore create additional look - up based embedding matrices for the vocabulary of each tag - type , similar to the embeddings for words . In this paper , we introduce a new type of deep contextualized word representation that directly addresses both challenges , can be easily integrated into existing models , and significantly improves the state of the art in every considered case across a range of challenging language understanding problems . Our representations differ from traditional word type embeddings in that each token is assigned a representation that is a function of the entire input sentence . We use vectors derived from a bidirectional LSTM that is trained with a coupled language model ( LM ) objective on a large text corpus . paragraph : Pre - training word vectors . The embedding layer for our 90 K vocabulary can be initialized randomly or using pre - trained word - vector embeddings . We pre - trained skip - gram embeddings of size 512 using word2vec on a 10B - word corpus . To this end , a multitude of different approaches for classifying sentences in biomedical publications according to their coarse semantic and rhetoric categories ( e.g. , Background , Methods , Results , Conclusions ) have been devised , with recent state-of-the-art results reported for a complex deep learning model . Recent evidence showed that shallow and wide neural models such as fastText can provide results that are competitive or superior to complex deep learning models while requiring drastically lower training times and having better scalability . We analyze the efficacy of the fastText model in the classification of biomedical sentences in the PubMed 200k RCT benchmark , and introduce a simple pre-processing step that enables the application of fastText on sentence sequences . Domain experts identified as anatomically significant nine prostate and ten lung organs-at-risk ( OAR ) structures and manually labeled them according to the TG-263 standards , and remaining structures were labeled as Non_OAR . We experimented with six different classification algorithms and three feature vector methods , and the final model was built with fastText algorithm . Multiple validation techniques are used to assess the robustness of the proposed methodology . We analyze features such as the most frequent 1000 lemmas , word forms , all lemmas , selected ( content insensitive ) lemmas , bigrams of grammatical classes and mixture of bigrams of grammatical classes , selected lemmas and punctuations . Moreover , the approach based on the recently proposed fastText algorithm ( for vector based representation of text ) is also applied . We evaluate these different approaches on two publicly available collections of Polish literary texts from late 19th- and early 20th-century : one consisting of 99 novels from 33 authors and the second one 888 novels from 58 authors . In this approach , we combine two tools , supervised machine learning and process mining . With the help of supervised machine learning , fastText classifier , we classify the body text of emails to the activity-related . Then the generated log will be mined with process mining techniques to find process models . While the original technique leads to a loss in accuracy , we adapt this method to circumvent the quantization artifacts . As a result , our approach produces a text classifier , derived from the fastText approach , which at test time requires only a fraction of the memory compared to the original one , without noticeably sacrificing the quality in terms of classification accuracy . Our experiments carried out on several benchmarks show that our approach typically requires two orders of magnitude less memory than fastText while being only slightly inferior with respect to accuracy . This paper presents a systematic study of the effect of these representations for Chinese sentiment analysis by proposing a multi-channel convolutional neural network ( MCCNN ) , where each channel corresponds to a representation . Experimental results show that : ( 1 ) Word wins on the dataset of low OOV rate while character wins otherwise ; ( 2 ) Using these representations in combination generally improves the performance ; ( 3 ) The representations based on MCCNN outperform conventional ngram features using SVM ; ( 4 ) The proposed MCCNN model achieves the competitive performance against the state-of-the-art model fastText for Chinese sentiment analysis . It is shown , that the writing genre ( for defined 28 classes ) could be automatically recognized based only on the book title with accuracy equal to 0.74 . The best results were achieved by fastText methods with word n-grams . While neural models have been proven to be effective for text classification , they operate like a black box and offer no interpretability for humans . In this paper , we study the state-of-the-art text classification model fastText and propose a mathematical approach to interpret the behavior of fastText into a score matrix . By decomposing the output of fastText into the sum of word-level importance scores , we can observe how each word contributes to the final prediction and understand the inner implementation of fastText . Hence , automated IR techniques are needed to find and process such information . In this paper , we utilize word vector embeddings along with fastText sentence classification algorithm to perform the task of classification of tweets posted during natural disasters . In the lyrics experiments we exploit the state-of-the-art word representations applied to the main Deep Learning architectures available in literature . In our benchmarks the results show how the Bilinear LSTM classifier with Attention based on fastText word embedding performs better than the CNN applied on audio . This paper explores a simple and efficient baseline for text classification . Our experiments show that our fast text classifier fastText is often on par with deep learning classifiers in terms of accuracy , and many orders of magnitude faster for training and evaluation . We can train fastText on more than one billion words in less than ten minutes using a standard multicore CPU , and classify half a million sentences among 312 K classes in less than a minute . With 4.59 billion sentence pairs , the full raw release of ParaCrawl is very large and noisy . For that reason , we first preprocess it to remove all duplicated sentence pairs , sentences for which the fastText language identification model predicts a different language , those with less than 3 or more than 80 tokens , or those with either an overlap of at least 50 % or a ratio above 2 between the source and target tokens . This reduces the corpus size to 64.4 M million sentence pairs , mostly due to deduplication . Using traditional approaches , good representations require high dimensionality , making downstream tasks such as visualization more difficult . We applied Poincaré embeddings in a 2-dimensional hyperbolic space to a large-scale administrative claims database and show performance comparable to 100-dimensional embeddings in a euclidean space . We then examine disease relationships under different disease contexts to better understand potential phenotypes .
Cyberbullying commonly happened to each and every person on social platforms . In this paper we propose a framework to detect cyberbullying messages in the form of text data using deep neural networks and word embeddings . We stack together the state-of-the-art Bert and Glove embeddings to improve the performance of the classifier .
Yet , they elude humans intuition . In this paper we are making the attempt to explain the arithmetic difference between two document embeddings by a series of word embeddings . We present an algorithm that iteratively picks words from a vocabulary that closes the topical gap between the documents .
One of the assumptions of such representations is that words with similar properties have similar representation , allowing for better generalization from subsequent models . In the standard setting , two kinds of training corpora are used : a very large unlabeled corpus for learning the word embedding representations ; and an in-domain training corpus with gold labels for training classifiers on the target NLP task . Because of the amount of data required to learn embeddings , they are trained on large corpus of written text .
This paper describes different approaches to detect malicious content in email interactions through a combination of machine learning and natural language processing tools . Specifically , several neural network designs are tested on word embedding representations to detect suspicious messages and separate them from non-suspicious , benign email . The proposed approaches are trained and tested on distinct email collections , including datasets constructed from publicly available corpora ( such as Enron , APWG , etc . ) as well as several smaller , non-public datasets used in recent government evaluations .
Integrating and connecting all of these components , \sysname supports a range of analysis workflows that help understand similarities and differences between embedding spaces . We assess our approach by applying it in several use cases , including understanding corpora differences via word vector embeddings , and understanding algorithmic differences in generating embeddings .
There has not been a rigorous evaluation regarding the relative importance of each component to different text-representation-based tasks ; i.e. , how important is the modeling capacity of word embeddings alone , relative to the added value of a compositional function ? In this paper , we conduct an extensive comparative study between Simple Word Embeddings-based Models ( SWEMs ) , with no compositional parameters , relative to employing word embeddings within RNN/CNN-based models . Surprisingly , SWEMs exhibit comparable or even superior performance in the majority of cases considered .
Nowadays , writings take many forms of social media posts , micro-blogs , news articles , customer review , etc . , and the content of these short-texts can be a useful resource for text mining to discover an unhide various aspects , including emotions . The previously presented models mainly adopted word embedding vectors that represent rich semantic/syntactic information and those models can not capture the emotional relationship between words . Recently , some emotional word embeddings are proposed but it requires semantic and syntactic information vice versa .
Many approaches were based on the emotional words or lexicon in order to detect emotions . While the word embedding vectors like Word2Vec were successfully employed in many NLP approaches . Also , word mover 's distance ( WMD ) is a method introduced recently to calculate the distance between two documents based on the embedded words .
We considered the task of Cross-Topic Authorship Attribution and made experiments on The Guardian corpus . Experimental results show that our method outperforms word-based embeddings and character n-gram based linear models , which are among the most effective approaches for identifying the writing style of an author .
Internal represention of words has gained a renewed interest with the apparition and extent of word embedders . It is crucial in many fields of research , information retrieval , natural language processing , document indexing , knowledge extraction .
Given a set of instances of some relation , the relation induction task is to predict which other word pairs are likely to be related in the same way . While it is natural to use word embeddingsfor this task , standard approaches based on vector translations turn out to perform poorly . To address this issue , we propose two probabilistic relation induction models .
In most neural natural language understanding ( NLU ) systems , the requisite background knowledge is implicitly encoded in the models ’ parameters . That is , what background knowledge is present has been learned from task supervision and also by pre - training word embeddings ( where distributional properties correlate with certain kinds of useful background knowledge , such as semantic relatedness ) . However , acquisition of background knowledge from static training corpora is limiting for two reasons .
We note that when using an off - the - shelf dropout layer in this context , it is important to remove automatic rescaling functionality from the implementation if it is present . In many experiments , we also apply ordinary dropout between layers , including between word embeddings and the first QRNN layer . Densely - Connected Layers
We further train a set of simple yet effective ranking models based on feed - forward neural networks . We study their effectiveness under various learning scenarios ( point - wise and pair - wise models ) and using different input representations ( i.e. , from encoding query - document pairs into dense / sparse vectors to using word embedding representation ) . We train our networks using tens of millions of training instances and evaluate it on two standard collections : a homogeneous news collection ( Robust ) and a heterogeneous large - scale web collection ( ClueWeb ) .
Among these , distributed word representations or embeddings Mikolov:2013 , Pennington:2014 have attracted a lot of attention . Word embedding vectors have been applied to term re - weighting in IR models Zheng:2015 , Rekabsaz:2017 , query expansion Diaz:2016 , Zamani:2016a , Rekabsaz:2016 , query classification Liu:2015 , Zamani:2016b , etc . The main shortcoming of most of the approaches in this category is that the objective of the trained neural network differs from the objective of these tasks . For instance , the word embedding vectors proposed in Mikolov:2013 , Pennington:2014 are trained based on term proximity in a large corpus , which is different from the objective in most IR tasks . To overcome this issue , some approaches try to learn representations in an end - to - end neural model for learning a specific task like entity ranking for expert finding VanGysel:2016:www or product search VanGysel:2016:cikm .
However , the encoding of the words is also crucial and a better encoding layer can lead to substantial difference to the final performance . Many powerful methods only represent their words in two ways , word - level embeddings and character - level embeddings . They use pre - train vectors , like GloVe , to do the word - level embeddings , which ignore syntactic information and name entity of the words .
Table [ reference ] shows the performance of our model and its ablations on the SQuAD dev set . Both char - level and word - level embeddings contribute towards the model ’s performance .
In summarization , one of the key challenges is to identify the key concepts and key entities in the document , around which the story revolves . In order to accomplish this goal , we may need to go beyond the word - embeddings - based representation of the input document and capture additional linguistic features such as parts - of - speech tags , named - entity tags , and TF and IDF statistics of the words . We therefore create additional look - up based embedding matrices for the vocabulary of each tag - type , similar to the embeddings for words .
In this paper , we introduce a new type of deep contextualized word representation that directly addresses both challenges , can be easily integrated into existing models , and significantly improves the state of the art in every considered case across a range of challenging language understanding problems . Our representations differ from traditional word type embeddings in that each token is assigned a representation that is a function of the entire input sentence . We use vectors derived from a bidirectional LSTM that is trained with a coupled language model ( LM ) objective on a large text corpus .
paragraph : Pre - training word vectors . The embedding layer for our 90 K vocabulary can be initialized randomly or using pre - trained word - vector embeddings . We pre - trained skip - gram embeddings of size 512 using word2vec on a 10B - word corpus .
To this end , a multitude of different approaches for classifying sentences in biomedical publications according to their coarse semantic and rhetoric categories ( e.g. , Background , Methods , Results , Conclusions ) have been devised , with recent state-of-the-art results reported for a complex deep learning model . Recent evidence showed that shallow and wide neural models such as fastText can provide results that are competitive or superior to complex deep learning models while requiring drastically lower training times and having better scalability . We analyze the efficacy of the fastText model in the classification of biomedical sentences in the PubMed 200k RCT benchmark , and introduce a simple pre-processing step that enables the application of fastText on sentence sequences .
Domain experts identified as anatomically significant nine prostate and ten lung organs-at-risk ( OAR ) structures and manually labeled them according to the TG-263 standards , and remaining structures were labeled as Non_OAR . We experimented with six different classification algorithms and three feature vector methods , and the final model was built with fastText algorithm . Multiple validation techniques are used to assess the robustness of the proposed methodology .
We analyze features such as the most frequent 1000 lemmas , word forms , all lemmas , selected ( content insensitive ) lemmas , bigrams of grammatical classes and mixture of bigrams of grammatical classes , selected lemmas and punctuations . Moreover , the approach based on the recently proposed fastText algorithm ( for vector based representation of text ) is also applied . We evaluate these different approaches on two publicly available collections of Polish literary texts from late 19th- and early 20th-century : one consisting of 99 novels from 33 authors and the second one 888 novels from 58 authors .
In this approach , we combine two tools , supervised machine learning and process mining . With the help of supervised machine learning , fastText classifier , we classify the body text of emails to the activity-related . Then the generated log will be mined with process mining techniques to find process models .
While the original technique leads to a loss in accuracy , we adapt this method to circumvent the quantization artifacts . As a result , our approach produces a text classifier , derived from the fastText approach , which at test time requires only a fraction of the memory compared to the original one , without noticeably sacrificing the quality in terms of classification accuracy . Our experiments carried out on several benchmarks show that our approach typically requires two orders of magnitude less memory than fastText while being only slightly inferior with respect to accuracy .
This paper presents a systematic study of the effect of these representations for Chinese sentiment analysis by proposing a multi-channel convolutional neural network ( MCCNN ) , where each channel corresponds to a representation . Experimental results show that : ( 1 ) Word wins on the dataset of low OOV rate while character wins otherwise ; ( 2 ) Using these representations in combination generally improves the performance ; ( 3 ) The representations based on MCCNN outperform conventional ngram features using SVM ; ( 4 ) The proposed MCCNN model achieves the competitive performance against the state-of-the-art model fastText for Chinese sentiment analysis .
It is shown , that the writing genre ( for defined 28 classes ) could be automatically recognized based only on the book title with accuracy equal to 0.74 . The best results were achieved by fastText methods with word n-grams .
While neural models have been proven to be effective for text classification , they operate like a black box and offer no interpretability for humans . In this paper , we study the state-of-the-art text classification model fastText and propose a mathematical approach to interpret the behavior of fastText into a score matrix . By decomposing the output of fastText into the sum of word-level importance scores , we can observe how each word contributes to the final prediction and understand the inner implementation of fastText .
Hence , automated IR techniques are needed to find and process such information . In this paper , we utilize word vector embeddings along with fastText sentence classification algorithm to perform the task of classification of tweets posted during natural disasters .
In the lyrics experiments we exploit the state-of-the-art word representations applied to the main Deep Learning architectures available in literature . In our benchmarks the results show how the Bilinear LSTM classifier with Attention based on fastText word embedding performs better than the CNN applied on audio .
This paper explores a simple and efficient baseline for text classification . Our experiments show that our fast text classifier fastText is often on par with deep learning classifiers in terms of accuracy , and many orders of magnitude faster for training and evaluation . We can train fastText on more than one billion words in less than ten minutes using a standard multicore CPU , and classify half a million sentences among 312 K classes in less than a minute .
With 4.59 billion sentence pairs , the full raw release of ParaCrawl is very large and noisy . For that reason , we first preprocess it to remove all duplicated sentence pairs , sentences for which the fastText language identification model predicts a different language , those with less than 3 or more than 80 tokens , or those with either an overlap of at least 50 % or a ratio above 2 between the source and target tokens . This reduces the corpus size to 64.4 M million sentence pairs , mostly due to deduplication .
Using traditional approaches , good representations require high dimensionality , making downstream tasks such as visualization more difficult . We applied Poincaré embeddings in a 2-dimensional hyperbolic space to a large-scale administrative claims database and show performance comparable to 100-dimensional embeddings in a euclidean space . We then examine disease relationships under different disease contexts to better understand potential phenotypes .
Deep Learning Cyberbullying Detection Using Stacked Embbedings Approach Explaining Topical Distances Using Word Embeddings Adapting lexical representation and OOV handling from written to spoken language with word embedding Email Threat Detection Using Distinct Neural Network Approaches embComp: Visual Interactive Comparison of Vector Embeddings On the Use of Word Embeddings Alone to Represent Natural Language Sequences Semantic-Emotion Neural Network for Emotion Recognition From Text Word mover's distance for affect detection Document embeddings learned on various types of n-grams for cross-topic authorship attribution Methods for word encoding: A survey Relation Induction in Word Embeddings Revisited Dynamic Integration of Background Knowledge in Neural NLU Systems Quasi-Recurrent Neural Networks Neural Ranking Models with Weak Supervision MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension Bidirectional Attention Flow for Machine Comprehension Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond Deep contextualized word representations Grammar as a Foreign Language Fast and scalable neural embedding models for biomedical sentence classification Integrated Natural Language Processing and Machine Learning Models for Standardizing Radiotherapy Structure Names Stylometry Analysis of Literary Texts in Polish Discovering Business Processes from Email Logs Using fastText and Process Mining FastText.zip: Compressing text classification models Exploiting Effective Representations for Chinese Sentiment Analysis Using a Multi-Channel Convolutional Neural Network Automatic Recognition of Gender and Genre in a Corpus of Microtexts A Deep Investigation into fastText BITS_PILANI@IMRiDis-FIRE 2017: Information Retrieval from Microblog during Disasters Exploiting Synchronized Lyrics And Vocal Features For Music Emotion Detection Bag of Tricks for Efficient Text Classification Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings Learning Contextual Hierarchical Structure of Medical Concepts with Poincairé Embeddings to Clarify Phenotypes
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