The experimental results shows that it is feasible to use the punctuation to divide the corpus into semantically complete contexts , which can significantly improve the semantic and syntactic of word embedding . Glove1 and Glove2 improve the quality of word embedding , which contains more semantic and syntactic information and has better linear relationships . Word embedding that can map the words to low-dimensional vector representations has been widely used in natural language processing tasks . But the word embedding based on context such as Word2Vec and GloVe is lack of capturing the sentiment information . Most of existing sentiment analysis methods incorporate sentiment polarity ( positive and negative ) to improve the sentiment embedding for sentiment tasks . We demonstrate many of the learned factors have surprisingly strong semantic or syntactic meaning corresponding to the factors previously identified manually by human inspection . Thus dictionary learning provides a powerful visualization tool for understanding word embedding representations . Furthermore , we show that the word factors can help in identifying key semantic and syntactic differences in word analogy tasks and improve upon the state-of-the-art word embedding techniques in these tasks by a large margin . This paper focuses on error prediction in Automatic Speech Recognition ASR outputs and proposes to investigate the use of continuous word representation word embeddings within a neural network architecture . The main contribution of this paper is about word embeddings combination : several combination approaches are proposed in order to take advantage of their complementarity . The use of prosodic features , in addition to classical syntactic ones , is evaluated . We compare the results against GloVe showing that they are similar . As far as we know this is the first application of feature hashing to the word embeddings problem and the results indicate this is a scalable technique with practical results for NLP applications . The given paper presents an overview of modern RESTful API description languages ( belongs to interface description languages set ) - OpenAPI , RAML , WADL , Slate - designed to provide a structured description of a RESTful web APIs ( that is useful both to a human and for automated machine processing ) , with related RESTful web API modeling frameworks . We propose an example of the schema model of web API of the service for pre-trained distributional semantic models ( word embeddings ) processing . This service is a part of the Personal Research Information System services ecosystem - the Research and Development Workstation Environment class system for supporting research in the field of ontology engineering : the automated building of applied ontology in an arbitrary domain area as a main feature ; scientific and technical creativity : the automated preparation of application documents for patenting inventions in Ukraine . Word embeddings/vectors are becoming an extremely important component of natural language processing tasks . Word2vec and fastText are few of the most common word embedding techniques . Our best run achieved an overall micro F-score of 0.919 on the employed corpus . Our results also suggested that the Global Vectors for word representation embedding in general domain provides a very strong baseline , which can be further improved by applying the principal component analysis to generate more isotropic vectors . CONCLUSIONS We have demonstrated that the proposed cascading architecture can handle the problem of overlapped annotations and further improve the overall recall and F-scores because the architecture enables the developed models to exploit more context information and forms an ensemble for creating a stronger recognizer . Words with similar vectors are semantically similar . Words Embeddings are one of the few successful application of unsupervised learning with the major benefit that it do not require expensive annotations but they can be derived from large un-annotated corpora . In this paper , we present a comparative analysis of different word embedding models namely Continuous bag of words , Skip gram , Glove(Global Vectors for word representation ) and Hellinger-PCA ( Principal Component Analysis ) . We maintain stop words during the model training stage . Word Embeddings . We obtain pre - trained word embeddings with the Word2Vec tool by Mikolov et al . For each word in the input sentence of length , we use two types of embeddings . Word embeddings : We use Skip - gram mikolov2013word2vec to train word embeddings . From Figure [ reference ] , we can find that SSWE and SSWE perform worse than SSWE , which is consistent with the results reported on target - independent sentiment classification of tweets . This shows the importance of context information for word embedding learning as both SSWE and SSWE do not encode any word contexts . Glove and SSWE perform comparably , which indicates the importance of global context for estimating a good word representation . Two questions need to be solved in order to build such an encoder , namely : what is the preferable neural network architecture ; and how and on what task should such a network be trained . Following existing work on learning word embeddings , most current approaches consider learning sentence encoders in an unsupervised manner like SkipThought or FastSent . Here , we investigate whether supervised learning can be leveraged instead , taking inspiration from previous results in computer vision , where many models are pretrained on the ImageNet before being transferred . document : Compressing Word Embeddings via Deep Compositional Code Learning Natural language processing ( NLP ) models often require a massive number of parameters for word embeddings , resulting in a large storage or memory footprint . section : Advantage of Compositional Codes In this section , we formally describe the compositional coding approach and analyze its merits for compressing word embeddings . The coding approach follows the scheme in additive quantization Furthermore , many recent works showed that neural networks can be successfully used in a number of tasks in natural language processing ( NLP ) . These include , but are not limited to , language modeling , paraphrase detection and word embedding extraction . In the field of statistical machine translation ( SMT ) , deep neural networks have begun to show promising results . While one could develop custom architectures for each task so as to read supplementary inputs , we would like ours to augment any existing NLU task architectures with the ability to read relevant information with minimal effort . To realize this goal , we adopt the strategy of refining word embeddings ; that is , we replace static word embeddings with embeddings that are functions of the task inputs and any supplementary inputs . Word embeddings can be considered a simple form of key - value memory stores that , in our case , not only contain general - purpose knowledge ( as in typical neural NLU systems ) but also contextual information ( including background knowledge ) . To fully explore label-label correlation and network topology structures , we propose to model a multi-label network as two Siamese GCNs : a node-node-label graph and a label-label-node graph . The two GCNs each handle one aspect of representation learning for nodes and labels , respectively , and they are seamlessly integrated under one objective function . The learned label representations can effectively preserve the inner-label interaction and node label properties , and are then aggregated to enhance the node representation learning under a unified training framework . Much of the appeal comes from the fact that Atari games are varied , showcase aspects of competency we expect from an intelligent agent , and are not biased towards any particular solution approach . The challenge of the ALE includes 1 ) the representation learning problem of extracting pertinent information from the raw pixels , and 2 ) the behavioural learning problem of leveraging complex , delayed associations between actions and rewards . Often , in reinforcement learning research , we care more about the latter , but the representation learning problem adds significant computational expense . We provide a theoretical analysis of the representation learning problem aimed at learning the latent variables ( design matrix ) $ \Theta$ of observations $ Y$ with the knowledge of the coefficient matrix $ X$. The design matrix is learned under the assumption that the latent variables $ \Theta$ are smooth with respect to a ( known ) topological structure $ \mathcal{G}$. To learn such latent variables , we study a graph Laplacian regularized estimator , which is the penalized least squares estimator with penalty term proportional to a Laplacian quadratic form . This type of estimators has recently received considerable attention due to its capability in incorporating underlying topological graph structure of variables into the learning process . Our experiments yield some surprising ( and some expected ) findings : Better Representation Learning Helps ! Our first observation is that large - scale data helps in representation learning as evidenced by improvement in performance on each and every vision task we study . This suggests that collection of a larger - scale dataset to study visual pretraining may greatly benefit the field . Paraphrase Detection is the task of examining if two sentences convey the same meaning or not . Here , in this paper , we have chosen a sentence embedding by unsupervised RAE vectors for capturing syntactic as well as semantic information . The RAEs learn features from the nodes of the parse tree and chunk information along with unsupervised word embedding . We propose a mechanism to identify potential sentences containing focus locations using NER . After that , we perform sentence embedding over words from different languages and then employ a supervised classification mechanism to predict the primary focus location . We also perform bias correction over the training data using a suitable adaptation mechanism to reduce the sampling bias in training data . Text matching is a fundamental problem in natural language processing . Neural models using bidirectional LSTMs for sentence encoding and inter-sentence attention mechanisms perform remarkably well on several benchmark datasets . We propose REGMAPR - a simple and general architecture for text matching that does not use inter-sentence attention . A set of phrase structure rules and a set of transformational rules are proposed for which the claim is made that these rules enumerate the underlying and derived sentential structures which exemplify two productive classes of sentential embedding in English . These are sentential embedding in noun phrases and sentential embedding in verb phrases . N2O requires only a collection of examples and is simple to understand : two embedders are more similar if , for the same set of inputs , there is greater overlap between the inputs ' nearest neighbors . Though applicable to embedders of texts of any size , we focus on sentence embedders and use N2O to show the effects of different design choices and architectures . In addition , we propose a method to tune these embeddings towards better compositionality . We find that training the existing embeddings to compose lexicographic definitions improves their performance in this task significantly , while also getting similar or better performance in both word similarity and sentence embedding evaluations . Our method tunes word embeddings using a simple neural network architecture with definitions and lemmas from WordNet . In this paper , we transfer this idea to sentence embeddings and explore several approaches to obtain a sparse representation . We further introduce a novel , quantitative and automated evaluation metric for sentence embedding interpretability , based on topic coherence methods . We observe an increase in interpretability compared to dense models , on a dataset of movie dialogs and on the scene descriptions from the MS COCO dataset . Recent work has addressed the problem by embedding visual and textual inputs into a common space where semantic similarities correlate to distances . We also adopt the embedding approach , and make the following contributions : First , we utilize web image search in sentence embedding process to disambiguate fine-grained visual concepts . Second , we propose embedding models for sentence , image , and video inputs whose parameters are learned simultaneously . Also , our system produced leading results when evaluated with a separate STS benchmark dataset . The word alignment and sentence embeddings based features were found to be very effective . In this paper , we propose a neural network model for graph-based dependency parsing which utilizes Bidirectional LSTM ( BLSTM ) to capture richer contextual information instead of using high-order factorization , and enable our model to use much fewer features than previous work . In addition , we propose an effective way to learn sentence segment embedding on sentence-level based on an extra forward LSTM network . Although our model uses only first-order factorization , experiments on English Peen Treebank and Chinese Penn Treebank show that our model could be competitive with previous higher-order graph-based dependency parsing models and state-of-the-art models . Convolutional neural network ( CNN ) and recurrent neural network ( RNN ) are two mainstream architectures for such modeling tasks , which adopt totally different ways of understanding natural languages . In this work , we combine the strengths of both architectures and propose a novel and unified model called C - LSTM for sentence representation and text classification . C - LSTM utilizes CNN to extract a sequence of higher - level phrase representations , and are fed into a long short - term memory recurrent neural network ( LSTM ) to obtain the sentence representation . The availability of SNLI dataset with 570k human annotated sentence pairs has enabled a good deal of progress on natural language understanding . The essential representation learning techniques for NLU such as attention , memory and the use of parse structure are studied on the SNLI which serves as an important benchmark for sentence understanding . The models trained on NLI task can be divided into two categories : ( i ) sentence encoding - based model which aims to find vector representation for each sentence and classifies the relation by using the concatenation of two vector representation along with their absolute element - wise difference and element - wise product . adopts two layer GRU encoder with pre - trained ” skip - thoughts ” vectors . To capture sentence - level semantics , use tree - based CNN and propose a stack - augmented parser - interpreter neural network ( SPINN ) which incorporates parsing information in a sequential manner . uses intra - attention on top of BiLSTM to generate sentence representation , and proposes an memory augmented neural network to encode the sentence . The next group of model , experiments ( 8 - 18 ) , uses cross sentence feature . Among them , methods of learning representations of words , e.g. word2vec [ reference ] or GloVe , are relatively well - studied empirically and theoretically [ reference ][ reference ] , and some of them became typical choices to consider when initializing word representations for better performance at downstream tasks . Meanwhile , research on sentence representation is still in active progress , and accordingly various architecturesdesigned with different intuition and tailored for different tasks - are being proposed . In the midst of them , three architectures are most frequently used in obtaining sentence representation from words . We empirically showed that the optimal structure might differ per task , and investigating task - specific latent tree structures could be an interesting future research direction . For future work , we plan to apply the core idea beyond sentence encoding . The performance could be further improved by applying intra - sentence or inter - sentence attention mechanisms . Consequently , we refer to our model as sentence - state LSTM , or S - LSTM in short . Empirically , S - LSTM can give effective sentence encoding after 3 - 6 recurrent steps . In contrast , the number of recurrent steps necessary for BiLSTM scales with the size of the sentence . We bring the idea of utilizing states coming from multiple directions to construction of stacked LSTM and apply the idea to the research of sentence representation learning . There is some prior work addressing the idea of incorporating more than one type of state , however to the best of our knowledge there is little work on applying the idea to sentence encoding and text classification . We conduct extensive evaluation of the proposed method and empirically prove its effectiveness on encoding sentences . Then we add a fully - connected hidden layer to further mix the information in a sentence . The obtained vector representation of a sentence is denoted as ( also called a sentence embedding ) . Notice that the same tree - based convolution applies to both the premise and hypothesis . Arc - II outperforms Arc - I significantly , showing the power of joint modeling of matching and sentence meaning . As another convolutional model , SENNA + MLP performs fairly well on this task , although still running behind the proposed convolutional architectures since it is too shallow to adequately model the sentence . In contrast , transferring SNLI representations to SICK yields the best performance yet reported for an unaugmented neural network model , surpasses the available EOP models , and approaches both the overall state of the art at 84.6 % and the 84 % level of interannotator agreement , which likely represents an approximate performance ceiling . This suggests that the introduction of a large high - quality corpus makes it possible to train representation - learning models for sentence meaning that are competitive with the best hand - engineered models on inference tasks . We attempted to apply this same transfer evaluation technique to the RTE - 3 challenge , but found that the small training set ( 800 examples ) did not allow the model to adapt to the unfamiliar genre of text used in that corpus , such that no training configuration yielded competitive performance . First , we explain how the NLI task can be used to train universal sentence encoding models using the SNLI task . We subsequently describe the architectures that we investigated for the sentence encoder , which , in our opinion , covers a suitable range of sentence encoders currently in use . Specifically , we examine standard recurrent models such as LSTMs and GRUs , for which we investigate mean and max - pooling over the hidden representations ; a self - attentive network that incorporates different views of the sentence ; and a hierarchical convolutional network that can be seen as a tree - based method that blends different levels of abstraction . To evaluate the quality of these representations , we use them as features in 12 transfer tasks . We present our sentence - embedding evaluation procedure in this section . We constructed a sentence evaluation tool called SentEval to automate evaluation on all the tasks mentioned in this paper . The resulting adaptive sampler finds harder negative examples , which forces the main model to learn a better representation of the data . We evaluate our proposal on learning word embeddings , order embeddings and knowledge graph embeddings and observe both faster convergence and improved results on multiple metrics . Learning word embeddings has received a significant amount of attention recently . Often , word embeddings are learned in an unsupervised manner from a large collection of text . An important class of problems involves training deep neural networks with sparse prediction targets of very high dimension D. These occur naturally in e.g. neural language models or the learning of word-embeddings , often posed as predicting the probability of next words among a vocabulary of size D ( e.g. 200,000 ) . Computing the equally large , but typically non-sparse D-dimensional output vector from a last hidden layer of reasonable dimension d ( e.g. 500 ) incurs a prohibitive O(Dd ) computational cost for each example , as does updating the $ D \times d$ output weight matrix and computing the gradient needed for backpropagation to previous layers . Neural word embedding approaches , due to their ability to capture semantic meanings of vocabulary terms , have recently gained attention of the information retrieval ( IR ) community and have shown promising results in improving ad hoc retrieval performance . It has been observed that these approaches are sensitive to various choices made during the learning of word embeddings and their usage , often leading to poor reproducibility . We study the effect of varying following two parameters , viz . , i ) the term normalization and ii ) the choice of training collection , on ad hoc retrieval performance with word2vec and fastText embeddings . Co-occurrences between two words provide useful insights into the semantics of those words . Consequently , numerous prior work on word embedding learning have used co-occurrences between two words as the training signal for learning word embeddings . However , in natural language texts it is common for multiple words to be related and co-occurring in the same context . In recent years , there have been researches on using semantic knowledge and global statistical features to guide the learning of word embeddings . Though the syntax knowledge also plays an very important role in natural language understanding , its effectiveness on the word embedding learning is still far from well investigated . Inspired by the principle of the dissociation between nouns and verbs ( DNV ) in language acquisition observed in neuropsychology , we propose a novel model for word embeddings learning using DNV ( named Continuous Dissociation between Nouns and Verbs Model , CDNV ) . The authors analyze three critical components in training word embeddings : model , corpus , and training parameters . They systematize existing neural-network-based word embedding methods and experimentally compare them using the same corpus . However , it is not necessary for similar or related words to occur in the same context window . In this paper , we propose a new approach to combine different types of resources for training word embeddings . The lexical resources used in this work are Dependency Parse Tree and WordNet . Learning embeddings of words and knowledge base elements is a promising approach for open domain question answering . Based on the remark that relations and entities are distinct object types lying in the same embedding space , we analyze the benefit of adding a regularizer favoring the embeddings of entities to be orthogonal to those of relations . Learning high-quality word embeddings is of significant importance in achieving better performance in many down-stream learning tasks . On one hand , traditional word embeddings are trained on a large scale corpus for general-purpose tasks , which are often sub-optimal for many domain-specific tasks . The idea of Word Embedding is based on the semantic distribution hypothesis of the linguist Harris ( 1954 ) , who believes that words of the same semantics are distributed in similar contexts . Learning of vector-space word embeddings is a technique of central importance in natural language processing . In recent years , cross-lingual word vectors have received more and more attention . We also identify several strengths of the Open IE paradigm , which enable it to be a useful intermediate structure for end tasks . We survey its use in both human-facing applications and downstream NLP tasks , including event schema induction , sentence similarity , text comprehension , learning word vector embeddings , and more . The error - exploring dynamic - oracle training always improves over static oracle training controlling for the transition system , but the arc - hybrid system slightly under - performs the arc - standard system when trained with static oracle . Flattening the sampling distribution ( ) is especially beneficial when training with pretrained word embeddings . In order to be able to compare with similar greedy parsers we report the performance of the parser on the multilingual treebanks of the CoNLL 2009 shared task . paragraph : Effect of training set size Our motivation for introducing language parsing and pretrained word embeddings is to better generalize the concepts learned from the limited training examples . Words representing semantically close concepts ideally get assigned close word embeddings . We refer to our baseline network as variational dropout LSTM , or VD - LSTM in short . section : EMPIRICAL VALIDATION FOR THE THEORY OF REUSING WORD EMBEDDINGS In Section 4 , we showed that the particular loss augmentation scheme we choose constrains the output projection matrix to be close to the input embedding matrix , without explicitly doing so by reusing the input embedding matrix .
The experimental results shows that it is feasible to use the punctuation to divide the corpus into semantically complete contexts , which can significantly improve the semantic and syntactic of word embedding . Glove1 and Glove2 improve the quality of word embedding , which contains more semantic and syntactic information and has better linear relationships .
Word embedding that can map the words to low-dimensional vector representations has been widely used in natural language processing tasks . But the word embedding based on context such as Word2Vec and GloVe is lack of capturing the sentiment information . Most of existing sentiment analysis methods incorporate sentiment polarity ( positive and negative ) to improve the sentiment embedding for sentiment tasks .
We demonstrate many of the learned factors have surprisingly strong semantic or syntactic meaning corresponding to the factors previously identified manually by human inspection . Thus dictionary learning provides a powerful visualization tool for understanding word embedding representations . Furthermore , we show that the word factors can help in identifying key semantic and syntactic differences in word analogy tasks and improve upon the state-of-the-art word embedding techniques in these tasks by a large margin .
This paper focuses on error prediction in Automatic Speech Recognition ASR outputs and proposes to investigate the use of continuous word representation word embeddings within a neural network architecture . The main contribution of this paper is about word embeddings combination : several combination approaches are proposed in order to take advantage of their complementarity . The use of prosodic features , in addition to classical syntactic ones , is evaluated .
We compare the results against GloVe showing that they are similar . As far as we know this is the first application of feature hashing to the word embeddings problem and the results indicate this is a scalable technique with practical results for NLP applications .
The given paper presents an overview of modern RESTful API description languages ( belongs to interface description languages set ) - OpenAPI , RAML , WADL , Slate - designed to provide a structured description of a RESTful web APIs ( that is useful both to a human and for automated machine processing ) , with related RESTful web API modeling frameworks . We propose an example of the schema model of web API of the service for pre-trained distributional semantic models ( word embeddings ) processing . This service is a part of the Personal Research Information System services ecosystem - the Research and Development Workstation Environment class system for supporting research in the field of ontology engineering : the automated building of applied ontology in an arbitrary domain area as a main feature ; scientific and technical creativity : the automated preparation of application documents for patenting inventions in Ukraine .
Word embeddings/vectors are becoming an extremely important component of natural language processing tasks . Word2vec and fastText are few of the most common word embedding techniques .
Our best run achieved an overall micro F-score of 0.919 on the employed corpus . Our results also suggested that the Global Vectors for word representation embedding in general domain provides a very strong baseline , which can be further improved by applying the principal component analysis to generate more isotropic vectors . CONCLUSIONS We have demonstrated that the proposed cascading architecture can handle the problem of overlapped annotations and further improve the overall recall and F-scores because the architecture enables the developed models to exploit more context information and forms an ensemble for creating a stronger recognizer .
Words with similar vectors are semantically similar . Words Embeddings are one of the few successful application of unsupervised learning with the major benefit that it do not require expensive annotations but they can be derived from large un-annotated corpora . In this paper , we present a comparative analysis of different word embedding models namely Continuous bag of words , Skip gram , Glove(Global Vectors for word representation ) and Hellinger-PCA ( Principal Component Analysis ) .
We maintain stop words during the model training stage . Word Embeddings . We obtain pre - trained word embeddings with the Word2Vec tool by Mikolov et al .
For each word in the input sentence of length , we use two types of embeddings . Word embeddings : We use Skip - gram mikolov2013word2vec to train word embeddings .
From Figure [ reference ] , we can find that SSWE and SSWE perform worse than SSWE , which is consistent with the results reported on target - independent sentiment classification of tweets . This shows the importance of context information for word embedding learning as both SSWE and SSWE do not encode any word contexts . Glove and SSWE perform comparably , which indicates the importance of global context for estimating a good word representation .
Two questions need to be solved in order to build such an encoder , namely : what is the preferable neural network architecture ; and how and on what task should such a network be trained . Following existing work on learning word embeddings , most current approaches consider learning sentence encoders in an unsupervised manner like SkipThought or FastSent . Here , we investigate whether supervised learning can be leveraged instead , taking inspiration from previous results in computer vision , where many models are pretrained on the ImageNet before being transferred .
document : Compressing Word Embeddings via Deep Compositional Code Learning Natural language processing ( NLP ) models often require a massive number of parameters for word embeddings , resulting in a large storage or memory footprint .
section : Advantage of Compositional Codes In this section , we formally describe the compositional coding approach and analyze its merits for compressing word embeddings . The coding approach follows the scheme in additive quantization
Furthermore , many recent works showed that neural networks can be successfully used in a number of tasks in natural language processing ( NLP ) . These include , but are not limited to , language modeling , paraphrase detection and word embedding extraction . In the field of statistical machine translation ( SMT ) , deep neural networks have begun to show promising results .
While one could develop custom architectures for each task so as to read supplementary inputs , we would like ours to augment any existing NLU task architectures with the ability to read relevant information with minimal effort . To realize this goal , we adopt the strategy of refining word embeddings ; that is , we replace static word embeddings with embeddings that are functions of the task inputs and any supplementary inputs . Word embeddings can be considered a simple form of key - value memory stores that , in our case , not only contain general - purpose knowledge ( as in typical neural NLU systems ) but also contextual information ( including background knowledge ) .
To fully explore label-label correlation and network topology structures , we propose to model a multi-label network as two Siamese GCNs : a node-node-label graph and a label-label-node graph . The two GCNs each handle one aspect of representation learning for nodes and labels , respectively , and they are seamlessly integrated under one objective function . The learned label representations can effectively preserve the inner-label interaction and node label properties , and are then aggregated to enhance the node representation learning under a unified training framework .
Much of the appeal comes from the fact that Atari games are varied , showcase aspects of competency we expect from an intelligent agent , and are not biased towards any particular solution approach . The challenge of the ALE includes 1 ) the representation learning problem of extracting pertinent information from the raw pixels , and 2 ) the behavioural learning problem of leveraging complex , delayed associations between actions and rewards . Often , in reinforcement learning research , we care more about the latter , but the representation learning problem adds significant computational expense .
We provide a theoretical analysis of the representation learning problem aimed at learning the latent variables ( design matrix ) $ \Theta$ of observations $ Y$ with the knowledge of the coefficient matrix $ X$. The design matrix is learned under the assumption that the latent variables $ \Theta$ are smooth with respect to a ( known ) topological structure $ \mathcal{G}$. To learn such latent variables , we study a graph Laplacian regularized estimator , which is the penalized least squares estimator with penalty term proportional to a Laplacian quadratic form . This type of estimators has recently received considerable attention due to its capability in incorporating underlying topological graph structure of variables into the learning process .
Our experiments yield some surprising ( and some expected ) findings : Better Representation Learning Helps ! Our first observation is that large - scale data helps in representation learning as evidenced by improvement in performance on each and every vision task we study . This suggests that collection of a larger - scale dataset to study visual pretraining may greatly benefit the field .
Paraphrase Detection is the task of examining if two sentences convey the same meaning or not . Here , in this paper , we have chosen a sentence embedding by unsupervised RAE vectors for capturing syntactic as well as semantic information . The RAEs learn features from the nodes of the parse tree and chunk information along with unsupervised word embedding .
We propose a mechanism to identify potential sentences containing focus locations using NER . After that , we perform sentence embedding over words from different languages and then employ a supervised classification mechanism to predict the primary focus location . We also perform bias correction over the training data using a suitable adaptation mechanism to reduce the sampling bias in training data .
Text matching is a fundamental problem in natural language processing . Neural models using bidirectional LSTMs for sentence encoding and inter-sentence attention mechanisms perform remarkably well on several benchmark datasets . We propose REGMAPR - a simple and general architecture for text matching that does not use inter-sentence attention .
A set of phrase structure rules and a set of transformational rules are proposed for which the claim is made that these rules enumerate the underlying and derived sentential structures which exemplify two productive classes of sentential embedding in English . These are sentential embedding in noun phrases and sentential embedding in verb phrases .
N2O requires only a collection of examples and is simple to understand : two embedders are more similar if , for the same set of inputs , there is greater overlap between the inputs ' nearest neighbors . Though applicable to embedders of texts of any size , we focus on sentence embedders and use N2O to show the effects of different design choices and architectures .
In addition , we propose a method to tune these embeddings towards better compositionality . We find that training the existing embeddings to compose lexicographic definitions improves their performance in this task significantly , while also getting similar or better performance in both word similarity and sentence embedding evaluations . Our method tunes word embeddings using a simple neural network architecture with definitions and lemmas from WordNet .
In this paper , we transfer this idea to sentence embeddings and explore several approaches to obtain a sparse representation . We further introduce a novel , quantitative and automated evaluation metric for sentence embedding interpretability , based on topic coherence methods . We observe an increase in interpretability compared to dense models , on a dataset of movie dialogs and on the scene descriptions from the MS COCO dataset .
Recent work has addressed the problem by embedding visual and textual inputs into a common space where semantic similarities correlate to distances . We also adopt the embedding approach , and make the following contributions : First , we utilize web image search in sentence embedding process to disambiguate fine-grained visual concepts . Second , we propose embedding models for sentence , image , and video inputs whose parameters are learned simultaneously .
Also , our system produced leading results when evaluated with a separate STS benchmark dataset . The word alignment and sentence embeddings based features were found to be very effective .
In this paper , we propose a neural network model for graph-based dependency parsing which utilizes Bidirectional LSTM ( BLSTM ) to capture richer contextual information instead of using high-order factorization , and enable our model to use much fewer features than previous work . In addition , we propose an effective way to learn sentence segment embedding on sentence-level based on an extra forward LSTM network . Although our model uses only first-order factorization , experiments on English Peen Treebank and Chinese Penn Treebank show that our model could be competitive with previous higher-order graph-based dependency parsing models and state-of-the-art models .
Convolutional neural network ( CNN ) and recurrent neural network ( RNN ) are two mainstream architectures for such modeling tasks , which adopt totally different ways of understanding natural languages . In this work , we combine the strengths of both architectures and propose a novel and unified model called C - LSTM for sentence representation and text classification . C - LSTM utilizes CNN to extract a sequence of higher - level phrase representations , and are fed into a long short - term memory recurrent neural network ( LSTM ) to obtain the sentence representation .
The availability of SNLI dataset with 570k human annotated sentence pairs has enabled a good deal of progress on natural language understanding . The essential representation learning techniques for NLU such as attention , memory and the use of parse structure are studied on the SNLI which serves as an important benchmark for sentence understanding . The models trained on NLI task can be divided into two categories : ( i ) sentence encoding - based model which aims to find vector representation for each sentence and classifies the relation by using the concatenation of two vector representation along with their absolute element - wise difference and element - wise product .
adopts two layer GRU encoder with pre - trained ” skip - thoughts ” vectors . To capture sentence - level semantics , use tree - based CNN and propose a stack - augmented parser - interpreter neural network ( SPINN ) which incorporates parsing information in a sequential manner . uses intra - attention on top of BiLSTM to generate sentence representation , and proposes an memory augmented neural network to encode the sentence . The next group of model , experiments ( 8 - 18 ) , uses cross sentence feature .
Among them , methods of learning representations of words , e.g. word2vec [ reference ] or GloVe , are relatively well - studied empirically and theoretically [ reference ][ reference ] , and some of them became typical choices to consider when initializing word representations for better performance at downstream tasks . Meanwhile , research on sentence representation is still in active progress , and accordingly various architecturesdesigned with different intuition and tailored for different tasks - are being proposed . In the midst of them , three architectures are most frequently used in obtaining sentence representation from words .
We empirically showed that the optimal structure might differ per task , and investigating task - specific latent tree structures could be an interesting future research direction . For future work , we plan to apply the core idea beyond sentence encoding . The performance could be further improved by applying intra - sentence or inter - sentence attention mechanisms .
Consequently , we refer to our model as sentence - state LSTM , or S - LSTM in short . Empirically , S - LSTM can give effective sentence encoding after 3 - 6 recurrent steps . In contrast , the number of recurrent steps necessary for BiLSTM scales with the size of the sentence .
We bring the idea of utilizing states coming from multiple directions to construction of stacked LSTM and apply the idea to the research of sentence representation learning . There is some prior work addressing the idea of incorporating more than one type of state , however to the best of our knowledge there is little work on applying the idea to sentence encoding and text classification . We conduct extensive evaluation of the proposed method and empirically prove its effectiveness on encoding sentences .
Then we add a fully - connected hidden layer to further mix the information in a sentence . The obtained vector representation of a sentence is denoted as ( also called a sentence embedding ) . Notice that the same tree - based convolution applies to both the premise and hypothesis .
Arc - II outperforms Arc - I significantly , showing the power of joint modeling of matching and sentence meaning . As another convolutional model , SENNA + MLP performs fairly well on this task , although still running behind the proposed convolutional architectures since it is too shallow to adequately model the sentence .
In contrast , transferring SNLI representations to SICK yields the best performance yet reported for an unaugmented neural network model , surpasses the available EOP models , and approaches both the overall state of the art at 84.6 % and the 84 % level of interannotator agreement , which likely represents an approximate performance ceiling . This suggests that the introduction of a large high - quality corpus makes it possible to train representation - learning models for sentence meaning that are competitive with the best hand - engineered models on inference tasks . We attempted to apply this same transfer evaluation technique to the RTE - 3 challenge , but found that the small training set ( 800 examples ) did not allow the model to adapt to the unfamiliar genre of text used in that corpus , such that no training configuration yielded competitive performance .
First , we explain how the NLI task can be used to train universal sentence encoding models using the SNLI task . We subsequently describe the architectures that we investigated for the sentence encoder , which , in our opinion , covers a suitable range of sentence encoders currently in use . Specifically , we examine standard recurrent models such as LSTMs and GRUs , for which we investigate mean and max - pooling over the hidden representations ; a self - attentive network that incorporates different views of the sentence ; and a hierarchical convolutional network that can be seen as a tree - based method that blends different levels of abstraction .
To evaluate the quality of these representations , we use them as features in 12 transfer tasks . We present our sentence - embedding evaluation procedure in this section . We constructed a sentence evaluation tool called SentEval to automate evaluation on all the tasks mentioned in this paper .
The resulting adaptive sampler finds harder negative examples , which forces the main model to learn a better representation of the data . We evaluate our proposal on learning word embeddings , order embeddings and knowledge graph embeddings and observe both faster convergence and improved results on multiple metrics .
Learning word embeddings has received a significant amount of attention recently . Often , word embeddings are learned in an unsupervised manner from a large collection of text .
An important class of problems involves training deep neural networks with sparse prediction targets of very high dimension D. These occur naturally in e.g. neural language models or the learning of word-embeddings , often posed as predicting the probability of next words among a vocabulary of size D ( e.g. 200,000 ) . Computing the equally large , but typically non-sparse D-dimensional output vector from a last hidden layer of reasonable dimension d ( e.g. 500 ) incurs a prohibitive O(Dd ) computational cost for each example , as does updating the $ D \times d$ output weight matrix and computing the gradient needed for backpropagation to previous layers .
Neural word embedding approaches , due to their ability to capture semantic meanings of vocabulary terms , have recently gained attention of the information retrieval ( IR ) community and have shown promising results in improving ad hoc retrieval performance . It has been observed that these approaches are sensitive to various choices made during the learning of word embeddings and their usage , often leading to poor reproducibility . We study the effect of varying following two parameters , viz . , i ) the term normalization and ii ) the choice of training collection , on ad hoc retrieval performance with word2vec and fastText embeddings .
Co-occurrences between two words provide useful insights into the semantics of those words . Consequently , numerous prior work on word embedding learning have used co-occurrences between two words as the training signal for learning word embeddings . However , in natural language texts it is common for multiple words to be related and co-occurring in the same context .
In recent years , there have been researches on using semantic knowledge and global statistical features to guide the learning of word embeddings . Though the syntax knowledge also plays an very important role in natural language understanding , its effectiveness on the word embedding learning is still far from well investigated . Inspired by the principle of the dissociation between nouns and verbs ( DNV ) in language acquisition observed in neuropsychology , we propose a novel model for word embeddings learning using DNV ( named Continuous Dissociation between Nouns and Verbs Model , CDNV ) .
The authors analyze three critical components in training word embeddings : model , corpus , and training parameters . They systematize existing neural-network-based word embedding methods and experimentally compare them using the same corpus .
However , it is not necessary for similar or related words to occur in the same context window . In this paper , we propose a new approach to combine different types of resources for training word embeddings . The lexical resources used in this work are Dependency Parse Tree and WordNet .
Learning embeddings of words and knowledge base elements is a promising approach for open domain question answering . Based on the remark that relations and entities are distinct object types lying in the same embedding space , we analyze the benefit of adding a regularizer favoring the embeddings of entities to be orthogonal to those of relations .
Learning high-quality word embeddings is of significant importance in achieving better performance in many down-stream learning tasks . On one hand , traditional word embeddings are trained on a large scale corpus for general-purpose tasks , which are often sub-optimal for many domain-specific tasks .
The idea of Word Embedding is based on the semantic distribution hypothesis of the linguist Harris ( 1954 ) , who believes that words of the same semantics are distributed in similar contexts . Learning of vector-space word embeddings is a technique of central importance in natural language processing . In recent years , cross-lingual word vectors have received more and more attention .
We also identify several strengths of the Open IE paradigm , which enable it to be a useful intermediate structure for end tasks . We survey its use in both human-facing applications and downstream NLP tasks , including event schema induction , sentence similarity , text comprehension , learning word vector embeddings , and more .
The error - exploring dynamic - oracle training always improves over static oracle training controlling for the transition system , but the arc - hybrid system slightly under - performs the arc - standard system when trained with static oracle . Flattening the sampling distribution ( ) is especially beneficial when training with pretrained word embeddings . In order to be able to compare with similar greedy parsers we report the performance of the parser on the multilingual treebanks of the CoNLL 2009 shared task .
paragraph : Effect of training set size Our motivation for introducing language parsing and pretrained word embeddings is to better generalize the concepts learned from the limited training examples . Words representing semantically close concepts ideally get assigned close word embeddings .
We refer to our baseline network as variational dropout LSTM , or VD - LSTM in short . section : EMPIRICAL VALIDATION FOR THE THEORY OF REUSING WORD EMBEDDINGS In Section 4 , we showed that the particular loss augmentation scheme we choose constrains the output projection matrix to be close to the input embedding matrix , without explicitly doing so by reusing the input embedding matrix .
A word embedding generation method based on flexible length context Multi-Emotion Category Improving Embedding for Sentiment Classification Word Embedding Visualization Via Dictionary Learning Combining Continuous Word Representation and Prosodic Features for ASR Error Prediction Hash2Vec, Feature Hashing for Word Embeddings Modern RESTful API DLs and frameworks for RESTful web services API schema modeling, documenting, visualizing Word Embeddings in Low Resource Gujarati Language Adverse drug event and medication extraction in electronic health records via a cascading architecture with different sequence labeling models and word embeddings Comparative analysis of different word embedding models aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks Effective LSTMs for Target-Dependent Sentiment Classification Supervised Learning of Universal Sentence Representations from Natural Language Inference Data Compressing Word Embeddings via Deep Compositional Code Learning Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation Dynamic Integration of Background Knowledge in Neural NLU Systems Multi-Label Graph Convolutional Network Representation Learning MinAtar: An Atari-inspired Testbed for More Efficient Reinforcement Learning Experiments Error Analysis on Graph Laplacian Regularized Estimator Revisiting Unreasonable Effectiveness of Data in Deep Learning Era Chunking based malayalam paraphrase identification using unfolding recursive autoencoders Where Did the Political News Event Happen? Primary Focus Location Extraction in Different Languages REGMAPR - Text Matching Made Easy The grammar of English predicate complement constructions Situating Sentence Embedders with Nearest Neighbor Overlap Improving Word Embedding Compositionality using Lexicographic Definitions Learning and Evaluating Sparse Interpretable Sentence Embeddings Learning Joint Representations of Videos and Sentences with Web Image Search DT_Team at SemEval-2017 Task 1: Semantic Similarity Using Alignments, Sentence-Level Embeddings and Gaussian Mixture Model Output Graph-based Dependency Parsing with Bidirectional LSTM A C-LSTM Neural Network for Text Classification Natural Language Inference over Interaction Space Learning to Compose Task-Specific Tree Structures Sentence-State LSTM for Text Representation Cell-aware Stacked LSTMs for Modeling Sentences Natural Language Inference by Tree-Based Convolution and Heuristic Matching Convolutional Neural Network Architectures for Matching Natural Language Sentences A large annotated corpus for learning natural language inference Supervised Learning of Universal Sentence Representations from Natural Language Inference Data Adversarial Contrastive Estimation A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings Exact gradient updates in time independent of output size for the spherical loss family Using Word Embeddings for Information Retrieval: How Collection and Term Normalization Choices Affect Performance Using $k$-way Co-occurrences for Learning Word Embeddings A novel word embedding learning model using the dissociation between nouns and verbs How to Generate a Good Word Embedding Multi-Context Information for Word Representation Learning Orthogonality regularizer for question answering Lifelong Word Embedding via Meta-Learning Learning Tibetan-Chinese Cross-Lingual Word Embeddings Open Information Extraction Systems and Downstream Applications Training with Exploration Improves a Greedy Stack LSTM Parser Graph-Structured Representations for Visual Question Answering Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling
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