We briefly introduce Codenames – an existing party board game – and the way of recording word associations suggested by human players . Advanced word embedding models are then compared on the collected data and it is demonstrated that they often fail in the cases of complex word associations that go beyond simple contextual interchangeability . We conclude with an initial evaluation of the automatic guessing of associated words based on clues provided by human players and a discussion on further extensions of the system towards a wide language coverage and explanations of word associations in the language learning context . This paper presents word embedding-based approach to text classification . In this study , we introduce a new vector space model called Semantically-Augmented Statistical Vector Space Model ( SAS-VSM ) that is a statistical VSM with a semantic VSM for information access systems , especially for automatic text classification . 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 . The input data ( raw text strings ) are discrete , sparse and high dimensional . We assessed scalable statistical models for high dimensional discrete data , including fitting , assessing and exploring models from three broad statistical areas : i ) matrix factorization/decomposition models ii ) probabilistic topic models and iii ) word-vector embedding models . ResultsEMRALD is comprised of 12 text data streams . In contrast , this study introduced convolutional neural network ( CNN ) and long-short term memory ( LSTM ) for the shared task of Chinese Grammatical Error Diagnosis ( CGED ) . Different from traditional word-based embedding , single word embedding was used as input of CNN and LSTM . The proposed single word embedding can capture both semantic and syntactic information to detect those four type grammatical error . We tested our models on the i2b2/VA relation classification challenge dataset . We showed that our segment LSTM model , with only word embedding feature and no manual feature engineering , achieved a micro-averaged f-measure of 0.661 for classifying medical problem-treatment relations , 0.800 for medical problem-test relations , and 0.683 for medical problem-medical problem relations . These results are comparable to those of the state-of-the-art systems on the i2b2/VA relation classification challenge . 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 . 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 . Intuitively , the update gate controls how much the unit updates its hidden state , and the reset gate determines how much information from the previous hidden state needs to be reset . The input to the character - level GRUs is character embeddings , while the input to the word - level GRUs is the concatenation of character - level GRU hidden states and word embeddings . Both GRUs are bi - directional and have two layers . paragraph : 2 . Word Embedding Layer . Word embedding layer also maps each word to a high - dimensional vector space . We use pre - trained word vectors , GloVe glove , to obtain the fixed word embedding of each word . Moreover , let be the dimension of the hidden state of the - th RNN , and let be the dimensions of the embedding vectors . Then the RNN language models predict probability distribution by the following equation : where is a weight matrix , is a word embedding matrix , is a one - hot vector of input word at timestep , and is the hidden state of the - th RNN at timestep . We define at timestep as a zero vector : . subsection : Experimental Setup We use pre - trained - Glove vectors to initialize our word embedding vectors . All hidden states of BiLSTMs during input encoding and inference have dimensions ( and ) . The idea of using a recurrent layer over characters to induce a complementary view of a word has occurred in numerous papers . Perhaps the earliest is Santos and Zadrozny ( 2014 ) who compare character - based LSTM encodings to traditional word - based embeddings . [ reference ] take this a step further and combine the word embeddings with a recurrent character encoding of the word - instead of just relying on one or the other . subsection : Formal Description Our model uses one word embedding function and two encoder functions . The word embedding function translates words into vector representations . 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 ) . paragraph : Pre - training influence . As described in the previous section , we initialized the word - vector embedding with pre - trained word vectors obtained from word2vec . To test the influence of this initialization , we trained a LSTM + A model on the high - confidence corpus , and a LSTM + A + D model on the WSJ corpus , starting with randomly initialized word - vector embeddings . It is shown that a word or an expression ’s context vector is an efficient feature to be used for predicting the type of a named entity . Distributed word representations are now claimed ( and on a reasonable basis ) to be one of the most promising distributional semantics models . In the described experiment on retrieving similar named entities the results go further than retrieving named entities of the same type or named entities-individuals of the same class : it is shown that equivalent variants of a named entity can be extracted . Distributed Representations of Words , Documents and Class Labels . While the traditional method of deriving representations for documents was bag-of-words , they suffered from high dimensionality and sparsity . Text representation is a fundamental process that affects the effectiveness of many text summarization methods . Distributed word vector representations have been shown to improve Natural Language Processing ( NLP ) tasks , especially Automatic Text Summarization ( ATS ) . However , most of them do not consider the order and the context of the words in a sentence . Word vectors are distributed representations of word features . Continuous Bag-of-Words Model(CBOW ) is a state-of-the-art model for learning word vectors , yet can be ameliorated for learning better word vectors because we find that CBOW is vulnerable to be overfitted and unstable . Word embeddings are the distributed representation of the words in numerical form . Recent research in word embeddings shows the importance of using them in deep learning algorithms . Recurrent Neural Network ( RNN ) is one of the most popular architectures used in Natural Language Processsing ( NLP ) tasks because its recurrent structure is very suitable to process variable - length text . RNN can utilize distributed representations of words by first converting the tokens comprising each text into vectors , which form a matrix . And this matrix includes two dimensions : the time - step dimension and the feature vector dimension . subsection : Learning Vector Representation of Words This section introduces the concept of distributed vector representation of words . A well known framework for learning the word vectors is shown in Figure [ reference ] . This model is also similar to the Skip - gram model in word vectors . In our experiments , each paragraph vector is a combination of two vectors : one learned by the standard paragraph vector with distributed memory ( PV - DM ) and one learned by the paragraph vector with distributed bag of words ( PV - DBOW ) . PV - DM alone usually works well for most tasks ( with state - of - art performances ) , but its combination with PV - DBOW is usually more consistent across many tasks that we try and therefore strongly recommended . We formulate language modeling as a matrix factorization problem , and show that the expressiveness of Softmax - based models ( including the majority of neural language models ) is limited by a Softmax bottleneck . Given that natural language is highly context - dependent , this further implies that in practice Softmax with distributed word embeddings does not have enough capacity to model natural language . We propose a simple and effective method to address this issue , and improve the state - of - the - art perplexities on Penn Treebank and WikiText - 2 to 47.69 and 40.68 respectively . More importantly , due to the fact that natural language is highly context - dependent , the matrix to be factorized can be high - rank . This further implies that standard Softmax - based language models with distributed ( output ) word embeddings do not have enough capacity to model natural language . We call this the Softmax bottleneck . NLI has been addressed using a variety of techniques , including those based on symbolic logic , knowledge bases , and neural networks . In recent years , it has become an important testing ground for approaches employing distributed word and phrase representations . Distributed representations excel at capturing relations based in similarity , and have proven effective at modeling simple dimensions of meaning like evaluative sentiment ( e.g. , ? ) , but it is less clear that they can be trained to support the full range of logical and commonsense inferences required for NLI . One of the most obvious facts one could observe from table [ reference ] and figure [ reference ] is that the bag - of - means model performs worse in every case . Comparing with traditional models , this suggests such a simple use of a distributed word representation may not give us an advantage to text classification . However , our experiments does not speak for any other language processing tasks or use of word2vec in any other way . Specifically , VDA reduces the joint marginal and conditional distributions across domains in an unsupervised manner where no label is available in test set . Moreover , VDA constructs condensed domain invariant clusters in the embedding representation to separate various classes alongside the domain transfer . In this work , we employ pseudo target labels refinement to iteratively converge to final solution . Natural language processing research has made major advances with the concept of representing words , sentences , paragraphs , and even documents by embedded vector representations . We apply this idea to the problem of relating foods , as expressed in natural language meal descriptions , to corresponding database entries . In this paper , we investigate the advantages of recurrent neural networks-based language representations ( e.g. , BERT , BiLSTM ) in order to build ensemble classifiers that can accurately predict if one news title is related to , and , additionally disagrees with an earlier news title . Our experiments , on a dataset of 321k news titles created for the WSDM 2019 challenge , show that the BERT-based models significantly outperform BiLSTM , which in-turn significantly outperforms a simpler embedding-based representation . Furthermore , even the state-of-the-art BERT approach can be enhanced when combined with a simple BM25 feature . Starting with a simple assumption that each observation may or may not depend on its context , a conditional probability distribution is decomposed into two parts : context-free and context-sensitive . Then by employing the log-linear word production model for relating random variables to their embedding space representation and making use of the convexity of natural exponential function , we show that the embedding of an observation can also be decomposed into a weighted sum of two vectors , representing its context-free and context-sensitive parts , respectively . This simple treatment of context provides a unified view of many existing deep learning models , leading to revisions of these models able to achieve significant performance boost . Then , we employ this global information to predict an explicit topic for each word and regard the combination of each word and its assigned topic as a new pseudo word . After that , we exploit pseudo word sequence as the input of embedding vector model and finally learn the text feature which could reflect the text semantic with social network characteristics . Classification and search experiments in real-world datasets of the social network have verified that the proposed STTE has better semantic modeling ability than other baseline methods . Data-hidden or encryption techniques normally be used to keep privacy , and as a result can reduce the veracity or can′t preserve the distance of data . The embedding vector space techniques which are frequently used in complex objects matching area are proposed , by selecting random parameters to generate new coordinate system , embedding the original data into the new space and processing the similarity linking there . This Method keeps the distance of the data , and at the same time , preserves the privacy without reducing the linkage affection . 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 . As the output feature map becomes more complex , it is able to learn patterns for more complicate relations . For example , MemN2N , which has the lowest performance among the four models , measures the relatedness between question and sentence by the inner product , while the best performing DMN + uses inner product and absolute difference with two embedding matrices . Recently , a new architecture called Relation Network ( RN ) section : EMBEDDING COMPONENT We first embed words in each x i = { x i1 , x i2 , x i3 , ... , x ini } and u to a continuous space multiplying an embedding matrix A ∈ R d×V . To facilitate our models , we firstly give some definitions . Given two sequences and , we let denote the embedded representation of the word . The standard LSTM have one temporal dimension . In this work , we propose a novel hypernetwork model for link prediction in knowledge graphs . In summary , the hypernetwork projects a vector embedding of each relation via a fully connected layer , the result of which is reshaped to give a set of convolutional filter weight vectors for each relation . We explain this process in more detail below . Hence , the size of the input layer is in the point - wise setting , and in the pair - wise setting . Embedding vector representation ( Embed ) : The major weakness of the previous input representation is that words are treated as discrete units , hence prohibiting the network from performing soft matching between semantically similar words in queries and documents . To see if there is a real benefit from using a non - linear neural network in different settings , we examined RankSVM Joachims:2002 as a strong - performing pair - wise learning to rank method with linear kernel that is fed with different inputs : dense vector representation , sparse vector representation , and embedding vector representation . Considering that off - the - shelf RankSVM is not able to learn embedding representations during training , for embedding vector representation , instead of learning embeddings we use a pre - trained embedding matrix trained on Google News and fixed IDF weights . The results are reported in Table [ reference ] . Training with lower resolution faces could improve this range further . subsection : Embedding Dimensionality We explored various embedding dimensionalities and selected 128 for all experiments other than the comparison reported in Table [ reference ] . Here , we still train on the first two sentences , but we exploit the parts - of - speech and named - entity tags in the annotated gigaword corpus as well as TF , IDF values , to augment the input embeddings on the source side as described in Sec [ reference ] . In total , our embedding vector grew from the original 100 to 155 , and produced incremental gains compared to its counterpart words - lvt2k - 2sent as shown in Table [ reference ] , demonstrating the utility of syntax based features in this task . feats Simple vector space arithmetic using cosine distances has been shown to capture certain types of analogies , such as reasoning about plurals from singulars , past tense from present tense , etc . In this paper , we introduce a new approach to capture analogies in continuous word representations , based on modeling not just individual word vectors , but rather the subspaces spanned by groups of words . We exploit the property that the set of subspaces in n-dimensional Euclidean space form a curved manifold space called the Grassmannian , a quotient subgroup of the Lie group of rotations in n- dimensions . These represenations are not sufficiently effective for sentiment analysis as , in many cases , words with similar syntactic context , having neighboring word vectors can bear opposite sentiment polarity . In this paper , we present a weighted average word embeddings method which incorporates sentiment information in the continuous representation of words based on an adapted version of the delta TFIDF measure . Majority voting was then applied to determine the final polarity involving three machine learning classifiers notably , Support Vector Machine , Maximum Entropy and Naïve Bayes . To overcome this issue , in this paper we propose a centroidbased method for text summarization that exploits the compositional capabilities of word embeddings . The evaluations on multi-document and multilingual datasets prove the effectiveness of the continuous vector representation of words compared to the bag-of-words model . Despite its simplicity , our method achieves good performance even in comparison to more complex deep learning models . We evaluate different compositions for sentence representation on a standard dataset using the ROUGE evaluation measures . Our experiments show that the evaluated methods improve the performance of a state-of-the-art summarization framework and strongly indicate the benefits of continuous word vector representations for automatic summarization . While most representations ignore words ' local context , a natural way to induce context-dependent representations is to perform inference in a probabilistic latent-variable sequence model . Given the recent success of continuous vector space word representations , we provide such an inference procedure for continuous states , where words ' representations are given by the posterior mean of a linear dynamical system . Here , efficient inference can be performed using Kalman filtering . However these discrete representations suffer from a number of issues such as fixing the number of discrete classes and also such a representation does not capture the co-occurrence statistics of the words . As a result , the use of continuous valued word representation was proposed in literature . In this paper , we propose a neural network dictionary learning architecture to induce task specific continuous valued word representations , and show that these task specific features perform better at phrase break prediction as compared to continuous features derived using Latent Semantic Analysis ( LSA ) . Content-based event retrieval in unconstrained web videos , based on query tags , is a hard problem due to large intra-class variances , and limited vocabulary and accuracy of the video concept detectors , creating a " semantic query gap " . We present a technique to overcome this gap by using continuous word space representations to explicitly compute query and detector concept similarity . This not only allows for fast query-video similarity computation with implicit query expansion , but leads to a compact video representation , which allows implementation of a real-time retrieval system that can fit several thousand videos in a few hundred megabytes of memory . In comparison to standard feed - forward MLPs or DNNs , these acoustic models have the ability to model a large amount of acoustic context with temporal invariance , and in the case of convolutional models , with frequency invariance as well . In language modeling , recurrent models appear to improve over classical N - gram models through the generalization ability of continuous word representations . In the meantime , ensemble learning has become commonly used in several neural models , to improve robustness by reducing bias and variance . The second layer predicts the target word ( s ) via hierarchical soft - max . Word2Vec has two main formulations for the target prediction : skip - gram ( SG ) and continuous bag - of - words ( CBOW ) . In SG , words within a local context window are predicted from the centering word . For sentence / document level sentiment classification , previous studies mostly have two steps . They first learn continuous word vector embeddings from data . Afterwards , semantic compositional approaches are used to compute the vector of a sentence / document from the vectors of its constituents based on the principle of compositionality . Recent work for learning word representations has applied successfully to many NLP applications , such as sentiment analysis and question answering . However , most of these models assume a single vector per word type without considering polysemy and homonymy . In this paper we propose a novel model , CroLSim , to detect similar software applications across different programming languages . We define a semantic relationship among cross-language libraries and API methods ( both local and third party ) using functional descriptions and a word-vector learning model . Our experiments show that CroLSim can successfully detect cross-language similar software applications , which outperforms all existing approaches ( mean average precision rate of 0.65 , confidence rate of 3.6 , and 75 % highly rated successful queries ) . In the context of natural language processing , representation learning has emerged as a newly active research subject because of its excellent performance in many applications . Learning representations of words is a pioneering study in this school of research . However , paragraph ( or sentence and document ) embedding learning is more suitable/reasonable for some realistic tasks such as document summarization . The model outperforms the baseline system which is shown by an increase in BLEU score . We also show the effect of varying the vector dimension and context window for two different approaches of learning word vectors . We conclude that initialization does not play an important role for classification . However , learning of word vectors is crucial to obtain good results . While previous studies show that modeling the minimum meaning-bearing units ( characters or morphemes ) benefits learning vector representations of words , they ignore the semantic dependencies across these units when deriving word vectors . In this work , we propose to improve the learning of Chinese word embeddings by exploiting semantic knowledge . Learning vector space representation of words ( i.e. , word embeddings ) has recently attracted wide research interests , and has been extended to cross-lingual scenario . Currently most cross-lingual word embedding learning models are based on sentence alignment , which inevitably introduces much noise . 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 . In recent years , learning word vector representations has attracted much interest in Natural Language Processing . Word representations or embeddings learned using unsupervised methods help addressing the problem of traditional bag-of-word approaches which fail to capture contextual semantics . Moreover , the fact that the two components are separately trained can limit its expressiveness . levy2014neural also considered the matrix factorization perspective , but in the context of learning word embeddings . In a general sense , Mixture of Softmaxes proposed in this work can be seen as a particular instantiation of the long - existing idea called Mixture of Experts ( MoE ) jacobs1991adaptive . Due to their ability to capture syntactic and semantic information of words from large scale unlabeled text , pretrained word vectors Turian2010WordRA , word2vec , Pennington2014GloveGV are a standard component of most state - of - the - art NLP architectures , including for question answering liu2017stochastic , textual entailment Chen2017EnhancedLF and semantic role labeling He2017DeepSR . However , these approaches for learning word vectors only allow a single context - independent representation for each word . Previously proposed methods overcome some of the shortcomings of traditional word vectors by either enriching them with subword information [ e.g. , ] Overall , this formulation is similar to the approach of Peters2017SemisupervisedST , with the exception that we share some weights between directions instead of using completely independent parameters . In the next section , we depart from previous work by introducing a new approach for learning word representations that are a linear combination of the biLM layers . subsection : ELMo subsubsection : Vector arithmetic on face samples In the context of evaluating learned representations of words mikolov2013distributed demonstrated that simple arithmetic operations revealed rich linear structure in representation space . One canonical example demonstrated that the vector ( ”King ” ) - vector ( ”Man ” ) + vector ( ”Woman ” ) resulted in a vector whose nearest neighbor was the vector for Queen . These methods are the inspiration for our Paragraph Vector methods . subsection : Learning Vector Representation of Words This section introduces the concept of distributed vector representation of words . cluster tightness Next , we perform an analysis on representation learning of words ( word embeddings ) for the English ( PTB - WSJ ) and French ( UD ) experiments . We hypothesize that adversarial training ( AT ) helps to learn better word embeddings so that the POS tag prediction of a word can not be influenced by a small perturbation in the input embedding . The essence of convolution layers is to convert text regions of a fixed size ( e.g. , “ am so happy ” with size 3 ) to feature vectors , as described later . In that sense , a word vector learning layer is a special ( and unusual ) case of convolution layer with region size one . Why is size one appropriate if bi - grams are more discriminating than uni - grams ? Also , with our approach , a system is simpler with one fewer layer – no need to tune the dimensionality of word vectors or meta - parameters for word vector learning . KGB14 proposed complex modifications of CNN for sentence modeling .
We briefly introduce Codenames – an existing party board game – and the way of recording word associations suggested by human players . Advanced word embedding models are then compared on the collected data and it is demonstrated that they often fail in the cases of complex word associations that go beyond simple contextual interchangeability . We conclude with an initial evaluation of the automatic guessing of associated words based on clues provided by human players and a discussion on further extensions of the system towards a wide language coverage and explanations of word associations in the language learning context .
This paper presents word embedding-based approach to text classification . In this study , we introduce a new vector space model called Semantically-Augmented Statistical Vector Space Model ( SAS-VSM ) that is a statistical VSM with a semantic VSM for information access systems , especially for automatic text classification .
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 .
The input data ( raw text strings ) are discrete , sparse and high dimensional . We assessed scalable statistical models for high dimensional discrete data , including fitting , assessing and exploring models from three broad statistical areas : i ) matrix factorization/decomposition models ii ) probabilistic topic models and iii ) word-vector embedding models . ResultsEMRALD is comprised of 12 text data streams .
In contrast , this study introduced convolutional neural network ( CNN ) and long-short term memory ( LSTM ) for the shared task of Chinese Grammatical Error Diagnosis ( CGED ) . Different from traditional word-based embedding , single word embedding was used as input of CNN and LSTM . The proposed single word embedding can capture both semantic and syntactic information to detect those four type grammatical error .
We tested our models on the i2b2/VA relation classification challenge dataset . We showed that our segment LSTM model , with only word embedding feature and no manual feature engineering , achieved a micro-averaged f-measure of 0.661 for classifying medical problem-treatment relations , 0.800 for medical problem-test relations , and 0.683 for medical problem-medical problem relations . These results are comparable to those of the state-of-the-art systems on the i2b2/VA relation classification challenge .
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 .
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 .
Intuitively , the update gate controls how much the unit updates its hidden state , and the reset gate determines how much information from the previous hidden state needs to be reset . The input to the character - level GRUs is character embeddings , while the input to the word - level GRUs is the concatenation of character - level GRU hidden states and word embeddings . Both GRUs are bi - directional and have two layers .
paragraph : 2 . Word Embedding Layer . Word embedding layer also maps each word to a high - dimensional vector space . We use pre - trained word vectors , GloVe glove , to obtain the fixed word embedding of each word .
Moreover , let be the dimension of the hidden state of the - th RNN , and let be the dimensions of the embedding vectors . Then the RNN language models predict probability distribution by the following equation : where is a weight matrix , is a word embedding matrix , is a one - hot vector of input word at timestep , and is the hidden state of the - th RNN at timestep . We define at timestep as a zero vector : .
subsection : Experimental Setup We use pre - trained - Glove vectors to initialize our word embedding vectors . All hidden states of BiLSTMs during input encoding and inference have dimensions ( and ) .
The idea of using a recurrent layer over characters to induce a complementary view of a word has occurred in numerous papers . Perhaps the earliest is Santos and Zadrozny ( 2014 ) who compare character - based LSTM encodings to traditional word - based embeddings . [ reference ] take this a step further and combine the word embeddings with a recurrent character encoding of the word - instead of just relying on one or the other .
subsection : Formal Description Our model uses one word embedding function and two encoder functions . The word embedding function translates words into vector representations .
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 ) .
paragraph : Pre - training influence . As described in the previous section , we initialized the word - vector embedding with pre - trained word vectors obtained from word2vec . To test the influence of this initialization , we trained a LSTM + A model on the high - confidence corpus , and a LSTM + A + D model on the WSJ corpus , starting with randomly initialized word - vector embeddings .
It is shown that a word or an expression ’s context vector is an efficient feature to be used for predicting the type of a named entity . Distributed word representations are now claimed ( and on a reasonable basis ) to be one of the most promising distributional semantics models . In the described experiment on retrieving similar named entities the results go further than retrieving named entities of the same type or named entities-individuals of the same class : it is shown that equivalent variants of a named entity can be extracted .
Distributed Representations of Words , Documents and Class Labels . While the traditional method of deriving representations for documents was bag-of-words , they suffered from high dimensionality and sparsity .
Text representation is a fundamental process that affects the effectiveness of many text summarization methods . Distributed word vector representations have been shown to improve Natural Language Processing ( NLP ) tasks , especially Automatic Text Summarization ( ATS ) . However , most of them do not consider the order and the context of the words in a sentence .
Word vectors are distributed representations of word features . Continuous Bag-of-Words Model(CBOW ) is a state-of-the-art model for learning word vectors , yet can be ameliorated for learning better word vectors because we find that CBOW is vulnerable to be overfitted and unstable .
Word embeddings are the distributed representation of the words in numerical form . Recent research in word embeddings shows the importance of using them in deep learning algorithms .
Recurrent Neural Network ( RNN ) is one of the most popular architectures used in Natural Language Processsing ( NLP ) tasks because its recurrent structure is very suitable to process variable - length text . RNN can utilize distributed representations of words by first converting the tokens comprising each text into vectors , which form a matrix . And this matrix includes two dimensions : the time - step dimension and the feature vector dimension .
subsection : Learning Vector Representation of Words This section introduces the concept of distributed vector representation of words . A well known framework for learning the word vectors is shown in Figure [ reference ] .
This model is also similar to the Skip - gram model in word vectors . In our experiments , each paragraph vector is a combination of two vectors : one learned by the standard paragraph vector with distributed memory ( PV - DM ) and one learned by the paragraph vector with distributed bag of words ( PV - DBOW ) . PV - DM alone usually works well for most tasks ( with state - of - art performances ) , but its combination with PV - DBOW is usually more consistent across many tasks that we try and therefore strongly recommended .
We formulate language modeling as a matrix factorization problem , and show that the expressiveness of Softmax - based models ( including the majority of neural language models ) is limited by a Softmax bottleneck . Given that natural language is highly context - dependent , this further implies that in practice Softmax with distributed word embeddings does not have enough capacity to model natural language . We propose a simple and effective method to address this issue , and improve the state - of - the - art perplexities on Penn Treebank and WikiText - 2 to 47.69 and 40.68 respectively .
More importantly , due to the fact that natural language is highly context - dependent , the matrix to be factorized can be high - rank . This further implies that standard Softmax - based language models with distributed ( output ) word embeddings do not have enough capacity to model natural language . We call this the Softmax bottleneck .
NLI has been addressed using a variety of techniques , including those based on symbolic logic , knowledge bases , and neural networks . In recent years , it has become an important testing ground for approaches employing distributed word and phrase representations . Distributed representations excel at capturing relations based in similarity , and have proven effective at modeling simple dimensions of meaning like evaluative sentiment ( e.g. , ? ) , but it is less clear that they can be trained to support the full range of logical and commonsense inferences required for NLI .
One of the most obvious facts one could observe from table [ reference ] and figure [ reference ] is that the bag - of - means model performs worse in every case . Comparing with traditional models , this suggests such a simple use of a distributed word representation may not give us an advantage to text classification . However , our experiments does not speak for any other language processing tasks or use of word2vec in any other way .
Specifically , VDA reduces the joint marginal and conditional distributions across domains in an unsupervised manner where no label is available in test set . Moreover , VDA constructs condensed domain invariant clusters in the embedding representation to separate various classes alongside the domain transfer . In this work , we employ pseudo target labels refinement to iteratively converge to final solution .
Natural language processing research has made major advances with the concept of representing words , sentences , paragraphs , and even documents by embedded vector representations . We apply this idea to the problem of relating foods , as expressed in natural language meal descriptions , to corresponding database entries .
In this paper , we investigate the advantages of recurrent neural networks-based language representations ( e.g. , BERT , BiLSTM ) in order to build ensemble classifiers that can accurately predict if one news title is related to , and , additionally disagrees with an earlier news title . Our experiments , on a dataset of 321k news titles created for the WSDM 2019 challenge , show that the BERT-based models significantly outperform BiLSTM , which in-turn significantly outperforms a simpler embedding-based representation . Furthermore , even the state-of-the-art BERT approach can be enhanced when combined with a simple BM25 feature .
Starting with a simple assumption that each observation may or may not depend on its context , a conditional probability distribution is decomposed into two parts : context-free and context-sensitive . Then by employing the log-linear word production model for relating random variables to their embedding space representation and making use of the convexity of natural exponential function , we show that the embedding of an observation can also be decomposed into a weighted sum of two vectors , representing its context-free and context-sensitive parts , respectively . This simple treatment of context provides a unified view of many existing deep learning models , leading to revisions of these models able to achieve significant performance boost .
Then , we employ this global information to predict an explicit topic for each word and regard the combination of each word and its assigned topic as a new pseudo word . After that , we exploit pseudo word sequence as the input of embedding vector model and finally learn the text feature which could reflect the text semantic with social network characteristics . Classification and search experiments in real-world datasets of the social network have verified that the proposed STTE has better semantic modeling ability than other baseline methods .
Data-hidden or encryption techniques normally be used to keep privacy , and as a result can reduce the veracity or can′t preserve the distance of data . The embedding vector space techniques which are frequently used in complex objects matching area are proposed , by selecting random parameters to generate new coordinate system , embedding the original data into the new space and processing the similarity linking there . This Method keeps the distance of the data , and at the same time , preserves the privacy without reducing the linkage affection .
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 .
As the output feature map becomes more complex , it is able to learn patterns for more complicate relations . For example , MemN2N , which has the lowest performance among the four models , measures the relatedness between question and sentence by the inner product , while the best performing DMN + uses inner product and absolute difference with two embedding matrices . Recently , a new architecture called Relation Network ( RN )
section : EMBEDDING COMPONENT We first embed words in each x i = { x i1 , x i2 , x i3 , ... , x ini } and u to a continuous space multiplying an embedding matrix A ∈ R d×V .
To facilitate our models , we firstly give some definitions . Given two sequences and , we let denote the embedded representation of the word . The standard LSTM have one temporal dimension .
In this work , we propose a novel hypernetwork model for link prediction in knowledge graphs . In summary , the hypernetwork projects a vector embedding of each relation via a fully connected layer , the result of which is reshaped to give a set of convolutional filter weight vectors for each relation . We explain this process in more detail below .
Hence , the size of the input layer is in the point - wise setting , and in the pair - wise setting . Embedding vector representation ( Embed ) : The major weakness of the previous input representation is that words are treated as discrete units , hence prohibiting the network from performing soft matching between semantically similar words in queries and documents .
To see if there is a real benefit from using a non - linear neural network in different settings , we examined RankSVM Joachims:2002 as a strong - performing pair - wise learning to rank method with linear kernel that is fed with different inputs : dense vector representation , sparse vector representation , and embedding vector representation . Considering that off - the - shelf RankSVM is not able to learn embedding representations during training , for embedding vector representation , instead of learning embeddings we use a pre - trained embedding matrix trained on Google News and fixed IDF weights . The results are reported in Table [ reference ] .
Training with lower resolution faces could improve this range further . subsection : Embedding Dimensionality We explored various embedding dimensionalities and selected 128 for all experiments other than the comparison reported in Table [ reference ] .
Here , we still train on the first two sentences , but we exploit the parts - of - speech and named - entity tags in the annotated gigaword corpus as well as TF , IDF values , to augment the input embeddings on the source side as described in Sec [ reference ] . In total , our embedding vector grew from the original 100 to 155 , and produced incremental gains compared to its counterpart words - lvt2k - 2sent as shown in Table [ reference ] , demonstrating the utility of syntax based features in this task . feats
Simple vector space arithmetic using cosine distances has been shown to capture certain types of analogies , such as reasoning about plurals from singulars , past tense from present tense , etc . In this paper , we introduce a new approach to capture analogies in continuous word representations , based on modeling not just individual word vectors , but rather the subspaces spanned by groups of words . We exploit the property that the set of subspaces in n-dimensional Euclidean space form a curved manifold space called the Grassmannian , a quotient subgroup of the Lie group of rotations in n- dimensions .
These represenations are not sufficiently effective for sentiment analysis as , in many cases , words with similar syntactic context , having neighboring word vectors can bear opposite sentiment polarity . In this paper , we present a weighted average word embeddings method which incorporates sentiment information in the continuous representation of words based on an adapted version of the delta TFIDF measure . Majority voting was then applied to determine the final polarity involving three machine learning classifiers notably , Support Vector Machine , Maximum Entropy and Naïve Bayes .
To overcome this issue , in this paper we propose a centroidbased method for text summarization that exploits the compositional capabilities of word embeddings . The evaluations on multi-document and multilingual datasets prove the effectiveness of the continuous vector representation of words compared to the bag-of-words model . Despite its simplicity , our method achieves good performance even in comparison to more complex deep learning models .
We evaluate different compositions for sentence representation on a standard dataset using the ROUGE evaluation measures . Our experiments show that the evaluated methods improve the performance of a state-of-the-art summarization framework and strongly indicate the benefits of continuous word vector representations for automatic summarization .
While most representations ignore words ' local context , a natural way to induce context-dependent representations is to perform inference in a probabilistic latent-variable sequence model . Given the recent success of continuous vector space word representations , we provide such an inference procedure for continuous states , where words ' representations are given by the posterior mean of a linear dynamical system . Here , efficient inference can be performed using Kalman filtering .
However these discrete representations suffer from a number of issues such as fixing the number of discrete classes and also such a representation does not capture the co-occurrence statistics of the words . As a result , the use of continuous valued word representation was proposed in literature . In this paper , we propose a neural network dictionary learning architecture to induce task specific continuous valued word representations , and show that these task specific features perform better at phrase break prediction as compared to continuous features derived using Latent Semantic Analysis ( LSA ) .
Content-based event retrieval in unconstrained web videos , based on query tags , is a hard problem due to large intra-class variances , and limited vocabulary and accuracy of the video concept detectors , creating a " semantic query gap " . We present a technique to overcome this gap by using continuous word space representations to explicitly compute query and detector concept similarity . This not only allows for fast query-video similarity computation with implicit query expansion , but leads to a compact video representation , which allows implementation of a real-time retrieval system that can fit several thousand videos in a few hundred megabytes of memory .
In comparison to standard feed - forward MLPs or DNNs , these acoustic models have the ability to model a large amount of acoustic context with temporal invariance , and in the case of convolutional models , with frequency invariance as well . In language modeling , recurrent models appear to improve over classical N - gram models through the generalization ability of continuous word representations . In the meantime , ensemble learning has become commonly used in several neural models , to improve robustness by reducing bias and variance .
The second layer predicts the target word ( s ) via hierarchical soft - max . Word2Vec has two main formulations for the target prediction : skip - gram ( SG ) and continuous bag - of - words ( CBOW ) . In SG , words within a local context window are predicted from the centering word .
For sentence / document level sentiment classification , previous studies mostly have two steps . They first learn continuous word vector embeddings from data . Afterwards , semantic compositional approaches are used to compute the vector of a sentence / document from the vectors of its constituents based on the principle of compositionality .
Recent work for learning word representations has applied successfully to many NLP applications , such as sentiment analysis and question answering . However , most of these models assume a single vector per word type without considering polysemy and homonymy .
In this paper we propose a novel model , CroLSim , to detect similar software applications across different programming languages . We define a semantic relationship among cross-language libraries and API methods ( both local and third party ) using functional descriptions and a word-vector learning model . Our experiments show that CroLSim can successfully detect cross-language similar software applications , which outperforms all existing approaches ( mean average precision rate of 0.65 , confidence rate of 3.6 , and 75 % highly rated successful queries ) .
In the context of natural language processing , representation learning has emerged as a newly active research subject because of its excellent performance in many applications . Learning representations of words is a pioneering study in this school of research . However , paragraph ( or sentence and document ) embedding learning is more suitable/reasonable for some realistic tasks such as document summarization .
The model outperforms the baseline system which is shown by an increase in BLEU score . We also show the effect of varying the vector dimension and context window for two different approaches of learning word vectors .
We conclude that initialization does not play an important role for classification . However , learning of word vectors is crucial to obtain good results .
While previous studies show that modeling the minimum meaning-bearing units ( characters or morphemes ) benefits learning vector representations of words , they ignore the semantic dependencies across these units when deriving word vectors . In this work , we propose to improve the learning of Chinese word embeddings by exploiting semantic knowledge .
Learning vector space representation of words ( i.e. , word embeddings ) has recently attracted wide research interests , and has been extended to cross-lingual scenario . Currently most cross-lingual word embedding learning models are based on sentence alignment , which inevitably introduces much noise .
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 .
In recent years , learning word vector representations has attracted much interest in Natural Language Processing . Word representations or embeddings learned using unsupervised methods help addressing the problem of traditional bag-of-word approaches which fail to capture contextual semantics .
Moreover , the fact that the two components are separately trained can limit its expressiveness . levy2014neural also considered the matrix factorization perspective , but in the context of learning word embeddings . In a general sense , Mixture of Softmaxes proposed in this work can be seen as a particular instantiation of the long - existing idea called Mixture of Experts ( MoE ) jacobs1991adaptive .
Due to their ability to capture syntactic and semantic information of words from large scale unlabeled text , pretrained word vectors Turian2010WordRA , word2vec , Pennington2014GloveGV are a standard component of most state - of - the - art NLP architectures , including for question answering liu2017stochastic , textual entailment Chen2017EnhancedLF and semantic role labeling He2017DeepSR . However , these approaches for learning word vectors only allow a single context - independent representation for each word . Previously proposed methods overcome some of the shortcomings of traditional word vectors by either enriching them with subword information [ e.g. , ]
Overall , this formulation is similar to the approach of Peters2017SemisupervisedST , with the exception that we share some weights between directions instead of using completely independent parameters . In the next section , we depart from previous work by introducing a new approach for learning word representations that are a linear combination of the biLM layers . subsection : ELMo
subsubsection : Vector arithmetic on face samples In the context of evaluating learned representations of words mikolov2013distributed demonstrated that simple arithmetic operations revealed rich linear structure in representation space . One canonical example demonstrated that the vector ( ”King ” ) - vector ( ”Man ” ) + vector ( ”Woman ” ) resulted in a vector whose nearest neighbor was the vector for Queen .
These methods are the inspiration for our Paragraph Vector methods . subsection : Learning Vector Representation of Words This section introduces the concept of distributed vector representation of words .
cluster tightness Next , we perform an analysis on representation learning of words ( word embeddings ) for the English ( PTB - WSJ ) and French ( UD ) experiments . We hypothesize that adversarial training ( AT ) helps to learn better word embeddings so that the POS tag prediction of a word can not be influenced by a small perturbation in the input embedding .
The essence of convolution layers is to convert text regions of a fixed size ( e.g. , “ am so happy ” with size 3 ) to feature vectors , as described later . In that sense , a word vector learning layer is a special ( and unusual ) case of convolution layer with region size one . Why is size one appropriate if bi - grams are more discriminating than uni - grams ?
Also , with our approach , a system is simpler with one fewer layer – no need to tune the dimensionality of word vectors or meta - parameters for word vector learning . KGB14 proposed complex modifications of CNN for sentence modeling .
Crowdsourcing Complex Associations among Words by Means of A Game Centroid-Means-Embedding: An Approach to Infusing Word Embeddings into Features for Text Classification Adapting lexical representation and OOV handling from written to spoken language with word embedding Learning Unsupervised Representations from Biomedical Text Chinese Grammatical Error Diagnosis Using Single Word Embedding Recurrent Neural Networks for Classifying Relations in Clinical Notes Word mover's distance for affect detection Adverse drug event and medication extraction in electronic health records via a cascading architecture with different sequence labeling models and word embeddings Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks Bidirectional Attention Flow for Machine Comprehension Direct Output Connection for a High-Rank Language Model DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings Text Understanding with the Attention Sum Reader Network Neural Ranking Models with Weak Supervision Grammar as a Foreign Language Russian Named Entities Recognition and Classification Using Distributed Word and Phrase Representations Supervised Paragraph Vector: Distributed Representations of Words, Documents and Class Labels A Supervised Method for Extractive Single Document Summarization based on Sentence Embeddings and Neural Networks A Study on the CBOW Model's Overfitting and Stability Distributional Semantics of Clinical Words Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling Distributed Representations of Sentences and Documents Breaking the Softmax Bottleneck: A High-Rank RNN Language Model A large annotated corpus for learning natural language inference Character-level Convolutional Networks for Text Classification Visual domain adaptation via transfer feature learning Semantic mapping of natural language input to database entries via convolutional neural networks Ensembles of Recurrent Networks for Classifying the Relationship of Fake News Titles Context Aware Machine Learning Spatial Temporal Topic Embedding: A Semantic Modeling Method for Short Text in Social Network Record Linking Protocol Based on Privacy-preserving in Vector Space Grammar as a Foreign Language Finding ReMO (Related Memory Object): A Simple Neural Architecture for Text based Reasoning Modelling Interaction of Sentence Pair with Coupled-LSTMs Hypernetwork Knowledge Graph Embeddings Neural Ranking Models with Weak Supervision FaceNet: A unified embedding for face recognition and clustering Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond Reasoning about Linguistic Regularities in Word Embeddings using Matrix Manifolds Improving Sentiment Analysis in Twitter Using Sentiment Specific Word Embeddings Centroid-based Text Summarization through Compositionality of Word Embeddings Extractive Summarization using Continuous Vector Space Models A Linear Dynamical System Model for Text Learning continuous-valued word representations for phrase break prediction Tag-based video retrieval by embedding semantic content in a continuous word space The microsoft 2016 conversational speech recognition system Evaluation of output embeddings for fine-grained image classification Effective LSTMs for Target-Dependent Sentiment Classification Different Contexts Lead to Different Word Embeddings A universal cross language software similarity detector for open source software categorization An Information Distillation Framework for Extractive Summarization Exploring the effect of semantic similarity for Phrase-based Machine Translation Word Embeddings for Multi-label Document Classification Improved Learning of Chinese Word Embeddings with Semantic Knowledge Exploiting Common Characters in Chinese and Japanese to Learn Cross-Lingual Word Embeddings via Matrix Factorization Open Information Extraction Systems and Downstream Applications Learning Higher-Level Features with Convolutional Restricted Boltzmann Machines for Sentiment Analysis Breaking the Softmax Bottleneck: A High-Rank RNN Language Model Deep contextualized word representations Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Distributed Representations of Sentences and Documents Robust Multilingual Part-of-Speech Tagging via Adversarial Training Effective Use of Word Order for Text Categorization with Convolutional Neural Networks
36e4e64040fe323988a1fc5b313ddb26ac1a170b a64d0ef30485a96970751099b3e20c1091b18eca 6731daae6b157a25154f0c97264aa2b67099f08e e3badb3ba44ba5485e538411a93498026244cb97 ab5d562bf29ef956929b55c48cc4c6fa1d056855 7d3c1ee1a09ab6df859df7d47d62245155d7b42a bd5afc03b9322bf861d9aa51135313cdae9bbb5e dd26c7a5907c9106182eeb0fe2144907a5855abb f7467051fc01356ed598f5757fdc4ecdeac70b89 dcdb981f11a0ddbb5e2e7392521965e3e71d0c39 73f54d963c9a79b4732aa368fc6077f3023a8bcc 8e476588f58711a522e97b193495f0ab55550bd1 02c8244f492cac00c4742268190e4724e050692e 7b690e253ed72feb191ce76afeacc0a6d1dc36d3 cf782669a8af54e30ea54aad6c5da6b31e8abdd1 6f5b93502ac67ae74f8fbf17d90dd5dc6264d8dc 03c9a175f4348ee7924ac1ed11c792e40f43dec5 107dcdb1e74b0aa5728e7c6a3f43b5a7d7fd92ce 7e1986a4198c7afff69ef33b92effd879cb81ced 2f35d23d68f596bbbb67277bb2fe3ea1bde26aad 17c1e93d7852acd0f8a17fc45ddae349c8ae513c 7610396ba26ad0021a51f71615ed0829318e3bbe 4d36e239f21ae8d1553e38a050e465347e73d8f8 9b510639c82fbbc8c7d883644d7d15f5901c8c73 9879f7f89de9eec7c4f8ff073fd8baf2fb7cc202 767c2e1a2301c0e51ef4614bb125612aaddc8161 1ba89d99a0e8075a4a69ae03e05835fb5a9ca925 3b02d6ff5643247db64faeba78591e431e95c1f0 11080821c58eae2bc49acf7060cc2c3a99d7f496 fd9ed0040c0160439e4556028a2a1d9f47402a21 df4b7954a9f661ad89061c3b63b006db2517e564 bf71e6b9d6d405fe69ab51ba3a08c6cb641c9acf 34db396db0c3b91d7a8d2827113a5287544fd0a4 5873b77b3d784d9e1ea7447ddcaaa5388f16a4d4 c4292546f4e36017d6fb598d1105e0982ded44d9
["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Medicine"] ["Computer Science"] ["Medicine","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"]
2019 2015 2015 2018 2016 2017 2017 2020 2017 2017 2018 2018 2018 2016 2017 2015 2016 2019 2019 2014 2019 2016 2014 2018 2015 2015 2017 2017 2019 2019 2018 2009 2015 2018 2016 2019 2017 2015 2016 2015 2019 2017 2014 2015 2014 2016 2017 2015 2016 2016 2020 2018 2015 2017 2015 2018 2016 2015 2018 2018 2016 2014 2018 2015
PAKDD (1) INTERSPEECH NLP-TEA@COLING ICLR ICLR EMNLP NAACL-HLT ACL ACL SIGIR NIPS SIMBig Web-KRM@CIKM ICSC COLING ICML ICLR EMNLP NIPS ICASSP SIGIR NIPS ArXiv EMNLP ICANN SIGIR 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) CoNLL IDAACS MultiLing@EACL ICML INTERSPEECH WACV 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) COLING COLING RANLP CCL Rep4NLP@ACL ECIR ICLR NAACL-HLT ICLR ICML NAACL-HLT HLT-NAACL
https://api.semanticscholar.org/36e4e64040fe323988a1fc5b313ddb26ac1a170b https://api.semanticscholar.org/a64d0ef30485a96970751099b3e20c1091b18eca https://api.semanticscholar.org/6731daae6b157a25154f0c97264aa2b67099f08e https://api.semanticscholar.org/e3badb3ba44ba5485e538411a93498026244cb97 https://api.semanticscholar.org/ab5d562bf29ef956929b55c48cc4c6fa1d056855 https://api.semanticscholar.org/7d3c1ee1a09ab6df859df7d47d62245155d7b42a https://api.semanticscholar.org/bd5afc03b9322bf861d9aa51135313cdae9bbb5e https://api.semanticscholar.org/dd26c7a5907c9106182eeb0fe2144907a5855abb https://www.semanticscholar.org/paper/189e6bb7523733c4e524214b9e6ae92d4ed50dac https://www.semanticscholar.org/paper/3a7b63b50c64f4ec3358477790e84cbd6be2a0b4 https://www.semanticscholar.org/paper/0be9ca65ad318ee3729928882ef2c403d4b6d24e https://www.semanticscholar.org/paper/3c1d781f2dab8da12e3cb0e4d7abfb440a340a09 https://www.semanticscholar.org/paper/cc5582606aef717855bb37c7aa35639da755cb13 https://www.semanticscholar.org/paper/f2e50e2ee4021f199877c8920f1f984481c723aa https://www.semanticscholar.org/paper/c7a59fc69aee480e53cd1f869bad4c83170d1328 https://www.semanticscholar.org/paper/47570e7f63e296f224a0e7f9a0d08b0de3cbaf40 https://api.semanticscholar.org/f7467051fc01356ed598f5757fdc4ecdeac70b89 https://api.semanticscholar.org/dcdb981f11a0ddbb5e2e7392521965e3e71d0c39 https://api.semanticscholar.org/73f54d963c9a79b4732aa368fc6077f3023a8bcc https://api.semanticscholar.org/8e476588f58711a522e97b193495f0ab55550bd1 https://api.semanticscholar.org/02c8244f492cac00c4742268190e4724e050692e https://www.semanticscholar.org/paper/786f95cada23d4639aa1a8b922cdb9fb9a9c03fa https://www.semanticscholar.org/paper/f527bcfb09f32e6a4a8afc0b37504941c1ba2cee https://www.semanticscholar.org/paper/ef9ddbc35676ce8ffc2a8067044473727839dbac https://www.semanticscholar.org/paper/f04df4e20a18358ea2f689b4c129781628ef7fc1 https://www.semanticscholar.org/paper/51a55df1f023571a7e07e338ee45a3e3d66ef73e https://api.semanticscholar.org/7b690e253ed72feb191ce76afeacc0a6d1dc36d3 https://api.semanticscholar.org/cf782669a8af54e30ea54aad6c5da6b31e8abdd1 https://api.semanticscholar.org/6f5b93502ac67ae74f8fbf17d90dd5dc6264d8dc https://api.semanticscholar.org/03c9a175f4348ee7924ac1ed11c792e40f43dec5 https://api.semanticscholar.org/107dcdb1e74b0aa5728e7c6a3f43b5a7d7fd92ce https://api.semanticscholar.org/7e1986a4198c7afff69ef33b92effd879cb81ced https://www.semanticscholar.org/paper/47570e7f63e296f224a0e7f9a0d08b0de3cbaf40 https://www.semanticscholar.org/paper/2af864bc38fd1116b4af32f16b44f5b3b156f5a3 https://www.semanticscholar.org/paper/10d89efc96beb45676149c8a3237a86a72a2116e https://www.semanticscholar.org/paper/7e9264aa7ac411cae5533029cbaeb87289639bce https://www.semanticscholar.org/paper/c7a59fc69aee480e53cd1f869bad4c83170d1328 https://www.semanticscholar.org/paper/5aa26299435bdf7db874ef1640a6c3b5a4a2c394 https://www.semanticscholar.org/paper/f37076f426023241f19cdc2fb0a0fd733a6fa7fa https://api.semanticscholar.org/2f35d23d68f596bbbb67277bb2fe3ea1bde26aad https://api.semanticscholar.org/17c1e93d7852acd0f8a17fc45ddae349c8ae513c https://api.semanticscholar.org/7610396ba26ad0021a51f71615ed0829318e3bbe https://api.semanticscholar.org/4d36e239f21ae8d1553e38a050e465347e73d8f8 https://api.semanticscholar.org/9b510639c82fbbc8c7d883644d7d15f5901c8c73 https://api.semanticscholar.org/9879f7f89de9eec7c4f8ff073fd8baf2fb7cc202 https://api.semanticscholar.org/767c2e1a2301c0e51ef4614bb125612aaddc8161 https://www.semanticscholar.org/paper/ac94ef90be9b0c3bf744d6744e47b38855f9a4c7 https://www.semanticscholar.org/paper/caccc069e658ea397c9faf673e74c959c734ff53 https://www.semanticscholar.org/paper/0b0dc14b8a8dcccbfc62a38355fff2f6a361e9d2 https://api.semanticscholar.org/1ba89d99a0e8075a4a69ae03e05835fb5a9ca925 https://api.semanticscholar.org/3b02d6ff5643247db64faeba78591e431e95c1f0 https://api.semanticscholar.org/11080821c58eae2bc49acf7060cc2c3a99d7f496 https://api.semanticscholar.org/fd9ed0040c0160439e4556028a2a1d9f47402a21 https://api.semanticscholar.org/df4b7954a9f661ad89061c3b63b006db2517e564 https://api.semanticscholar.org/bf71e6b9d6d405fe69ab51ba3a08c6cb641c9acf https://api.semanticscholar.org/34db396db0c3b91d7a8d2827113a5287544fd0a4 https://api.semanticscholar.org/5873b77b3d784d9e1ea7447ddcaaa5388f16a4d4 https://api.semanticscholar.org/c4292546f4e36017d6fb598d1105e0982ded44d9 https://www.semanticscholar.org/paper/ef9ddbc35676ce8ffc2a8067044473727839dbac https://www.semanticscholar.org/paper/3febb2bed8865945e7fddc99efd791887bb7e14f https://www.semanticscholar.org/paper/8388f1be26329fa45e5807e968a641ce170ea078 https://www.semanticscholar.org/paper/f527bcfb09f32e6a4a8afc0b37504941c1ba2cee https://www.semanticscholar.org/paper/1751668492bac56f0ae2b6410417515ab3215945 https://www.semanticscholar.org/paper/fbf417c83ae5b895fc645346e4efbf3a0aabeac9