In contrast to the well known back-off n-gram language models , the neural network approach attempts to overcome the data sparseness problem by performing the estimation in a continuous space . This type of language model was mostly used for tasks for which only a very limited amount of in-domain training data is available . In this paper we present new algorithms to train a neural network language model on very large text corpora . The goal of this research is to investigate how prosodic information can be employed to improve the language modelling component of a continuous speech recognition system . Because prosodic features are largely suprasegmental , operating over units larger than the phonetic segment , the language model is an appropriate place to incorporate such information . The prosodic features and standard language model features are combined under the maximum entropy framework , which provides an elegant solution to modelling information obtained from multiple , differing knowledge sources . In this paper , we present Pythia , an ontology matching system that utilizes and combines various matching techniques [ 1,3,4 ] to create mappings between two ontologies . Pythia is a combination of a string-based technique utilizing Apache Lucene ’s features , a language-based technique based on WordNet , and a graph-based technique that uses the structure of the ontology and the mappings produced by the two previous techniques . The system supports SKOS ontologies . Current principles of language usage in modern court are examined . The authors address the question of the need to improve it and provide some specific recommendations on effective application of language means in judicial proceedings . Behaviour of a New Mixed Self-Adaptive Multiple Access Protocol and Comparison with the CSMA/CD Protocol ( R.Z. Haliabalias , S.A. Koubias ) . Experience with Petri Net Based Verification of Communication Protocols Specified by Language Means ( M. Heiner ) . Automatic Verification of Estelle Specifications of Communication Protocols Using Petri Nets ( V. Dimitrov , A. Petkov ) . This work makes a number of contributions . Resin introduces the idea of a data flow assertion and demonstrates how to build them using three language-level mechanisms , policy objects , data tracking , and filter objects . We built prototype implementations of Resin in both the PHP and Python runtimes . document : Partially Shuffling the Training Data to Improve Language Models Although SGD requires shuffling the training data between epochs , currently none of the word - level language modeling systems do this . This paper proposes a state - of - the - art recurrent neural network ( RNN ) language model that combines probability distributions computed not only from a final RNN layer but also from middle layers . Our proposed method raises the expressive power of a language model based on the matrix factorization interpretation of language modeling introduced by DBLP : journals / corr / abs - 1711 - 03953 . The proposed method improves the current state - of - the - art language model and achieves the best score on the Penn Treebank and WikiText - 2 , which are the standard benchmark datasets . We obtain these representations using a fine-grained entity typing model , trained either on supervised ultra-fine entity typing data ( Choi et al. 2018 ) or distantly-supervised examples from Wikipedia . On entity probing tasks involving recognizing entity identity , our embeddings achieve competitive performance with ELMo and BERT without using any extra parameters . We also show that it is possible to reduce the size of our type set in a learning-based way for particular domains . One of the major challenges in this task is that , usually , place names are highly ambiguous ( e.g. , there are several locations on the surface of the Earth that share the same name ) . In this paper , we propose to address the task through a recurrent neural network architecture with multiple inputs and outputs , specifically leveraging pre-trained contextual embeddings ( ELMo ) and bi-directional Long Short-Term Memory ( LSTM ) units , both commonly used for textual data modeling . The proposed model was tested on two datasets that were previously used to evaluate toponym resolution systems , namely the War of the Rebellion and the Local-Global Lexicon corpora . In our comparison , we include such tasks as multiple choice question answering , next sentence prediction , and paraphrase identification . We employ FastText embeddings as a baseline and compare it to ELMo and BERT embeddings . We conduct two series of experiments , using both unsupervised ( i.e. , based on similarity measure only ) and supervised approaches for the tasks . In our comparison , we include such tasks as multiple choice question answering , next sentence prediction , and paraphrase identification . We employ FastText embeddings as a baseline and compare it to ELMo and BERT embeddings . We conduct two series of experiments , using both unsupervised ( i.e. , based on similarity measure only ) and supervised approaches for the tasks . In this paper , we propose an ELMo-ET-CRF model based approach to extract medical named entity from Chinese electronic medical records ( CEMRs ) . Firstly , a domain-specific ELMo model is fine-tuned on a common ELMo model with 4679 raw CEMRs . Then we use the encoder from Transformer ( ET ) as our model ’s encoder to alleviate the long context dependency problem , and the CRF is utilized as the decoder . In this paper we describe our system designed for the WASSA 2018 Implicit Emotion Shared Task ( IEST ) , which obtained 2$^{\text{nd}}$ place out of 26 teams with a test macro F1 score of $ 0.710$. The system is composed of a single pre-trained ELMo layer for encoding words , a Bidirectional Long-Short Memory Network BiLSTM for enriching word representations with context , a max-pooling operation for creating sentence representations from said word vectors , and a Dense Layer for projecting the sentence representations into label space . Our official submission was obtained by ensembling 6 of these models initialized with different random seeds . Both these problems are addressed by the new methodology introduced here . ResultsWe introduced a novel way to represent protein sequences as continuous vectors ( embeddings ) by using the language model ELMo taken from natural language processing . By modeling protein sequences , ELMo effectively captured the biophysical properties of the language of life from unlabeled big data ( UniRef50 ) . To this end , we compare lattice LSTM structure with its sequential LSTM counterpart , namely ELMo . Results show that our lattice version of ELMo gives better language modeling performances . On Chinese POS-tagging , chunking and syntactic parsing tasks , the resulting contextualized Chinese embeddings also give better performance than ELMo trained on the same data . Our primary results support the use language modeling , especially when combined with pretraining on additional labeled-data tasks . However , our results are mixed across pretraining tasks and show some concerning trends : In ELMo 's pretrain-then-freeze paradigm , random baselines are worryingly strong and results vary strikingly across target tasks . In addition , fine-tuning BERT on an intermediate task often negatively impacts downstream transfer . In this case , it is necessary to consider that irony may be expressed in a different way according to the Spanish variant , which makes this task more complex to tackle it . Taking into account that irony detection is a very important task in many applications , we proposed a system based on a distributed representation of the texts , using the ELMo approach . We did not use any handcrafted features , lexicons or external datasets as prior information . Based on the above background , we propose a new baseline for unsupervised keyphrase extraction based on pre-trained language model called SIFRank . SIFRank combines sentence embedding model SIF and autoregressive pre-trained language model ELMo , and it has the best performance in keyphrase extraction for short documents . We speed up SIFRank while maintaining its accuracy by document segmentation and contextual word embeddings alignment . We follow the bi - directional LSTM - CNN architecture , where convolutional neural networks ( CNNs ) encode characters inside a word into character - level representation then concatenated with its word - level into context - independent representation . To further enhance the word representation , we leverage an external representation from pretrained ELMo ( Embeddings from Language Models ) layers according to ELMo ELMo . Eventually , the resulting token representation is concatenated as Table [ reference ] compares F scores of he2018jointly he2018jointly and our model without ELMo representations . We observe that effect of ELMo is somewhat surprising , where removal of the ELMo dramatically declines the performance by 3.3 - 3.5 F on CoNLL - 2005 WSJ . However , our model gives quite stable performance for dependency SRL regardless of whether ELMo is concatenated or not . First , this layer projects each of the one - hot vectors ( of size ) into a - dimensional continuous vector space with a pre - trained weight matrix such as GloVe PenningtonSM14 . Next , it uses contextualized word representations , ELMo PetersNIGCLZ18 , which is a character - level two - layer bidirectional language model pre - trained on a large - scale corpus . ELMo representations allow our model to use morphological clues to form robust representations for out - of - vocabulary words unseen in training . To facilitate these residual connections , all sub - layers produce outputs of dimension . Note that our model does not use any position embeddings because ELMo gives the positional information of the words in each sequence . subsubsection : Dual Attention Layer The numbers of shared encoding blocks , modeling blocks for question , modeling blocks for passages , and decoder blocks were 3 , 2 , 5 , and 8 , respectively . We used the pre - trained uncased 300 - dimensional GloVe PenningtonSM14 and the original 512 - dimensional ELMo PetersNIGCLZ18 . We used the spaCy tokenizer , and all words were lowercased except the input for ELMo . Compared against the Neural - CRF , a very strong model in itself , our model significantly improves performance , showing the positive impact of our technique for exploiting manually - engineered features . Although Peters + :2018 report a higher F - score using their ELMo embedding technique , our approach here is orthogonal , and accordingly we would expect a performance increase if we were to incorporate their ELMo representations into our model . paragraph : Ablation Study Then , the model forms a context - sensitive representation , typically using either bidirectional RNNs , CNNs , or feed forward networks . To add ELMo to the supervised model , we first freeze the weights of the biLM and then concatenate the ELMo vector with and pass the ELMo enhanced representation into the task RNN . For some tasks ( e.g. , SNLI , SQuAD ) , we observe further improvements by also including ELMo at the output of the task RNN by introducing another set of output specific linear weights and replacing with . We also compare our language models to ELMo , a contextualized word representation based on a deep bidirectional language model , trained on large text corpus . We use a publicly available pretrained ELMo model from the authors . During finetuning , text is embedded with ELMo before being passed into a decoder . In these experiments we also compare to tan2018deep , the previous state - of - the art SRL model using gold predicates and standard embeddings . We demonstrate that our models benefit from injecting state - of - the - art predicted parses at test time ( + D & M ) by fixing the attention to parses predicted by dozat2017deep , the winner of the 2017 CoNLL shared task zeman2017conll which we re - train using ELMo embeddings . In all cases , using these parses at test time improves performance . The notion of topic has always been central to the IR research and also been very elusive from many researchers ' attempts to give it a clear definition . The language modeling approach 's solution is to represent it as a probabilistic language model , where a topic a user of the IR system assumes when searching for information of interest is captured as an ideal document he conceptualizes in his mind , represented by its corresponding language model . This thesis further develops the language modeling approach in general , and in particular the concept of an ideal document . Encyclopedic texts show less agreement ( and by implication are harder to annotate ) than narrative texts . The release of this corpus is intended to encourage research into the use of crowds for text annotation and the development of more advanced , probabilistic language models , in particular for anaphoric coreference . These representations can be considered as stylistic fingerprints of the authors . We are exploring the performance of the two main flavours of distributed representations , namely embeddings produced by Neural Probabilistic Language models ( such as word2vec ) and matrix factorization ( such as GloVe ) . Recent work has shown the effectiveness of neural probabilistic language models(NPLMs ) in statistical machine translation(SMT ) through both reranking the n-best outputs and direct decoding . However there are still some issues remained for application of NPLMs . This study presents a novel approach to spoken document retrieval based on neural probabilistic language modeling for semantic inference . The neural network based language model is applied to estimate word association in a continuous space . ( 2 ) Performing a language model-based comparison of genomic language variations using biovectors : the purpose is to quantify the distances between syntactic and semantic features of two genomic language variations , with various applications in comparative genomics . The training model of biovectors is analogous to neural probabilistic language modeling . This makes the network of n-grams in this space an indirect representation of the underlying language model . The system accepts continuous speech input and handles multiple speakers without explicit speaker enrollment . The MIT spoken language system combines SUMMIT , a segment-based speech recognition system , and TINA , a probabilistic natural language system , to achieve speech understanding . The system engages in interactive dialogue with the user , providing output in the form of tabular displays , as well as spoken and written output . In the second edition , every chapter has been extensively rewritten . Significant new material has been introduced to cover areas such as constraint satisfaction , fast propositional inference , planning graphs , internet agents , exact probabilistic inference , Markov Chain Monte Carlo techniques , Kalman filters , ensemble learning methods , statistical learning , probabilistic natural language models , probabilistic robotics , and ethical aspects of AI . The book is supported by a suite of online resources including source code , figures , lecture slides , a directory of over 800 links to " AI on the Web , " and an online discussion group . This method combines both visual attention model and convolutional neural network to increase the accuracy of classifying objects , especially in biology . S. Bi et al. propose a force-directed method using a fracture mechanic model to learn word embedding in the article " Fracture Mechanics Method for Word Embedding Generation of Neural Probabilistic Linguistic Model . " The method aims to improve the accuracy , recall , and text visualization of traditional language models , and a word embedding , a semantic vector representation , could be generated via the neural linguistic model . The rate of success of a NLP probabilistic language model is determined by three factors : entropy or per-plexity ; the quality of the training corpus ; and the accuracy of the probabilities \learned " from a training corpus of a particular size . We discuss the third factor in terms of formulae for estimating the error to be expected per single probability . Training a large probabilistic neural network language model , with typical high-dimensional output is excessively time-consuming , which is one of the main reasons that more simplified models such as n-gram is often more popular despite the inferior performance . In this paper a Chinese neural probabilistic language model is trained using the Fudan Chinese Language Corpus . It also builds upon older work such as class - based language models for dialog systems [ reference ] . section : A Neural Language Model Approach A feed - forward neural language model ( NLM ) estimates P ( w t |c t ) with a parametric function φ θ ( Equation 1 ) , where θ refers to all learnable parameters of the network . subsection : Neural Networks for Machine Translation Since introduced a neural probabilistic language model which uses a neural network to model the conditional probability of a word given a fixed number of the preceding words , neural networks have widely been used in machine translation . However , the role of neural networks has been largely limited to simply providing a single feature to an existing statistical machine translation system or to re - rank a list of candidate translations provided by an existing system .
In contrast to the well known back-off n-gram language models , the neural network approach attempts to overcome the data sparseness problem by performing the estimation in a continuous space . This type of language model was mostly used for tasks for which only a very limited amount of in-domain training data is available . In this paper we present new algorithms to train a neural network language model on very large text corpora .
The goal of this research is to investigate how prosodic information can be employed to improve the language modelling component of a continuous speech recognition system . Because prosodic features are largely suprasegmental , operating over units larger than the phonetic segment , the language model is an appropriate place to incorporate such information . The prosodic features and standard language model features are combined under the maximum entropy framework , which provides an elegant solution to modelling information obtained from multiple , differing knowledge sources .
In this paper , we present Pythia , an ontology matching system that utilizes and combines various matching techniques [ 1,3,4 ] to create mappings between two ontologies . Pythia is a combination of a string-based technique utilizing Apache Lucene ’s features , a language-based technique based on WordNet , and a graph-based technique that uses the structure of the ontology and the mappings produced by the two previous techniques . The system supports SKOS ontologies .
Current principles of language usage in modern court are examined . The authors address the question of the need to improve it and provide some specific recommendations on effective application of language means in judicial proceedings .
Behaviour of a New Mixed Self-Adaptive Multiple Access Protocol and Comparison with the CSMA/CD Protocol ( R.Z. Haliabalias , S.A. Koubias ) . Experience with Petri Net Based Verification of Communication Protocols Specified by Language Means ( M. Heiner ) . Automatic Verification of Estelle Specifications of Communication Protocols Using Petri Nets ( V. Dimitrov , A. Petkov ) .
This work makes a number of contributions . Resin introduces the idea of a data flow assertion and demonstrates how to build them using three language-level mechanisms , policy objects , data tracking , and filter objects . We built prototype implementations of Resin in both the PHP and Python runtimes .
document : Partially Shuffling the Training Data to Improve Language Models Although SGD requires shuffling the training data between epochs , currently none of the word - level language modeling systems do this .
This paper proposes a state - of - the - art recurrent neural network ( RNN ) language model that combines probability distributions computed not only from a final RNN layer but also from middle layers . Our proposed method raises the expressive power of a language model based on the matrix factorization interpretation of language modeling introduced by DBLP : journals / corr / abs - 1711 - 03953 . The proposed method improves the current state - of - the - art language model and achieves the best score on the Penn Treebank and WikiText - 2 , which are the standard benchmark datasets .
We obtain these representations using a fine-grained entity typing model , trained either on supervised ultra-fine entity typing data ( Choi et al. 2018 ) or distantly-supervised examples from Wikipedia . On entity probing tasks involving recognizing entity identity , our embeddings achieve competitive performance with ELMo and BERT without using any extra parameters . We also show that it is possible to reduce the size of our type set in a learning-based way for particular domains .
One of the major challenges in this task is that , usually , place names are highly ambiguous ( e.g. , there are several locations on the surface of the Earth that share the same name ) . In this paper , we propose to address the task through a recurrent neural network architecture with multiple inputs and outputs , specifically leveraging pre-trained contextual embeddings ( ELMo ) and bi-directional Long Short-Term Memory ( LSTM ) units , both commonly used for textual data modeling . The proposed model was tested on two datasets that were previously used to evaluate toponym resolution systems , namely the War of the Rebellion and the Local-Global Lexicon corpora .
In our comparison , we include such tasks as multiple choice question answering , next sentence prediction , and paraphrase identification . We employ FastText embeddings as a baseline and compare it to ELMo and BERT embeddings . We conduct two series of experiments , using both unsupervised ( i.e. , based on similarity measure only ) and supervised approaches for the tasks .
In our comparison , we include such tasks as multiple choice question answering , next sentence prediction , and paraphrase identification . We employ FastText embeddings as a baseline and compare it to ELMo and BERT embeddings . We conduct two series of experiments , using both unsupervised ( i.e. , based on similarity measure only ) and supervised approaches for the tasks .
In this paper , we propose an ELMo-ET-CRF model based approach to extract medical named entity from Chinese electronic medical records ( CEMRs ) . Firstly , a domain-specific ELMo model is fine-tuned on a common ELMo model with 4679 raw CEMRs . Then we use the encoder from Transformer ( ET ) as our model ’s encoder to alleviate the long context dependency problem , and the CRF is utilized as the decoder .
In this paper we describe our system designed for the WASSA 2018 Implicit Emotion Shared Task ( IEST ) , which obtained 2$^{\text{nd}}$ place out of 26 teams with a test macro F1 score of $ 0.710$. The system is composed of a single pre-trained ELMo layer for encoding words , a Bidirectional Long-Short Memory Network BiLSTM for enriching word representations with context , a max-pooling operation for creating sentence representations from said word vectors , and a Dense Layer for projecting the sentence representations into label space . Our official submission was obtained by ensembling 6 of these models initialized with different random seeds .
Both these problems are addressed by the new methodology introduced here . ResultsWe introduced a novel way to represent protein sequences as continuous vectors ( embeddings ) by using the language model ELMo taken from natural language processing . By modeling protein sequences , ELMo effectively captured the biophysical properties of the language of life from unlabeled big data ( UniRef50 ) .
To this end , we compare lattice LSTM structure with its sequential LSTM counterpart , namely ELMo . Results show that our lattice version of ELMo gives better language modeling performances . On Chinese POS-tagging , chunking and syntactic parsing tasks , the resulting contextualized Chinese embeddings also give better performance than ELMo trained on the same data .
Our primary results support the use language modeling , especially when combined with pretraining on additional labeled-data tasks . However , our results are mixed across pretraining tasks and show some concerning trends : In ELMo 's pretrain-then-freeze paradigm , random baselines are worryingly strong and results vary strikingly across target tasks . In addition , fine-tuning BERT on an intermediate task often negatively impacts downstream transfer .
In this case , it is necessary to consider that irony may be expressed in a different way according to the Spanish variant , which makes this task more complex to tackle it . Taking into account that irony detection is a very important task in many applications , we proposed a system based on a distributed representation of the texts , using the ELMo approach . We did not use any handcrafted features , lexicons or external datasets as prior information .
Based on the above background , we propose a new baseline for unsupervised keyphrase extraction based on pre-trained language model called SIFRank . SIFRank combines sentence embedding model SIF and autoregressive pre-trained language model ELMo , and it has the best performance in keyphrase extraction for short documents . We speed up SIFRank while maintaining its accuracy by document segmentation and contextual word embeddings alignment .
We follow the bi - directional LSTM - CNN architecture , where convolutional neural networks ( CNNs ) encode characters inside a word into character - level representation then concatenated with its word - level into context - independent representation . To further enhance the word representation , we leverage an external representation from pretrained ELMo ( Embeddings from Language Models ) layers according to ELMo ELMo . Eventually , the resulting token representation is concatenated as
Table [ reference ] compares F scores of he2018jointly he2018jointly and our model without ELMo representations . We observe that effect of ELMo is somewhat surprising , where removal of the ELMo dramatically declines the performance by 3.3 - 3.5 F on CoNLL - 2005 WSJ . However , our model gives quite stable performance for dependency SRL regardless of whether ELMo is concatenated or not .
First , this layer projects each of the one - hot vectors ( of size ) into a - dimensional continuous vector space with a pre - trained weight matrix such as GloVe PenningtonSM14 . Next , it uses contextualized word representations , ELMo PetersNIGCLZ18 , which is a character - level two - layer bidirectional language model pre - trained on a large - scale corpus . ELMo representations allow our model to use morphological clues to form robust representations for out - of - vocabulary words unseen in training .
To facilitate these residual connections , all sub - layers produce outputs of dimension . Note that our model does not use any position embeddings because ELMo gives the positional information of the words in each sequence . subsubsection : Dual Attention Layer
The numbers of shared encoding blocks , modeling blocks for question , modeling blocks for passages , and decoder blocks were 3 , 2 , 5 , and 8 , respectively . We used the pre - trained uncased 300 - dimensional GloVe PenningtonSM14 and the original 512 - dimensional ELMo PetersNIGCLZ18 . We used the spaCy tokenizer , and all words were lowercased except the input for ELMo .
Compared against the Neural - CRF , a very strong model in itself , our model significantly improves performance , showing the positive impact of our technique for exploiting manually - engineered features . Although Peters + :2018 report a higher F - score using their ELMo embedding technique , our approach here is orthogonal , and accordingly we would expect a performance increase if we were to incorporate their ELMo representations into our model . paragraph : Ablation Study
Then , the model forms a context - sensitive representation , typically using either bidirectional RNNs , CNNs , or feed forward networks . To add ELMo to the supervised model , we first freeze the weights of the biLM and then concatenate the ELMo vector with and pass the ELMo enhanced representation into the task RNN . For some tasks ( e.g. , SNLI , SQuAD ) , we observe further improvements by also including ELMo at the output of the task RNN by introducing another set of output specific linear weights and replacing with .
We also compare our language models to ELMo , a contextualized word representation based on a deep bidirectional language model , trained on large text corpus . We use a publicly available pretrained ELMo model from the authors . During finetuning , text is embedded with ELMo before being passed into a decoder .
In these experiments we also compare to tan2018deep , the previous state - of - the art SRL model using gold predicates and standard embeddings . We demonstrate that our models benefit from injecting state - of - the - art predicted parses at test time ( + D & M ) by fixing the attention to parses predicted by dozat2017deep , the winner of the 2017 CoNLL shared task zeman2017conll which we re - train using ELMo embeddings . In all cases , using these parses at test time improves performance .
The notion of topic has always been central to the IR research and also been very elusive from many researchers ' attempts to give it a clear definition . The language modeling approach 's solution is to represent it as a probabilistic language model , where a topic a user of the IR system assumes when searching for information of interest is captured as an ideal document he conceptualizes in his mind , represented by its corresponding language model . This thesis further develops the language modeling approach in general , and in particular the concept of an ideal document .
Encyclopedic texts show less agreement ( and by implication are harder to annotate ) than narrative texts . The release of this corpus is intended to encourage research into the use of crowds for text annotation and the development of more advanced , probabilistic language models , in particular for anaphoric coreference .
These representations can be considered as stylistic fingerprints of the authors . We are exploring the performance of the two main flavours of distributed representations , namely embeddings produced by Neural Probabilistic Language models ( such as word2vec ) and matrix factorization ( such as GloVe ) .
Recent work has shown the effectiveness of neural probabilistic language models(NPLMs ) in statistical machine translation(SMT ) through both reranking the n-best outputs and direct decoding . However there are still some issues remained for application of NPLMs .
This study presents a novel approach to spoken document retrieval based on neural probabilistic language modeling for semantic inference . The neural network based language model is applied to estimate word association in a continuous space .
( 2 ) Performing a language model-based comparison of genomic language variations using biovectors : the purpose is to quantify the distances between syntactic and semantic features of two genomic language variations , with various applications in comparative genomics . The training model of biovectors is analogous to neural probabilistic language modeling . This makes the network of n-grams in this space an indirect representation of the underlying language model .
The system accepts continuous speech input and handles multiple speakers without explicit speaker enrollment . The MIT spoken language system combines SUMMIT , a segment-based speech recognition system , and TINA , a probabilistic natural language system , to achieve speech understanding . The system engages in interactive dialogue with the user , providing output in the form of tabular displays , as well as spoken and written output .
In the second edition , every chapter has been extensively rewritten . Significant new material has been introduced to cover areas such as constraint satisfaction , fast propositional inference , planning graphs , internet agents , exact probabilistic inference , Markov Chain Monte Carlo techniques , Kalman filters , ensemble learning methods , statistical learning , probabilistic natural language models , probabilistic robotics , and ethical aspects of AI . The book is supported by a suite of online resources including source code , figures , lecture slides , a directory of over 800 links to " AI on the Web , " and an online discussion group .
This method combines both visual attention model and convolutional neural network to increase the accuracy of classifying objects , especially in biology . S. Bi et al. propose a force-directed method using a fracture mechanic model to learn word embedding in the article " Fracture Mechanics Method for Word Embedding Generation of Neural Probabilistic Linguistic Model . " The method aims to improve the accuracy , recall , and text visualization of traditional language models , and a word embedding , a semantic vector representation , could be generated via the neural linguistic model .
The rate of success of a NLP probabilistic language model is determined by three factors : entropy or per-plexity ; the quality of the training corpus ; and the accuracy of the probabilities \learned " from a training corpus of a particular size . We discuss the third factor in terms of formulae for estimating the error to be expected per single probability .
Training a large probabilistic neural network language model , with typical high-dimensional output is excessively time-consuming , which is one of the main reasons that more simplified models such as n-gram is often more popular despite the inferior performance . In this paper a Chinese neural probabilistic language model is trained using the Fudan Chinese Language Corpus .
It also builds upon older work such as class - based language models for dialog systems [ reference ] . section : A Neural Language Model Approach A feed - forward neural language model ( NLM ) estimates P ( w t |c t ) with a parametric function φ θ ( Equation 1 ) , where θ refers to all learnable parameters of the network .
subsection : Neural Networks for Machine Translation Since introduced a neural probabilistic language model which uses a neural network to model the conditional probability of a word given a fixed number of the preceding words , neural networks have widely been used in machine translation . However , the role of neural networks has been largely limited to simply providing a single feature to an existing statistical machine translation system or to re - rank a list of candidate translations provided by an existing system .
Training Neural Network Language Models on Very Large Corpora Prosodic features for a maximum entropy language model Enabling semantic search for EO products: an ontology matching approach Язык судебной экспертизы: тенденции формирования и развития Network information processing systems : proceedings of the IFIP TC6/TC8 Open Symposium on Network Information Processing Systems, Sofia, Bulgaria, 9-13 May, 1988 Improving web site security with data flow management Partially Shuffling the Training Data to Improve Language Models Direct Output Connection for a High-Rank Language Model Interpretable Entity Representations through Large-Scale Typing Using Recurrent Neural Networks for Toponym Resolution in Text Evaluation of Sentence Embedding Models for Natural Language Understanding Problems in Russian Sentence Embeddings for Russian NLU A self-attention based neural architecture for Chinese medical named entity recognition IIIDYT at IEST 2018: Implicit Emotion Classification With Deep Contextualized Word Representations Modeling aspects of the language of life through transfer-learning protein sequences Lattice LSTM for Chinese Sentence Representation Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling A Contextualized Word Representation Approach for Irony Detection SIFRank: A New Baseline for Unsupervised Keyphrase Extraction Based on Pre-Trained Language Model Dependency or Span, End-to-End Uniform Semantic Role Labeling Multi-style Generative Reading Comprehension Evaluating the Utility of Hand-crafted Features in Sequence Labelling Deep contextualized word representations Practical Text Classification With Large Pre-Trained Language Models Linguistically-Informed Self-Attention for Semantic Role Labeling A document prototyping approach to information retrieval Phrase Detectives Corpus 1.0 Crowdsourced Anaphoric Coreference Word embeddings for idiolect identification An Investigation on Statistical Machine Translation with Neural Language Models Semantic inference based on neural probabilistic language modeling for speech indexing Deep Genomics and Proteomics: Language Model-Based Embedding of Biological Sequences and Their Applications in Bioinformatics Spoken Language Recognition and Understanding Artificial intelligence - a modern approach: the intelligent agent book Smart Data: Where the Big Data Meets the Semantics Intrinsic Error Estimation for Corpus-Trained Probabilistic Language Models Parallel Randomized Block Coordinate Descent for Neural Probabilistic Language Model with High-Dimensional Output Targets Neural Text Generation from Structured Data with Application to the Biography Domain Neural Machine Translation by Jointly Learning to Align and Translate
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