Syntactic parsing is done using an open source dependency parser called MaltParser ( Nivre et al. 2007 ) . Whereas to accomplish semantic parsing , we will construct a biological proposition bank ( BioPropBank ) and a corpus annotated with semantic roles . Then we will employ supervised methods to automatic label the semantic roles of a Semantic parsing maps a sentence in natural language into a structured meaning representation . Previous studies show that semantic parsing with synchronous context-free grammars ( SCFGs ) achieves favorable performance over most other alternatives . Motivated by the observation that the performance of semantic parsing with SCFGs is closely tied to the translation rules , this article explores to extend translation rules with high quality and increased coverage in three ways . We present the technique of shallow semantic parsing , the process of assigning a simple WHO did WHAT to WHOM , etc . , structure to sentences in text , as a useful tool in achieving this goal . We formulate the semantic parsing problem as a classification problem using support vector machines . Using a hand-labeled training set and a set of features drawn from earlier work together with some feature enhancements , we demonstrate a system that performs better than all other published results on shallow semantic parsing . In the last two years , the models used for semantic parsing have changed dramatically , with the introduction of neural methods that allow us to rethink many of the previous assumptions underlying semantic parsing . Traditionally , the executable formalisms and models used in semantic parsing research have been heavily reliant on notions of formal semantics in linguistics , such as λ-calculus generated by a CCG parser . However , recent work with neural encoder-decoder semantic parsers allow for more accessible formalisms , such as standard programming languages , and NMT-style models that are much more approachable to a broader NLP audience . We show how data mining techniques that are typically applied to text should be modified to enable an analyst to do effective semantic data mining on a large collection of short speech utterances . For the purposes of this paper , we examine semantic data mining in the context of semantic parsing and analysis in a specific situation involving the solution of a business problem that is known to the analyst . We are not attempting a generic semantic analysis of a set of speech . We address this by introducing a robust system based on the lambda calculus for deriving neo-Davidsonian logical forms from dependency trees . These logical forms are then used for semantic parsing of natural language to Freebase . Experiments on the Free917 and Web-Questions datasets show that our representation is superior to the original dependency trees and that it outperforms a CCG-based representation on this task . The work can be divided into two segments : global / camera motion estimation and object motion analysis . The former involves optical flow analysis and semantic meaning parsing , and the latter involves object detection and tracking . Although each of these topics has been studied extensively in the literature , a thorough system combining all of them without human intervention , especially under a real time application scenario , is still worthy of further investigation . In the past , the search engine uses limited and shallow NLP technologies because NLP technology is not as mature as people have expected . In recent years , we have witnessed that NLP has made huge advances in various tasks such as semantic parser , question-answering , machine translation , machine reading comprehension and text generation . I think that now it is the time to consider applying these new technologies to a search engine to further improve the intelligence and naturalness of the search process . Semantic parsing aims at mapping natural language utterances into structured meaning representations . In this work , we propose a structure-aware neural architecture which decomposes the semantic parsing process into two stages . Given an input utterance , we first generate a rough sketch of its meaning , where low-level information ( such as variable names and arguments ) is glossed over . Correctly interpreting human instructions is the first step to human-robot interaction . Previous approaches to semantically parsing the instructions relied on large numbers of training examples with annotation to widely cover all words in a domain . Annotating large enough instructions with semantic forms needs exhaustive engineering efforts . This paper describes a number of freely available tools for implementing and running spoken language understanding systems . Unlike other free tools ( e.g. the CSLU toolkit ) , the main emphasis is on spoken language understanding ( syntactic/semantic parsing , generation of language models for recognition etc . ) . The suite supports ( reads and/or writes ) a number of grammar formats defined for speech recognisers like Entropic ’s GrapHvite ( HTK standard lattice ) as well as common unification grammar formats used in NLP : The American PATRII and the European EUROTRA ( Augmented Phrase Structure Grammar ) format . Figure [ reference ] shows example annotations . Great progress has been made in syntactic parsing . Most traditional SRL methods rely heavily on syntactic features . tackstrom2015efficient showed that constraints could be enforced more efficiently using a clever dynamic program for exact inference . sutton2005joint modeled syntactic parsing and SRL jointly , and lewis2015joint jointly modeled SRL and CCG parsing . collobert2011natural were among the first to use a neural network model for SRL , a CNN over word embeddings which failed to out - perform non - neural models . section : Introduction and Background Natural language inference ( NLI ) or recognizing textual entailment ( RTE ) is a fundamental semantic task in the field of natural language processing . The problem is to determine whether a given hypothesis sentence can be logically inferred from a given premise sentence . Multi - NLI Shared Task , and it also surpasses the state - of - the - art encoders for the SNLI dataset . In future work , we are also evaluating the effectiveness of shortcut - stacked sentence encoders on several other semantic tasks . section : Addendum : Shortcut vs. Residual Therefore , the results of the examples ( a ) and ( b ) suggest that it would be needed to capture various types of root nodes , and using sentence - dependent root representations would lead to better results in future work . appendix : Analysis on Semantic Tasks We inspected the development set results on the semantic tasks using the “ JMT ” setting . However , the scale and the difficulty for machines to interpret natural language still makes this task a challenging problem . The state - of - the - art techniques in open QA can be classified into two main classes , namely , information retrieval based and semantic parsing based . Information retrieval systems first retrieve a broad set of candidate answers by querying the search API of KBs with a transformation of the question into a valid query and then use fine - grained detection heuristics to identify the exact answer [ reference ][ reference ][ reference ] . section : Related Work Our method is motivated by the central role played by alignment in machine translation and previous approaches to sentence similarity modeling , natural language inference , and semantic parsing . The neural counterpart to alignment , attention , which is a key part of our approach , was originally proposed and has been predominantly used in conjunction with LSTMs and to a lesser extent with CNNs . On the other hand , even if the raw inputs are originally expressed in a sequence form , it can still benefit from the enhanced inputs with additional information ( to formulate graph inputs ) . For example , for semantic parsing tasks ( text - to - AMR or text - to - SQL ) , they have been shown better performance by augmenting the original sentence sequences with other structural information such as dependency parsing trees pust2015parsing . Intuitively , the ideal solution for graph - to - sequence tasks is to build a more powerful encoder which is able to learn the input representation regardless of its inherent structure . Given the word embeddings , this task is solved by finding the word whose embedding is closest to the vector in terms of cosine proximity , i.e. , . Two categories of word analogy are used in this task : semantic and syntactic . Significantly outperforms GF at the : Segmenting the high - resolution input with classical frameworks like FCN directly is time consuming . To overcome this shortcoming , we get semantic extraction using low - resolution input as shown in top branch of Fig . [ reference ] . This would make it possible for the model to learn to parse using guidance from the semantic representation objective , which currently is blocked from influencing the key parsing parameters by our use of hard shift / reduce decisions . This change would allow the model to learn to produce parses that are , in aggregate , better suited to supporting semantic interpretation than those supplied in the training data . subsubsection : Acknowledgments We have developed a heuristic method for unsupervised parsing of unrestricted text . Our method relies on detecting certain patterns of part-of-speech tag sequences of words in sentences . In the last 12 years , there has been a big progress in the field of unsupervised dependency parsing . Different approaches however sometimes differ in motivation and definition of the problem . We also describe the multiplicative combination of this dependency model with a model of linear constituency . The product model outperforms both components on their respective evaluation metrics , giving the best published figures for unsupervised dependency parsing and unsupervised constituency parsing . We also demonstrate that the combined model works and is robust cross-linguistically , being able to exploit either attachment or distributional regularities that are salient in the data . We propose a non-parametric Bayesian model for unsupervised semantic parsing . Following Poon and Domingos ( 2009 ) , we consider a semantic parsing setting where the goal is to ( 1 ) decompose the syntactic dependency tree of a sentence into fragments , ( 2 ) assign each of these fragments to a cluster of semantically equivalent syntactic structures , and ( 3 ) predict predicate-argument relations between the fragments . structured prediction ) has shown that joint inference can not only substantially improve predictive accuracy , but also enable effective learning with little or no labeled information . Markov logic is the unifying framework for statistical relational learning , and has spawned a series of successful NLP applications , ranging from information extraction to unsupervised semantic parsing . In this tutorial , I will introduce Markov logic to the NLP community and survey existing NLP applications . We consider a new subproblem of unsupervised parsing from raw text , unsupervised partial parsing --- the unsupervised version of text chunking . We show that addressing this task directly , using probabilistic finite-state methods , produces better results than relying on the local predictions of a current best unsu-pervised parser , Seginer 's ( 2007 ) CCL . We present an approach to unsupervised partial parsing : the identification of low-level constituents ( which we dub clumps ) in unannotated text . We begin by showing that CCLParser ( Seginer 2007 ) , an unsupervised parsing model , is particularly adept at identifying clumps , and that , surprisingly , building a simple right-branching structure above its clumps actually outperforms the full parser itself , indicating that much of the CCLParser 's performance comes from good local predictions . Given a training set T and a test set S , the goal of our algorithm is to identify subset pairs Ti , Si of T and S such that when the unsupervised parser is trained on a training subset Ti its results on its paired test subset Si are better than when it is trained on the entire training set T. A successful application of zoomed learning improves overall performance on the full test set S. We study our algorithm 's effect on the leading algorithm for the task of fully unsupervised parsing ( Seginer , 2007 ) in three different English domains , WSJ , BROWN and GENIA , and show that it improves the parser F-score by up to 4.47 % . Grammar induction refers to the process of learning grammars and languages from data ; this finds a variety of applications in syntactic pattern recognition , the modeling of natural language acquisition , data mining and machine translation . This special topic contains several papers presenting some of recent developments in the area of grammar induction and language learning , as applied to various problems in Natural Language Processing , including supervised and unsupervised parsing and statistical machine translation . We propose a spectral approach for unsupervised constituent parsing that comes with theoretical guarantees on latent structure recovery . Our approach is grammarless ‐ we directly learn the bracketing structure of a given sentence without using a grammar model . Also , as network content is both copious and dynamic , with topics and domains changing everyday , the approaches here must be primarily unsupervised . We propose a framework for unsupervised semantic analysis and organization of spoken documents and for this purpose propose two measures derived from latent topic analysis : latent topic significance and latent topic entropy . We show that these can be integrated into an application system , with which the user can more easily navigate archives of spoken documents . Finally , in order to evaluate the learned semantic separators , we use them in unsupervised Chinese text parsing . The experiments on a manually labeled test set show that the proposed method outperforms previous methods of unsupervised text parsing . Many approaches have been proposed to tackle the problem of Abstract Meaning Representation ( AMR ) parsing , help solving various natural language processing issues recently . In our paper , we provide an overview of different methods in AMR parsing and their performances when analyzing legal documents . We conduct experiments of different AMR parsers on our annotated dataset extracted from the English version of Japanese Civil Code . We use our annotations to test a baseline AMR-to-syntax aligner , finding that this task is more difficult than AMRto-string alignment ; and to pinpoint errors in an AMR parser . We make our data and code freely available for further research on AMR parsing and generation , and the relationship of AMR to syntax . Meaning Representation ( AMR ) annotations are often assumed to closely mirror dependency syntax , but AMR explicitly does not require this , and the assumption has never been tested . We use our annotations to test a baseline AMR-to-syntax aligner , finding that this task is more difficult than AMRto-string alignment ; and to pinpoint errors in an AMR parser . We make our data and code freely available for further research on AMR parsing and generation , and the relationship of AMR to syntax . This paper presents a synchronous-graphgrammar-based approach for string-toAMR parsing . We apply Markov Chain Monte Carlo ( MCMC ) algorithms to learn Synchronous Hyperedge Replacement Grammar ( SHRG ) rules from a forest that represents likely derivations consistent with a fixed string-to-graph alignment . We make an analogy of string-toAMR parsing to the task of phrase-based machine translation and come up with an efficient algorithm to learn graph grammars from string-graph pairs . We propose an effective approximation strategy to resolve the complexity issue of graph compositions . We propose a grammar induction technique for AMR semantic parsing . While previous grammar induction techniques were designed to re-learn a new parser for each target application , the recently annotated AMR Bank provides a unique opportunity to induce a single model for understanding broad-coverage newswire text and support a wide range of applications . We transform the sequence to graph mapping problem to a word sequence to transition action sequence problem using a special transition system called a cache transition system . To address the sparsity issue of neural AMR parsing , we feed feature embeddings from the transition state to provide relevant local information for each decoder state . We present a monotonic hard attention model for the transition framework to handle the strictly left-to-right alignment between each transition state and the current buffer input focus . document : An AMR Aligner Tuned by Transition - based Parser In this paper , we propose a new rich resource enhanced AMR aligner which produces multiple alignments and a new transition system for AMR parsing along with its oracle parser . Our aligner is further tuned by our oracle parser via picking the alignment that leads to the highest - scored achievable AMR graph . Semantic information is extremely valuable for many applications , such as Information Retrieval , Question Answering and Machine Translation , etc . Semantic Dependency Parsing mainly faces two problems , one of which is the semantic scheme problem , and the other is algorithm for automatic semantic dependency parsing . The paper focuses on such two points as the determination of semantic system and automatic semantic dependency parsing algorithm for the systematic introduction on semantic dependency parsing . In this paper , we investigate various strate- gies to predict both syntactic dependency parsing and contiguous multiword expres- sion ( MWE ) recognition , testing them on the dependency version of French Tree- bank ( Abeille and Barrier , 2004 ) , as in- stantiated in the SPMRL Shared Task ( Seddah et al. , 2013 ) . Our work focuses on using an alternative representation of syntactically regular MWEs , which cap- tures their syntactic internal structure . This architecture induces latent feature representations of the derivations , which are used to discover correlations both within and between the two derivations , providing the first application of ISBNs to a multi-task learning problem . This joint model achieves competitive performance on both syntactic and semantic dependency parsing for several languages . Because of the general nature of the approach , this extension of the ISBN architecture to weakly synchronized syntactic-semantic derivations is also an exemplification of its applicability to other problems where two independent , but related , representations are being learned . In current systems for syntactic and semantic dependency parsing , people usually define a very high-dimensional feature space to achieve good performance . But these systems often suffer severe performance drops on out-of-domain test data due to the diversity of features of different domains . According to the graph-based dependency parsing method , the syntax dependency tree of a sentence can be predicted based on word combination strength in a bottom-up paradigm , from pairs of smaller structures to larger structures . Therefore , the effectiveness of the model can be verified by sentence dependency parsing . The experimental results on the Penn Chinese Treebank 5.1 indicate that our model can effectively and continuously learn word combination strength . The most significant advantage of the model is the ability of continuous learning and the concise implementation method . The effectiveness of the model can be verified by sentence dependency parsing . Experimental results on Penn Chinese Treebank 5.1 indicate that our model can learn word combination strengths effectively and continuously . All three of the classifiers were used to produce candidate relations for test data and the candidate relation that had the majority vote was chosen . The proposed approach achieved an accuracy of 86 % in experimentation , which shows that the proposed approach is a promising one for semantic dependency analysis of Chinese . In Japanese sign language , input is represented as a sequence of important words . In our method , semantic dependency analysis based on case frame grammar is carried out to complement the lack of information such as postpositions . Then , rule-based rephrasing is executed to obtain natural verbal language . Recently , there has been renewed interest in semantic dependency parsing , among which one of the paradigms focuses on parsing directed acyclic graphs ( DAGs ) . Consideration of the decoding problem in natural language semantic dependency parsing as finding a maximum spanning DAG of a weighted directed graph carries many complexities . In particular , the computational complexity ( and approximability ) of the problem has not been addressed in the literature to date . We propose a novel sentence compression based model for semantic dependency parsing without using any syntactic dependency information . Our model divides semantic dependency parsing into two sequential sub-tasks : sentence compression and semantic dependency recognition . Sentence compression method is used to get backbone information of the sentence , conveying candidate heads of arguments to the next step . This paper proposes an unsupervised training approach for SLU systems that leverages the structured semantic knowledge graphs of the emerging Semantic Web . The approach creates natural language surface forms of entity-relation-entity portions of knowledge graphs using a combination of web search retrieval and syntax-based dependency parsing . The new forms are used to train an SLU system in an unsupervised manner . document : Globally Normalized Transition - Based Neural Networks We introduce a globally normalized transition - based neural network model that achieves state - of - the - art part - of - speech tagging , dependency parsing and sentence compression results . Our model is a simple feed - forward neural network that operates on a task - specific transition system , yet achieves comparable or better accuracies than recurrent models . We also use a single hidden layer before the softmax classifier . subsection : Syntactic Task : Dependency Parsing D ependency parsing identifies syntactic relations ( such as an adjective modifying a noun ) between word pairs in a sentence . The “ backward ” and “ past ” modules are analogous . subsection : CVT for Dependency Parsing In a dependency parse , words in a sentence are treated as nodes in a graph . To our knowledge , the conjunction of these formal properties is not supported by any existing parser . Our transition-based parser , which uses a novel transition set and features based on bidirectional LSTMs , has value not just for UCCA parsing : its ability to handle more general graph structures can inform the development of parsers for other semantic DAG structures , and in languages that frequently use discontinuous structures . We experiment on three languages , using a uniform transition-based system and learning architecture for all parsing tasks . Despite notable conceptual , formal and domain differences , we show that multitask learning significantly improves UCCA parsing in both in-domain and out-of-domain settings . We present UCCAApp , an open-source , flexible web-application for syntactic and semantic phrase-based annotation in general , and for UCCA annotation in particular . UCCAApp supports a variety of formal properties that have proven useful for syntactic and semantic representation , such as discontiguous phrases , multiple parents and empty elements , making it useful to a variety of other annotation schemes with similar formal properties .
Syntactic parsing is done using an open source dependency parser called MaltParser ( Nivre et al. 2007 ) . Whereas to accomplish semantic parsing , we will construct a biological proposition bank ( BioPropBank ) and a corpus annotated with semantic roles . Then we will employ supervised methods to automatic label the semantic roles of a
Semantic parsing maps a sentence in natural language into a structured meaning representation . Previous studies show that semantic parsing with synchronous context-free grammars ( SCFGs ) achieves favorable performance over most other alternatives . Motivated by the observation that the performance of semantic parsing with SCFGs is closely tied to the translation rules , this article explores to extend translation rules with high quality and increased coverage in three ways .
We present the technique of shallow semantic parsing , the process of assigning a simple WHO did WHAT to WHOM , etc . , structure to sentences in text , as a useful tool in achieving this goal . We formulate the semantic parsing problem as a classification problem using support vector machines . Using a hand-labeled training set and a set of features drawn from earlier work together with some feature enhancements , we demonstrate a system that performs better than all other published results on shallow semantic parsing .
In the last two years , the models used for semantic parsing have changed dramatically , with the introduction of neural methods that allow us to rethink many of the previous assumptions underlying semantic parsing . Traditionally , the executable formalisms and models used in semantic parsing research have been heavily reliant on notions of formal semantics in linguistics , such as λ-calculus generated by a CCG parser . However , recent work with neural encoder-decoder semantic parsers allow for more accessible formalisms , such as standard programming languages , and NMT-style models that are much more approachable to a broader NLP audience .
We show how data mining techniques that are typically applied to text should be modified to enable an analyst to do effective semantic data mining on a large collection of short speech utterances . For the purposes of this paper , we examine semantic data mining in the context of semantic parsing and analysis in a specific situation involving the solution of a business problem that is known to the analyst . We are not attempting a generic semantic analysis of a set of speech .
We address this by introducing a robust system based on the lambda calculus for deriving neo-Davidsonian logical forms from dependency trees . These logical forms are then used for semantic parsing of natural language to Freebase . Experiments on the Free917 and Web-Questions datasets show that our representation is superior to the original dependency trees and that it outperforms a CCG-based representation on this task .
The work can be divided into two segments : global / camera motion estimation and object motion analysis . The former involves optical flow analysis and semantic meaning parsing , and the latter involves object detection and tracking . Although each of these topics has been studied extensively in the literature , a thorough system combining all of them without human intervention , especially under a real time application scenario , is still worthy of further investigation .
In the past , the search engine uses limited and shallow NLP technologies because NLP technology is not as mature as people have expected . In recent years , we have witnessed that NLP has made huge advances in various tasks such as semantic parser , question-answering , machine translation , machine reading comprehension and text generation . I think that now it is the time to consider applying these new technologies to a search engine to further improve the intelligence and naturalness of the search process .
Semantic parsing aims at mapping natural language utterances into structured meaning representations . In this work , we propose a structure-aware neural architecture which decomposes the semantic parsing process into two stages . Given an input utterance , we first generate a rough sketch of its meaning , where low-level information ( such as variable names and arguments ) is glossed over .
Correctly interpreting human instructions is the first step to human-robot interaction . Previous approaches to semantically parsing the instructions relied on large numbers of training examples with annotation to widely cover all words in a domain . Annotating large enough instructions with semantic forms needs exhaustive engineering efforts .
This paper describes a number of freely available tools for implementing and running spoken language understanding systems . Unlike other free tools ( e.g. the CSLU toolkit ) , the main emphasis is on spoken language understanding ( syntactic/semantic parsing , generation of language models for recognition etc . ) . The suite supports ( reads and/or writes ) a number of grammar formats defined for speech recognisers like Entropic ’s GrapHvite ( HTK standard lattice ) as well as common unification grammar formats used in NLP : The American PATRII and the European EUROTRA ( Augmented Phrase Structure Grammar ) format .
Figure [ reference ] shows example annotations . Great progress has been made in syntactic parsing . Most traditional SRL methods rely heavily on syntactic features .
tackstrom2015efficient showed that constraints could be enforced more efficiently using a clever dynamic program for exact inference . sutton2005joint modeled syntactic parsing and SRL jointly , and lewis2015joint jointly modeled SRL and CCG parsing . collobert2011natural were among the first to use a neural network model for SRL , a CNN over word embeddings which failed to out - perform non - neural models .
section : Introduction and Background Natural language inference ( NLI ) or recognizing textual entailment ( RTE ) is a fundamental semantic task in the field of natural language processing . The problem is to determine whether a given hypothesis sentence can be logically inferred from a given premise sentence .
Multi - NLI Shared Task , and it also surpasses the state - of - the - art encoders for the SNLI dataset . In future work , we are also evaluating the effectiveness of shortcut - stacked sentence encoders on several other semantic tasks . section : Addendum : Shortcut vs. Residual
Therefore , the results of the examples ( a ) and ( b ) suggest that it would be needed to capture various types of root nodes , and using sentence - dependent root representations would lead to better results in future work . appendix : Analysis on Semantic Tasks We inspected the development set results on the semantic tasks using the “ JMT ” setting .
However , the scale and the difficulty for machines to interpret natural language still makes this task a challenging problem . The state - of - the - art techniques in open QA can be classified into two main classes , namely , information retrieval based and semantic parsing based . Information retrieval systems first retrieve a broad set of candidate answers by querying the search API of KBs with a transformation of the question into a valid query and then use fine - grained detection heuristics to identify the exact answer [ reference ][ reference ][ reference ] .
section : Related Work Our method is motivated by the central role played by alignment in machine translation and previous approaches to sentence similarity modeling , natural language inference , and semantic parsing . The neural counterpart to alignment , attention , which is a key part of our approach , was originally proposed and has been predominantly used in conjunction with LSTMs and to a lesser extent with CNNs .
On the other hand , even if the raw inputs are originally expressed in a sequence form , it can still benefit from the enhanced inputs with additional information ( to formulate graph inputs ) . For example , for semantic parsing tasks ( text - to - AMR or text - to - SQL ) , they have been shown better performance by augmenting the original sentence sequences with other structural information such as dependency parsing trees pust2015parsing . Intuitively , the ideal solution for graph - to - sequence tasks is to build a more powerful encoder which is able to learn the input representation regardless of its inherent structure .
Given the word embeddings , this task is solved by finding the word whose embedding is closest to the vector in terms of cosine proximity , i.e. , . Two categories of word analogy are used in this task : semantic and syntactic . Significantly outperforms GF at the :
Segmenting the high - resolution input with classical frameworks like FCN directly is time consuming . To overcome this shortcoming , we get semantic extraction using low - resolution input as shown in top branch of Fig . [ reference ] .
This would make it possible for the model to learn to parse using guidance from the semantic representation objective , which currently is blocked from influencing the key parsing parameters by our use of hard shift / reduce decisions . This change would allow the model to learn to produce parses that are , in aggregate , better suited to supporting semantic interpretation than those supplied in the training data . subsubsection : Acknowledgments
We have developed a heuristic method for unsupervised parsing of unrestricted text . Our method relies on detecting certain patterns of part-of-speech tag sequences of words in sentences .
In the last 12 years , there has been a big progress in the field of unsupervised dependency parsing . Different approaches however sometimes differ in motivation and definition of the problem .
We also describe the multiplicative combination of this dependency model with a model of linear constituency . The product model outperforms both components on their respective evaluation metrics , giving the best published figures for unsupervised dependency parsing and unsupervised constituency parsing . We also demonstrate that the combined model works and is robust cross-linguistically , being able to exploit either attachment or distributional regularities that are salient in the data .
We propose a non-parametric Bayesian model for unsupervised semantic parsing . Following Poon and Domingos ( 2009 ) , we consider a semantic parsing setting where the goal is to ( 1 ) decompose the syntactic dependency tree of a sentence into fragments , ( 2 ) assign each of these fragments to a cluster of semantically equivalent syntactic structures , and ( 3 ) predict predicate-argument relations between the fragments .
structured prediction ) has shown that joint inference can not only substantially improve predictive accuracy , but also enable effective learning with little or no labeled information . Markov logic is the unifying framework for statistical relational learning , and has spawned a series of successful NLP applications , ranging from information extraction to unsupervised semantic parsing . In this tutorial , I will introduce Markov logic to the NLP community and survey existing NLP applications .
We consider a new subproblem of unsupervised parsing from raw text , unsupervised partial parsing --- the unsupervised version of text chunking . We show that addressing this task directly , using probabilistic finite-state methods , produces better results than relying on the local predictions of a current best unsu-pervised parser , Seginer 's ( 2007 ) CCL .
We present an approach to unsupervised partial parsing : the identification of low-level constituents ( which we dub clumps ) in unannotated text . We begin by showing that CCLParser ( Seginer 2007 ) , an unsupervised parsing model , is particularly adept at identifying clumps , and that , surprisingly , building a simple right-branching structure above its clumps actually outperforms the full parser itself , indicating that much of the CCLParser 's performance comes from good local predictions .
Given a training set T and a test set S , the goal of our algorithm is to identify subset pairs Ti , Si of T and S such that when the unsupervised parser is trained on a training subset Ti its results on its paired test subset Si are better than when it is trained on the entire training set T. A successful application of zoomed learning improves overall performance on the full test set S. We study our algorithm 's effect on the leading algorithm for the task of fully unsupervised parsing ( Seginer , 2007 ) in three different English domains , WSJ , BROWN and GENIA , and show that it improves the parser F-score by up to 4.47 % .
Grammar induction refers to the process of learning grammars and languages from data ; this finds a variety of applications in syntactic pattern recognition , the modeling of natural language acquisition , data mining and machine translation . This special topic contains several papers presenting some of recent developments in the area of grammar induction and language learning , as applied to various problems in Natural Language Processing , including supervised and unsupervised parsing and statistical machine translation .
We propose a spectral approach for unsupervised constituent parsing that comes with theoretical guarantees on latent structure recovery . Our approach is grammarless ‐ we directly learn the bracketing structure of a given sentence without using a grammar model .
Also , as network content is both copious and dynamic , with topics and domains changing everyday , the approaches here must be primarily unsupervised . We propose a framework for unsupervised semantic analysis and organization of spoken documents and for this purpose propose two measures derived from latent topic analysis : latent topic significance and latent topic entropy . We show that these can be integrated into an application system , with which the user can more easily navigate archives of spoken documents .
Finally , in order to evaluate the learned semantic separators , we use them in unsupervised Chinese text parsing . The experiments on a manually labeled test set show that the proposed method outperforms previous methods of unsupervised text parsing .
Many approaches have been proposed to tackle the problem of Abstract Meaning Representation ( AMR ) parsing , help solving various natural language processing issues recently . In our paper , we provide an overview of different methods in AMR parsing and their performances when analyzing legal documents . We conduct experiments of different AMR parsers on our annotated dataset extracted from the English version of Japanese Civil Code .
We use our annotations to test a baseline AMR-to-syntax aligner , finding that this task is more difficult than AMRto-string alignment ; and to pinpoint errors in an AMR parser . We make our data and code freely available for further research on AMR parsing and generation , and the relationship of AMR to syntax . Meaning Representation ( AMR ) annotations are often assumed to closely mirror dependency syntax , but AMR explicitly does not require this , and the assumption has never been tested .
We use our annotations to test a baseline AMR-to-syntax aligner , finding that this task is more difficult than AMRto-string alignment ; and to pinpoint errors in an AMR parser . We make our data and code freely available for further research on AMR parsing and generation , and the relationship of AMR to syntax .
This paper presents a synchronous-graphgrammar-based approach for string-toAMR parsing . We apply Markov Chain Monte Carlo ( MCMC ) algorithms to learn Synchronous Hyperedge Replacement Grammar ( SHRG ) rules from a forest that represents likely derivations consistent with a fixed string-to-graph alignment . We make an analogy of string-toAMR parsing to the task of phrase-based machine translation and come up with an efficient algorithm to learn graph grammars from string-graph pairs . We propose an effective approximation strategy to resolve the complexity issue of graph compositions .
We propose a grammar induction technique for AMR semantic parsing . While previous grammar induction techniques were designed to re-learn a new parser for each target application , the recently annotated AMR Bank provides a unique opportunity to induce a single model for understanding broad-coverage newswire text and support a wide range of applications .
We transform the sequence to graph mapping problem to a word sequence to transition action sequence problem using a special transition system called a cache transition system . To address the sparsity issue of neural AMR parsing , we feed feature embeddings from the transition state to provide relevant local information for each decoder state . We present a monotonic hard attention model for the transition framework to handle the strictly left-to-right alignment between each transition state and the current buffer input focus .
document : An AMR Aligner Tuned by Transition - based Parser In this paper , we propose a new rich resource enhanced AMR aligner which produces multiple alignments and a new transition system for AMR parsing along with its oracle parser . Our aligner is further tuned by our oracle parser via picking the alignment that leads to the highest - scored achievable AMR graph .
Semantic information is extremely valuable for many applications , such as Information Retrieval , Question Answering and Machine Translation , etc . Semantic Dependency Parsing mainly faces two problems , one of which is the semantic scheme problem , and the other is algorithm for automatic semantic dependency parsing . The paper focuses on such two points as the determination of semantic system and automatic semantic dependency parsing algorithm for the systematic introduction on semantic dependency parsing .
In this paper , we investigate various strate- gies to predict both syntactic dependency parsing and contiguous multiword expres- sion ( MWE ) recognition , testing them on the dependency version of French Tree- bank ( Abeille and Barrier , 2004 ) , as in- stantiated in the SPMRL Shared Task ( Seddah et al. , 2013 ) . Our work focuses on using an alternative representation of syntactically regular MWEs , which cap- tures their syntactic internal structure .
This architecture induces latent feature representations of the derivations , which are used to discover correlations both within and between the two derivations , providing the first application of ISBNs to a multi-task learning problem . This joint model achieves competitive performance on both syntactic and semantic dependency parsing for several languages . Because of the general nature of the approach , this extension of the ISBN architecture to weakly synchronized syntactic-semantic derivations is also an exemplification of its applicability to other problems where two independent , but related , representations are being learned .
In current systems for syntactic and semantic dependency parsing , people usually define a very high-dimensional feature space to achieve good performance . But these systems often suffer severe performance drops on out-of-domain test data due to the diversity of features of different domains .
According to the graph-based dependency parsing method , the syntax dependency tree of a sentence can be predicted based on word combination strength in a bottom-up paradigm , from pairs of smaller structures to larger structures . Therefore , the effectiveness of the model can be verified by sentence dependency parsing . The experimental results on the Penn Chinese Treebank 5.1 indicate that our model can effectively and continuously learn word combination strength .
The most significant advantage of the model is the ability of continuous learning and the concise implementation method . The effectiveness of the model can be verified by sentence dependency parsing . Experimental results on Penn Chinese Treebank 5.1 indicate that our model can learn word combination strengths effectively and continuously .
All three of the classifiers were used to produce candidate relations for test data and the candidate relation that had the majority vote was chosen . The proposed approach achieved an accuracy of 86 % in experimentation , which shows that the proposed approach is a promising one for semantic dependency analysis of Chinese .
In Japanese sign language , input is represented as a sequence of important words . In our method , semantic dependency analysis based on case frame grammar is carried out to complement the lack of information such as postpositions . Then , rule-based rephrasing is executed to obtain natural verbal language .
Recently , there has been renewed interest in semantic dependency parsing , among which one of the paradigms focuses on parsing directed acyclic graphs ( DAGs ) . Consideration of the decoding problem in natural language semantic dependency parsing as finding a maximum spanning DAG of a weighted directed graph carries many complexities . In particular , the computational complexity ( and approximability ) of the problem has not been addressed in the literature to date .
We propose a novel sentence compression based model for semantic dependency parsing without using any syntactic dependency information . Our model divides semantic dependency parsing into two sequential sub-tasks : sentence compression and semantic dependency recognition . Sentence compression method is used to get backbone information of the sentence , conveying candidate heads of arguments to the next step .
This paper proposes an unsupervised training approach for SLU systems that leverages the structured semantic knowledge graphs of the emerging Semantic Web . The approach creates natural language surface forms of entity-relation-entity portions of knowledge graphs using a combination of web search retrieval and syntax-based dependency parsing . The new forms are used to train an SLU system in an unsupervised manner .
document : Globally Normalized Transition - Based Neural Networks We introduce a globally normalized transition - based neural network model that achieves state - of - the - art part - of - speech tagging , dependency parsing and sentence compression results . Our model is a simple feed - forward neural network that operates on a task - specific transition system , yet achieves comparable or better accuracies than recurrent models .
We also use a single hidden layer before the softmax classifier . subsection : Syntactic Task : Dependency Parsing D ependency parsing identifies syntactic relations ( such as an adjective modifying a noun ) between word pairs in a sentence .
The “ backward ” and “ past ” modules are analogous . subsection : CVT for Dependency Parsing In a dependency parse , words in a sentence are treated as nodes in a graph .
To our knowledge , the conjunction of these formal properties is not supported by any existing parser . Our transition-based parser , which uses a novel transition set and features based on bidirectional LSTMs , has value not just for UCCA parsing : its ability to handle more general graph structures can inform the development of parsers for other semantic DAG structures , and in languages that frequently use discontinuous structures .
We experiment on three languages , using a uniform transition-based system and learning architecture for all parsing tasks . Despite notable conceptual , formal and domain differences , we show that multitask learning significantly improves UCCA parsing in both in-domain and out-of-domain settings .
We present UCCAApp , an open-source , flexible web-application for syntactic and semantic phrase-based annotation in general , and for UCCA annotation in particular . UCCAApp supports a variety of formal properties that have proven useful for syntactic and semantic representation , such as discontiguous phrases , multiple parents and empty elements , making it useful to a variety of other annotation schemes with similar formal properties .
Domain Specific Automatic Question Generation from Text Improving Semantic Parsing with Enriched Synchronous Context-Free Grammars in Statistical Machine Translation Semantic role parsing: adding semantic structure to unstructured text Neural Semantic Parsing Semantic data mining of short utterances Transforming Dependency Structures to Logical Forms for Semantic Parsing Real time motion analysis toward semantic understanding of video content What Will Search Engines be Changed by NLP Advancements Coarse-to-Fine Decoding for Neural Semantic Parsing Understanding Instructions on Large Scale for Human-Robot Interaction The CPK NLP suite for spoken language understanding Dependency or Span, End-to-End Uniform Semantic Role Labeling Linguistically-Informed Self-Attention for Semantic Role Labeling Shortcut-Stacked Sentence Encoders for Multi-Domain Inference A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks Question Answering with Subgraph Embeddings A Decomposable Attention Model for Natural Language Inference Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks LINE: Large-scale Information Network Embedding ICNet for Real-Time Semantic Segmentation on High-Resolution Images A Fast Unified Model for Parsing and Sentence Understanding Pattern-based unsupervised parsing method Twelve Years of Unsupervised Dependency Parsing Corpus-Based Induction of Syntactic Structure: Models of Dependency and Constituency A Bayesian Model for Unsupervised Semantic Parsing Markov Logic in Natural Language Processing: Theory, Algorithms, and Applications Simple Unsupervised Grammar Induction from Raw Text with Cascaded Finite State Models Simple Unsupervised Identification of Low-Level Constituents Improved Fully Unsupervised Parsing with Zoomed Learning Introduction to the Special Topic on Grammar Induction, Representation of Language and Language Learning Spectral Unsupervised Parsing with Additive Tree Metrics Semantic Analysis and Organization of Spoken Documents Based on Parameters Derived From Latent Topics Semantic separator learning and its applications in unsupervised Chinese text parsing An empirical evaluation of AMR parsing for legal documents A Structured Syntax-Semantics Interface for English-AMR Alignment A Synchronous Hyperedge Replacement Grammar based approach for AMR parsing Broad-coverage CCG Semantic Parsing with AMR Sequence-to-sequence Models for Cache Transition Systems An AMR Aligner Tuned by Transition-based Parser Chinese Semantic Dependency Parsing Strategies for Contiguous Multiword Expression Analysis and Dependency Parsing Multilingual Joint Parsing of Syntactic and Semantic Dependencies with a Latent Variable Model Domain Adaptation for Syntactic and Semantic Dependency Parsing Using Deep Belief Networks A Multi-word-agent Autonomous Learning Model for Regulating Word Combination Strength Words Are Analogous To Lymphocytes: A Multi-Word-Agent Autonomous Learning Model Integration of Multiple Classifiers for Chinese Semantic Dependency Analysis Sign language translation based on syntactic and semantic analysis The complexity of finding the maximum spanning DAG and other restrictions for DAG parsing of natural language A lightweight Chinese semantic dependency parsing model based on sentence compression Exploiting the Semantic Web for unsupervised spoken language understanding Globally Normalized Transition-Based Neural Networks A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks Semi-Supervised Sequence Modeling with Cross-View Training A Transition-Based Directed Acyclic Graph Parser for UCCA Multitask Parsing Across Semantic Representations UCCAApp: Web-application for Syntactic and Semantic Phrase-based Annotation
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