The increasing interest in dependency-based representations in natural language parsing in recent years appears to be motivated both by the potential usefulness of bilexical relations in disambiguation and by the gains in efficiency that result from the more constrained parsing problem for these representations . In this paper , we will review the state of the art in dependency-based parsing , starting with the theoretical foundations of dependency grammar and moving on to consider both grammar-driven and data-driven methods for dependency parsing . We will limit our attention to systems for dependency parsing in a narrow sense , i.e. systems where the analysis assigned to an input sentence takes the form of a dependency structure . Part-of-speech tagging is a basic step in Natural Language Processing that is often essential . Labeling the word forms of a text with fine-grained word-class information adds new value to it and can be a prerequisite for downstream processes like a dependency parser . Corpus linguists and lexicographers also benefit greatly from the improved search options that are available with tagged data . Since there is not much training data available , we automatically generate dependency trees by applying head-percolation rules and heuristics to the constituent trees . Second , we show how to extract useful features for dependency parsing from rich morphology in Korean . Once we build the dependency Treebank , any statistical parsing approach can be applied . Our approach achieves 11.6%-25 % higher F-score over state-ofthe-art English slot filling methods . Our experiments also demonstrate that as long as a few trigger seeds , name tagging and dependency parsing capabilities exist , this approach can be quickly adapted to any language and new slot types . Our promising results on Chinese slot filling can serve as a new benchmark . Dependency parsing is an important task in natural language processing whose results are used in many downstream tasks such as machine translation , information retrieval , relation extraction , question answering and many others . Most of the dependency parsing literature focuses on using end-to-end and sequence-to-sequence neural architectures as the core of the system . One such system , namely Biaffine dependency parser is explored in the current paper for effective dependency parsing of Korean language . This paper describes an empirical study of high-performance dependency parsers based on a semi-supervised learning approach . We describe an extension of semi-supervised structured conditional models ( SS-SCMs ) to the dependency parsing problem , whose framework is originally proposed in ( Suzuki and Isozaki , 2008 ) . Moreover , we introduce two extensions related to dependency parsing : The first extension is to combine SS-SCMs with another semi-supervised approach , described in ( Koo et al. , 2008 ) . The model is used to generate the score of the machine translation dependency tree . Maximum entropy method is used in the dependency parsing process . The experimental results show that , at the system level , the new metric based on maximum entropy dependency parsing model is effective . Then the main process of dependency parsing is analyzed in detail , including feature learning , training and decoding process . Lastly , we summarize the current problems and challenges during dependency parsing research , as well as discussing the further development . The 36 revised full papers were carefully reviewed and selected from a total of 87 submissions . The papers are organized in topical sections roughly corresponding to evaluation tasks : parsing - dependency parsing track , parsing - constituency parsing track , domain adaptation for dependency parsing , named entity recognition on transcribed broadcast news , cross-document coreference resolution of named person entities , anaphora resolution , supersense tagging , frame labeling over italian texts , lemmatisation , automatic speech recognition - large vocabulary transcription , forced alignment on spontaneous speech . subsection : CVT for Dependency Parsing In a dependency parse , words in a sentence are treated as nodes in a graph . Typed directed edges connect the words , forming a tree structure describing the syntactic structure of the sentence . whether entity co - occurs with another entity or verb that appears in the question , in some sentence of the passage . Dependency parse match : we dependency parse both the question and all the sentences in the passage , and extract an indicator feature of whether and are both found ; similar features are constructed for and . subsection : End - to - end Neural Network section : Introduction Deep neural networks ( DNNs ) excel at learning from labeled data and have achieved state of the art in a wide array of supervised NLP tasks such as dependency parsing [ reference ] , named entity recognition [ reference ] , and semantic role labeling [ reference ] . In contrast , learning from unlabeled data , especially under domain shift , remains a challenge . sThe main contributions of the paper are : ( 1 ) a more sophisticated inference procedure that is both efficient and can consider longer paths ( [ reference ] considered only answers directly connected to the question in the graph ) ; and ( 2 ) a richer representation of the answers which encodes the question - answer path and surrounding subgraph of the KB . Our approach is competitive with the current state - of - the - art on the recent benchmark WebQuestions [ reference ] without using any lexicon , rules or additional system for partof - speech tagging , syntactic or dependency parsing during training as most other systems do . section : Task Definition We investigate a combination of a traditional linear sparse feature model and a multi-layer neural network model for deterministic transition-based dependency parsing , by integrating the sparse features into the neural model . Correlations are drawn between the hybrid model and previous work on integrating word embedding features into a discrete linear model . Recently , deep learning methods have achieved good results in dependency parsing for many natural languages . In this paper , we investigate the use of bidirectional long short-term memory network models for both transition-based and graph-based dependency parsing for the Vietnamese language . We also reportour contribution in building a Vietnamese dependency treebank whose tagset conforms to the Universal Dependency schema . In this paper we propose a neural network based classifier voting approach to dependency parsing using multiple classifiers as component systems in an ensemble and a neural network algorithm as an oracle . We show significant improvements over the best component systems for both transition-based and graph-based dependency parsing . We also investigate different weighting schemes for voting among individual classifiers in the ensemble . Making Use of Ambiguity in Transition-based Chinese Dependency Parsing . In the training of transition-based dependency parsers , an oracle is used to predict a transition sequence for a sentence and its gold tree . We present an efficient model selection method using boosting for transition-based constituency parsing . It is designed for exploring a high-dimensional search space , defined by a large set of feature templates , as for example is typically the case when parsing morphologically rich languages . We could further increase the parsing and training speed with a parallel feature extraction and a parallel parsing algorithm . We are convinced that the Hash Kernel and the parallelization can be applied successful to other NLP applications as well such as transition based dependency parsers , phrase structrue parsers , and machine translation . We could further increase the parsing and training speed with a parallel feature extraction and a parallel parsing algorithm . We are convinced that the Hash Kernel and the parallelization can be applied successful to other NLP applications as well such as transition based dependency parsers , phrase structrue parsers , and machine translation . We show that using confidence-weighted classification in transition-based parsing gives results comparable to using SVMs with faster training and parsing time . We also compare with other online learning algorithms and investigate the effect of pruning features when using confidence-weighted classification . We also show how to reverse our technique to obtain new transition-based dependency parsers from existing tabular methods . Additionally , we provide a detailed discussion of the conditions under which the feature models commonly used in transition-based parsing can be integrated into our algorithms . We introduce a novel approach to the decoding problem in transition-based parsing : heuristic backtracking . This algorithm uses a series of partial parses on the sentence to locate the best candidate parse , using confidence estimates of transition decisions as a heuristic to guide the starting points of the search . Transition-based parsing of natural language uses transition systems to build directed annotation graphs ( digraphs ) for sentences . In this paper , we define , for an arbitrary ordered digraph , a unique decomposition and a corresponding linear encoding that are associated bijectively with each other via a new transition system . We introduce a new approach to transitionbased dependency parsing in which the parser does not directly construct a dependency structure , but rather an undirected graph , which is then converted into a directed dependency tree in a post-processing step . This alleviates error propagation , since undirected parsers do not need to observe the single-head constraint . In this work , we present a general compositional vector framework for transitionbased dependency parsing . The ability to use transition-based algorithms allows for the application of vector composition to a large set of languages where only dependency treebanks are available , as well as handling linguistic phenomena such as non-projectivities which pose problems for previously proposed methods . In our framework , a Web pattern is visually yet formally specified as a graph grammar , which is automatically induced through a grammar induction engine . The grammar induction engine is featured by converting the problem of ( 2-dimensional ) graph grammar induction to ( 1-dimensional ) string induction . Based on the induced pattern , matching instances are recognized from Web pages through a graph parsing process . By repeatedly comparing the results of bracketing in the current state to proper bracketing provided in the training corpus , the system learns a set of simple structural transformations that can be applied to reduce error . After describing the algorithm , we present results and compare these results to other recent results in automatic grammar induction . We illustrate the importance and utility of this rich collection of features by presenting its use on examples ranging from difficult programming language grammars to web server logs to binary data specification . We also show that our grammars have important compositionality properties and explain why such properties areimportant in modern applications such as automatic grammar induction . In terms of technical contributions , we provide a traditional high-level semantics for our new grammar formalization and show how to compile grammars into non deterministic automata . On language modeling , unsupervised RNNGs perform as well their supervised counterparts on benchmarks in English and Chinese . On constituency grammar induction , they are competitive with recent neural language models that induce tree structures from words through attention mechanisms . Inductive Inference is the process of making generalizations from sample . The present problem is an inquiry regarding finding new strategy with use of an evolutionary approach , more specifically Genetic Algorithm , to perform context-free grammar induction . The work includes an effort towards determining the optimal structuring element to be used along with GA for the induction of Context Free Grammar . Grammatical inference : An introductory survey.- Learning morphology - practice makes good.- A hierarchy of language families learnable by regular language learners.- What is the search space of the regular inference?.- A characterization of even Linear Languages and its application to the learning problem.- Object-Oriented inferences in a logical framework for feature grammars.- Automatic determination of a stochastic bi-gram class language model.- The acquisition of a lexicon from paired phoneme sequences and semantic representations.- Inference and estimation of a long-range trigram model.- Application of OSTIA to machine translation tasks.- Inducing probabilistic grammars by Bayesian model merging.- Statistical estimation of Stochastic Context-Free Grammars using the Inside-Outside algorithm and a transformation on grammars.- Statistical inductive learning of regular formal languages.- Learning stochastic regular grammars by means of a state merging method.- Forming grammars for structured documents : an application of grammatical inference.- A comparison of syntactic and statistical techniques for off-line OCR.- The algorithms RT and k-TTI : A first comparison.- Dynamic grammatical representations in guided propagation networks.- A hybrid connectionist-symbolic approach to regular grammatical inference based on neural learning and hierarchical clustering.- Inference of context-free grammars by enumeration : Structural containment as an ordering bias.- Representational issues for context free grammar induction using genetic algorithms.- Regular grammatical inference from positive and negative samples by genetic search : the GIG method.- Training and application of integrated grammar/bigram language models.- Learning unification-based grammars using the Spoken English Corpus.- Stochastic optimization of a probabilistic language model.- Computer assisted grammar construction . This paper introduces Linear Context-Free Rewriting Systems , a mildly context-sensitive grammar formalism that displays a range of interesting formal properties and that has attracted a lot of interest in the context of natural language parsing recently because of its capacity to describe discontinuous constituents and non-projective dependencies . Besides a presentation of the formalism , the paper includes a discussion of recent LCFRS applications in parsing and dependency grammar . Gerd Fliedner , Valia Kordoni , Christian Korthals , Andrea Kowalski , Christopher-John Rupp and Magdalena Wolska . And Geert-Jan Kruijff , who was always a helpful adviser with all his expertise in dependency grammar . I had the pleasure to visit Charles University in Prague , where I was invited to by Martin Platek and Vladislav Kubon . This paper proposes an event generation model which input the complaints of patients directly then output a series of events as complementary to traditional keywords based chief complaint capture . The event generation model adopts an open Chinese Information Extraction ( open Chinese IE ) and build a part-of-speech tagging to do dependency grammar analysis . Two kinds of evaluations are taken . In this paper , we suggest an alternative to the Dirichlet prior , a family of logistic normal distributions . We derive an inference algorithm for this family of distributions and experiment with the task of dependency grammar induction , demonstrating performance improvements with our priors on a set of six treebanks in different natural languages . Our covariance framework permits soft parameter tying within grammars and across grammars for text in different languages , and we show empirical gains in a novel learning setting using bilingual , non-parallel data . Learning the parameters of such models relies on solving a non-convex optimization problem , thus making them sensitive to initialization . We propose a new convex formulation to the task of dependency grammar induction . Our approach is discriminative , allowing the use of different kinds of features . Context free grammar which represents type 2 grammar in Chomsky hierarchy has many applications in Formal Language Theory , pattern recognition , Speech recognition , Machine learning , Compiler design and Genetic engineering etc . Identification of unknown Context Free grammar of the target language from positive examples is an extensive area in Grammatical Inference/ Grammar induction . In this paper we propose a novel method which finds the equivalent Chomsky Normal form . Two major approaches to graph based data mining are frequent sub graph mining and graph-based relational learning . This article will focus on one particular approach embodied in the Subdue system , along with recent advances in graph-based supervised learning , graph-based hierarchical conceptual clustering , and graph-grammar induction . The need for mining structured data has increased rapidly . The automated method is particularly helpful in the developmental stages of graph mining applications for product design . The graph mining technique has capability for graph grammar induction , which is extended here to automate the generation of engineering grammars . In general , graph mining can be applied to extract design heuristics from any discrete and relational design data that can be represented as graphs . Abstract We propose a novel approach to the graph grammar induction problem , the goal of which is to find a concise graph grammar whose language ( the set of graphs which can be generated using the grammar 's productions , i.e. , graph substitution rules ) contains all of the given “ positive ” graphs and none of the given “ negative ” graphs . Most of the existing induction methods learn graph grammars from positive examples alone , performing an iterative specific-to-general search procedure in which candidate productions for the grammar being induced are created from repeated subgraphs in the input graph set . This is in contrast to shift - reduce type neural models where the intermediate decisions are hard and induction is more difficult . Finally , note that our model captures undirected lexical relations and is thus distinct from work on dependency grammar induction where the learned head - modifier relations are directed . section : The Machine Reader We propose that ambiguous prepositional phrase attachment can be resolved on the basis of the relative strength of association of the preposition with noun and verb , estimated on the basis of word distribution in a large corpus . This work suggests that a distributional approach can be effective in resolving parsing problems that apparently call for complex reasoning . In this demonstration , we show an algorithm for the semantic interpretation of unrestricted texts . The algorithm presents a solution for the following interpretation problems : determination of the meaning of the verb , identification of thematic roles and adjuncts , and attachments of prepositional phrases ( PPs ) . An interesting aspect of the algorithm is that the solution of all these problems is interdependent . In this paper , we present our work on the classical problem of prepo- sitional phrase attachment . This forms part of an interlingua based machine translation system , in which the semantics of the source language sentences is captured in the form of Universal Networking Language ( UNL ) expressions . Existing methods on preposition representation treat prepositions no different from content words ( e.g. , word2vec and GloVe ) . In addition , recent studies aiming at solving prepositional attachment and preposition selection problems depend heavily on external linguistic resources and use dataset-specific word representations . In this paper we use word-triple counts ( one of the triples being a preposition ) to capture a preposition 's interaction with its attachment and complement . This LDA model generates pairs of lemmas , representing the two arguments of a semantic relation , and can be trained , in an unsupervised manner , on a corpus annotated with semantic dependencies . To evaluate our framework in isolation from the rest of a parser , we consider the special case of prepositional phrase attachment ambiguity . The results show that our semantically-motivated feature is effective in this case , and moreover , the LDA smoothing both produces semantically interpretable topics , and also improves performance over raw co-occurrence frequencies , demonstrating that it can successfully generalise patterns in the training data . Probabilistic models have been effective in resolving prepositional phrase attachment ambiguity , but sparse data remains a significant problem . We propose a solution based on similarity-based smoothing , where the probability of new PPs is estimated with information from similar examples generated using a thesaurus . In this paper , we pursue a different approach ; machine reading methods that make use of background knowledge to facilitate language understanding . To this end , we have developed two methods : The first method addresses prepositional phrase attachment ambiguity . It uses background knowledge within a semi-supervised machine learning algorithm that learns from both labeled and unlabeled data . In this paper we describe a neural network-based approach to prepositional phrase attachment disam biguation for real world texts . Although the use of semantic classes in this task seems intuitively to be adequate , methods employed to date have not used them very effectively . Our observations shine light on several assumptions about syntactic errors , showing some to be true and others to be false . For example , prepositional phrase attachment errors are indeed a major issue , while coordination scope errors do not hurt performance as much as expected . Next , we describe an algorithm that builds a parse in one syntactic representation to match a parse in another representation . In the following section we sketch two substantially different machine learning approaches to language modeling . Next , we mention two well-studied natural language learning tasks : named entity recognition and prepositional phrase attachment resolution . The subsequent section discusses how two systems , NL-Soar and Analogical Modeling , have been combined in a way that brings together their relative strengths in novel and interesting ways involving these two tasks . Abstract We present an approach to simultaneously perform semantic segmentation and prepositional phrase attachment resolution for captioned images . Some ambiguities in language can not be resolved without simultaneously reasoning about an associated image . Together : Joint Segmentation & Prepositional Attachment Resolution in Captioned Scenes . We present an approach to simultaneously perform semantic segmentation and prepositional phrase attachment resolution for captioned images . Some ambiguities in language can not be resolved without simultaneously reasoning about an associated image .
The increasing interest in dependency-based representations in natural language parsing in recent years appears to be motivated both by the potential usefulness of bilexical relations in disambiguation and by the gains in efficiency that result from the more constrained parsing problem for these representations . In this paper , we will review the state of the art in dependency-based parsing , starting with the theoretical foundations of dependency grammar and moving on to consider both grammar-driven and data-driven methods for dependency parsing . We will limit our attention to systems for dependency parsing in a narrow sense , i.e. systems where the analysis assigned to an input sentence takes the form of a dependency structure .
Part-of-speech tagging is a basic step in Natural Language Processing that is often essential . Labeling the word forms of a text with fine-grained word-class information adds new value to it and can be a prerequisite for downstream processes like a dependency parser . Corpus linguists and lexicographers also benefit greatly from the improved search options that are available with tagged data .
Since there is not much training data available , we automatically generate dependency trees by applying head-percolation rules and heuristics to the constituent trees . Second , we show how to extract useful features for dependency parsing from rich morphology in Korean . Once we build the dependency Treebank , any statistical parsing approach can be applied .
Our approach achieves 11.6%-25 % higher F-score over state-ofthe-art English slot filling methods . Our experiments also demonstrate that as long as a few trigger seeds , name tagging and dependency parsing capabilities exist , this approach can be quickly adapted to any language and new slot types . Our promising results on Chinese slot filling can serve as a new benchmark .
Dependency parsing is an important task in natural language processing whose results are used in many downstream tasks such as machine translation , information retrieval , relation extraction , question answering and many others . Most of the dependency parsing literature focuses on using end-to-end and sequence-to-sequence neural architectures as the core of the system . One such system , namely Biaffine dependency parser is explored in the current paper for effective dependency parsing of Korean language .
This paper describes an empirical study of high-performance dependency parsers based on a semi-supervised learning approach . We describe an extension of semi-supervised structured conditional models ( SS-SCMs ) to the dependency parsing problem , whose framework is originally proposed in ( Suzuki and Isozaki , 2008 ) . Moreover , we introduce two extensions related to dependency parsing : The first extension is to combine SS-SCMs with another semi-supervised approach , described in ( Koo et al. , 2008 ) .
The model is used to generate the score of the machine translation dependency tree . Maximum entropy method is used in the dependency parsing process . The experimental results show that , at the system level , the new metric based on maximum entropy dependency parsing model is effective .
Then the main process of dependency parsing is analyzed in detail , including feature learning , training and decoding process . Lastly , we summarize the current problems and challenges during dependency parsing research , as well as discussing the further development .
The 36 revised full papers were carefully reviewed and selected from a total of 87 submissions . The papers are organized in topical sections roughly corresponding to evaluation tasks : parsing - dependency parsing track , parsing - constituency parsing track , domain adaptation for dependency parsing , named entity recognition on transcribed broadcast news , cross-document coreference resolution of named person entities , anaphora resolution , supersense tagging , frame labeling over italian texts , lemmatisation , automatic speech recognition - large vocabulary transcription , forced alignment on spontaneous speech .
subsection : CVT for Dependency Parsing In a dependency parse , words in a sentence are treated as nodes in a graph . Typed directed edges connect the words , forming a tree structure describing the syntactic structure of the sentence .
whether entity co - occurs with another entity or verb that appears in the question , in some sentence of the passage . Dependency parse match : we dependency parse both the question and all the sentences in the passage , and extract an indicator feature of whether and are both found ; similar features are constructed for and . subsection : End - to - end Neural Network
section : Introduction Deep neural networks ( DNNs ) excel at learning from labeled data and have achieved state of the art in a wide array of supervised NLP tasks such as dependency parsing [ reference ] , named entity recognition [ reference ] , and semantic role labeling [ reference ] . In contrast , learning from unlabeled data , especially under domain shift , remains a challenge .
sThe main contributions of the paper are : ( 1 ) a more sophisticated inference procedure that is both efficient and can consider longer paths ( [ reference ] considered only answers directly connected to the question in the graph ) ; and ( 2 ) a richer representation of the answers which encodes the question - answer path and surrounding subgraph of the KB . Our approach is competitive with the current state - of - the - art on the recent benchmark WebQuestions [ reference ] without using any lexicon , rules or additional system for partof - speech tagging , syntactic or dependency parsing during training as most other systems do . section : Task Definition
We investigate a combination of a traditional linear sparse feature model and a multi-layer neural network model for deterministic transition-based dependency parsing , by integrating the sparse features into the neural model . Correlations are drawn between the hybrid model and previous work on integrating word embedding features into a discrete linear model .
Recently , deep learning methods have achieved good results in dependency parsing for many natural languages . In this paper , we investigate the use of bidirectional long short-term memory network models for both transition-based and graph-based dependency parsing for the Vietnamese language . We also reportour contribution in building a Vietnamese dependency treebank whose tagset conforms to the Universal Dependency schema .
In this paper we propose a neural network based classifier voting approach to dependency parsing using multiple classifiers as component systems in an ensemble and a neural network algorithm as an oracle . We show significant improvements over the best component systems for both transition-based and graph-based dependency parsing . We also investigate different weighting schemes for voting among individual classifiers in the ensemble .
Making Use of Ambiguity in Transition-based Chinese Dependency Parsing . In the training of transition-based dependency parsers , an oracle is used to predict a transition sequence for a sentence and its gold tree .
We present an efficient model selection method using boosting for transition-based constituency parsing . It is designed for exploring a high-dimensional search space , defined by a large set of feature templates , as for example is typically the case when parsing morphologically rich languages .
We could further increase the parsing and training speed with a parallel feature extraction and a parallel parsing algorithm . We are convinced that the Hash Kernel and the parallelization can be applied successful to other NLP applications as well such as transition based dependency parsers , phrase structrue parsers , and machine translation .
We could further increase the parsing and training speed with a parallel feature extraction and a parallel parsing algorithm . We are convinced that the Hash Kernel and the parallelization can be applied successful to other NLP applications as well such as transition based dependency parsers , phrase structrue parsers , and machine translation .
We show that using confidence-weighted classification in transition-based parsing gives results comparable to using SVMs with faster training and parsing time . We also compare with other online learning algorithms and investigate the effect of pruning features when using confidence-weighted classification .
We also show how to reverse our technique to obtain new transition-based dependency parsers from existing tabular methods . Additionally , we provide a detailed discussion of the conditions under which the feature models commonly used in transition-based parsing can be integrated into our algorithms .
We introduce a novel approach to the decoding problem in transition-based parsing : heuristic backtracking . This algorithm uses a series of partial parses on the sentence to locate the best candidate parse , using confidence estimates of transition decisions as a heuristic to guide the starting points of the search .
Transition-based parsing of natural language uses transition systems to build directed annotation graphs ( digraphs ) for sentences . In this paper , we define , for an arbitrary ordered digraph , a unique decomposition and a corresponding linear encoding that are associated bijectively with each other via a new transition system .
We introduce a new approach to transitionbased dependency parsing in which the parser does not directly construct a dependency structure , but rather an undirected graph , which is then converted into a directed dependency tree in a post-processing step . This alleviates error propagation , since undirected parsers do not need to observe the single-head constraint .
In this work , we present a general compositional vector framework for transitionbased dependency parsing . The ability to use transition-based algorithms allows for the application of vector composition to a large set of languages where only dependency treebanks are available , as well as handling linguistic phenomena such as non-projectivities which pose problems for previously proposed methods .
In our framework , a Web pattern is visually yet formally specified as a graph grammar , which is automatically induced through a grammar induction engine . The grammar induction engine is featured by converting the problem of ( 2-dimensional ) graph grammar induction to ( 1-dimensional ) string induction . Based on the induced pattern , matching instances are recognized from Web pages through a graph parsing process .
By repeatedly comparing the results of bracketing in the current state to proper bracketing provided in the training corpus , the system learns a set of simple structural transformations that can be applied to reduce error . After describing the algorithm , we present results and compare these results to other recent results in automatic grammar induction .
We illustrate the importance and utility of this rich collection of features by presenting its use on examples ranging from difficult programming language grammars to web server logs to binary data specification . We also show that our grammars have important compositionality properties and explain why such properties areimportant in modern applications such as automatic grammar induction . In terms of technical contributions , we provide a traditional high-level semantics for our new grammar formalization and show how to compile grammars into non deterministic automata .
On language modeling , unsupervised RNNGs perform as well their supervised counterparts on benchmarks in English and Chinese . On constituency grammar induction , they are competitive with recent neural language models that induce tree structures from words through attention mechanisms .
Inductive Inference is the process of making generalizations from sample . The present problem is an inquiry regarding finding new strategy with use of an evolutionary approach , more specifically Genetic Algorithm , to perform context-free grammar induction . The work includes an effort towards determining the optimal structuring element to be used along with GA for the induction of Context Free Grammar .
Grammatical inference : An introductory survey.- Learning morphology - practice makes good.- A hierarchy of language families learnable by regular language learners.- What is the search space of the regular inference?.- A characterization of even Linear Languages and its application to the learning problem.- Object-Oriented inferences in a logical framework for feature grammars.- Automatic determination of a stochastic bi-gram class language model.- The acquisition of a lexicon from paired phoneme sequences and semantic representations.- Inference and estimation of a long-range trigram model.- Application of OSTIA to machine translation tasks.- Inducing probabilistic grammars by Bayesian model merging.- Statistical estimation of Stochastic Context-Free Grammars using the Inside-Outside algorithm and a transformation on grammars.- Statistical inductive learning of regular formal languages.- Learning stochastic regular grammars by means of a state merging method.- Forming grammars for structured documents : an application of grammatical inference.- A comparison of syntactic and statistical techniques for off-line OCR.- The algorithms RT and k-TTI : A first comparison.- Dynamic grammatical representations in guided propagation networks.- A hybrid connectionist-symbolic approach to regular grammatical inference based on neural learning and hierarchical clustering.- Inference of context-free grammars by enumeration : Structural containment as an ordering bias.- Representational issues for context free grammar induction using genetic algorithms.- Regular grammatical inference from positive and negative samples by genetic search : the GIG method.- Training and application of integrated grammar/bigram language models.- Learning unification-based grammars using the Spoken English Corpus.- Stochastic optimization of a probabilistic language model.- Computer assisted grammar construction .
This paper introduces Linear Context-Free Rewriting Systems , a mildly context-sensitive grammar formalism that displays a range of interesting formal properties and that has attracted a lot of interest in the context of natural language parsing recently because of its capacity to describe discontinuous constituents and non-projective dependencies . Besides a presentation of the formalism , the paper includes a discussion of recent LCFRS applications in parsing and dependency grammar .
Gerd Fliedner , Valia Kordoni , Christian Korthals , Andrea Kowalski , Christopher-John Rupp and Magdalena Wolska . And Geert-Jan Kruijff , who was always a helpful adviser with all his expertise in dependency grammar . I had the pleasure to visit Charles University in Prague , where I was invited to by Martin Platek and Vladislav Kubon .
This paper proposes an event generation model which input the complaints of patients directly then output a series of events as complementary to traditional keywords based chief complaint capture . The event generation model adopts an open Chinese Information Extraction ( open Chinese IE ) and build a part-of-speech tagging to do dependency grammar analysis . Two kinds of evaluations are taken .
In this paper , we suggest an alternative to the Dirichlet prior , a family of logistic normal distributions . We derive an inference algorithm for this family of distributions and experiment with the task of dependency grammar induction , demonstrating performance improvements with our priors on a set of six treebanks in different natural languages . Our covariance framework permits soft parameter tying within grammars and across grammars for text in different languages , and we show empirical gains in a novel learning setting using bilingual , non-parallel data .
Learning the parameters of such models relies on solving a non-convex optimization problem , thus making them sensitive to initialization . We propose a new convex formulation to the task of dependency grammar induction . Our approach is discriminative , allowing the use of different kinds of features .
Context free grammar which represents type 2 grammar in Chomsky hierarchy has many applications in Formal Language Theory , pattern recognition , Speech recognition , Machine learning , Compiler design and Genetic engineering etc . Identification of unknown Context Free grammar of the target language from positive examples is an extensive area in Grammatical Inference/ Grammar induction . In this paper we propose a novel method which finds the equivalent Chomsky Normal form .
Two major approaches to graph based data mining are frequent sub graph mining and graph-based relational learning . This article will focus on one particular approach embodied in the Subdue system , along with recent advances in graph-based supervised learning , graph-based hierarchical conceptual clustering , and graph-grammar induction . The need for mining structured data has increased rapidly .
The automated method is particularly helpful in the developmental stages of graph mining applications for product design . The graph mining technique has capability for graph grammar induction , which is extended here to automate the generation of engineering grammars . In general , graph mining can be applied to extract design heuristics from any discrete and relational design data that can be represented as graphs .
Abstract We propose a novel approach to the graph grammar induction problem , the goal of which is to find a concise graph grammar whose language ( the set of graphs which can be generated using the grammar 's productions , i.e. , graph substitution rules ) contains all of the given “ positive ” graphs and none of the given “ negative ” graphs . Most of the existing induction methods learn graph grammars from positive examples alone , performing an iterative specific-to-general search procedure in which candidate productions for the grammar being induced are created from repeated subgraphs in the input graph set .
This is in contrast to shift - reduce type neural models where the intermediate decisions are hard and induction is more difficult . Finally , note that our model captures undirected lexical relations and is thus distinct from work on dependency grammar induction where the learned head - modifier relations are directed . section : The Machine Reader
We propose that ambiguous prepositional phrase attachment can be resolved on the basis of the relative strength of association of the preposition with noun and verb , estimated on the basis of word distribution in a large corpus . This work suggests that a distributional approach can be effective in resolving parsing problems that apparently call for complex reasoning .
In this demonstration , we show an algorithm for the semantic interpretation of unrestricted texts . The algorithm presents a solution for the following interpretation problems : determination of the meaning of the verb , identification of thematic roles and adjuncts , and attachments of prepositional phrases ( PPs ) . An interesting aspect of the algorithm is that the solution of all these problems is interdependent .
In this paper , we present our work on the classical problem of prepo- sitional phrase attachment . This forms part of an interlingua based machine translation system , in which the semantics of the source language sentences is captured in the form of Universal Networking Language ( UNL ) expressions .
Existing methods on preposition representation treat prepositions no different from content words ( e.g. , word2vec and GloVe ) . In addition , recent studies aiming at solving prepositional attachment and preposition selection problems depend heavily on external linguistic resources and use dataset-specific word representations . In this paper we use word-triple counts ( one of the triples being a preposition ) to capture a preposition 's interaction with its attachment and complement .
This LDA model generates pairs of lemmas , representing the two arguments of a semantic relation , and can be trained , in an unsupervised manner , on a corpus annotated with semantic dependencies . To evaluate our framework in isolation from the rest of a parser , we consider the special case of prepositional phrase attachment ambiguity . The results show that our semantically-motivated feature is effective in this case , and moreover , the LDA smoothing both produces semantically interpretable topics , and also improves performance over raw co-occurrence frequencies , demonstrating that it can successfully generalise patterns in the training data .
Probabilistic models have been effective in resolving prepositional phrase attachment ambiguity , but sparse data remains a significant problem . We propose a solution based on similarity-based smoothing , where the probability of new PPs is estimated with information from similar examples generated using a thesaurus .
In this paper , we pursue a different approach ; machine reading methods that make use of background knowledge to facilitate language understanding . To this end , we have developed two methods : The first method addresses prepositional phrase attachment ambiguity . It uses background knowledge within a semi-supervised machine learning algorithm that learns from both labeled and unlabeled data .
In this paper we describe a neural network-based approach to prepositional phrase attachment disam biguation for real world texts . Although the use of semantic classes in this task seems intuitively to be adequate , methods employed to date have not used them very effectively .
Our observations shine light on several assumptions about syntactic errors , showing some to be true and others to be false . For example , prepositional phrase attachment errors are indeed a major issue , while coordination scope errors do not hurt performance as much as expected . Next , we describe an algorithm that builds a parse in one syntactic representation to match a parse in another representation .
In the following section we sketch two substantially different machine learning approaches to language modeling . Next , we mention two well-studied natural language learning tasks : named entity recognition and prepositional phrase attachment resolution . The subsequent section discusses how two systems , NL-Soar and Analogical Modeling , have been combined in a way that brings together their relative strengths in novel and interesting ways involving these two tasks .
Abstract We present an approach to simultaneously perform semantic segmentation and prepositional phrase attachment resolution for captioned images . Some ambiguities in language can not be resolved without simultaneously reasoning about an associated image .
Together : Joint Segmentation & Prepositional Attachment Resolution in Captioned Scenes . We present an approach to simultaneously perform semantic segmentation and prepositional phrase attachment resolution for captioned images . Some ambiguities in language can not be resolved without simultaneously reasoning about an associated image .
Dependency Grammar and Dependency Parsing A Proposal for a Part-of-Speech Tagset for the Albanian Language The Second Workshop on Statistical Parsing of Morphologically Rich Languages ( SPMRL Unsupervised Person Slot Filling based on Graph Mining Biaffine Dependency Parser for Korean An Empirical Study of Semi-supervised Structured Conditional Models for Dependency Parsing Automatic Evaluation Method Using Dependency Parsing Model Based on Maximum Entropy A Review of Graph-based Dependency Parsing Evaluation of Natural Language and Speech Tool for Italian: International Workshop, EVALITA 2011, Rome, January 24-25, 2012, Revised Selected Papers Semi-Supervised Sequence Modeling with Cross-View Training A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task Strong Baselines for Neural Semi-supervised Learning under Domain Shift Question Answering with Subgraph Embeddings Combining Discrete and Continuous Features for Deterministic Transition-based Dependency Parsing Using BiLSTM in Dependency Parsing for Vietnamese Classifier Ensemble Approach to Dependency Parsing Hybrid Oracle: Making Use of Ambiguity in Transition-based Chinese Dependency Parsing Boosting for Efficient Model Selection for Syntactic Parsing Top Accuracy and Fast Dependency Parsing is not a Contradiction Very high accuracy and fast dependency parsing is not a contradiction Transition-Based Parsing with Confidence-Weighted Classification Dynamic Programming Algorithms for Transition-Based Dependency Parsers Transition-Based Dependency Parsing with Heuristic Backtracking Transition-Based Coding and Formal Language Theory for Ordered Digraphs Dependency Parsing with Undirected Graphs Transition-based Dependency Parsing Using Recursive Neural Networks Specification and discovery of web patterns: a graph grammar approach Automatic Grammar Induction and Parsing Free Text: A Transformation-Based Approach Semantics and algorithms for data-dependent grammars Unsupervised Recurrent Neural Network Grammars Strategy for CFG Induction using Genetic Algorithm Grammatical Inference and Applications: Second International Colloquium, Icgi-94, Alicante, Spain, September 21-23, 1994 : Proceedings Linear Context-Free Rewriting Systems Extensible dependency grammar: a modular grammar formalism based on multigraph description Using Structured event to represent complaints of patients: a medical assistant for doctors Covariance in Unsupervised Learning of Probabilistic Grammars A convex and feature-rich discriminative approach to dependency grammar induction Context Free Grammar Identification from Positive Samples Innovative Study to the Graph-based Data Mining: Application of the Data Mining Automated Inclusive Design Heuristics Generation with Graph Mining Chapter Three - Graph grammar induction Long Short-Term Memory-Networks for Machine Reading Structural Ambiguity and Lexical Relations A System for the Semantic Interpretation of Unrestricted Domains using WordNet Prepositional Phrase Attachment and Interlingua Embedding Syntax and Semantics of Prepositions via Tensor Decomposition Leveraging a Semantically Annotated Corpus to Disambiguate Prepositional Phrase Attachment Thesauruses for Prepositional Phrase Attachment Machine Reading with Background Knowledge A Connectionist Approach to Propositional Phrase Attachment for Real World Texts Algorithms for Identifying Syntactic Errors and Parsing with Graph Structured Output Combining learning approaches for incremental on-line parsing Resolving vision and language ambiguities together: Joint segmentation & prepositional attachment resolution in captioned scenes Resolving Language and Vision Ambiguities Together: Joint Segmentation & Prepositional Attachment Resolution in Captioned Scenes
b354ad74a2cb74e8f5e440db2524cab37eee3325 bacb217676a19e2356f9028aa3e6b84f41388633 854676d238dcca1da95206ce2f4b24841c88d30d 2909e53b5abab88da090b84964ee1758ad7ad0e2 13a00864cb205684c03a29c0db3e72cf833e2f0a b47f0211778a2b84202bd5e0ac6003dd1909274d 7b6fc65ac92e16b82697a850998e4ca6fd72f8c2 d8c4a95262b152ea3ddd047fa92e565ad45f4a42 68932ae9a9f38e1b322698baab9617ad47cbd7b9 43c5cc8e9a6938159d9bc1c8f6ef89b503e17d9b baccebd288e3cf9ee845d8c6525992b457cc34b7 b871aa33b9cdd2c7af8e01991c6ad3c0f9088aa9 110d165a0cba0157ecbaa7f117b866c2c1d07d0f d227033cf926ee41f252c5c39542db9e720b0a47 51cfde9bbc25a4cff1d5666815674c83886d933e 09749f8c9c0775420efca1035467ca3e4e3621bc 3b0d2ab0a7ce9fa97cb7470b904055f3b81fc73a 6f1631fa69fe6ae17dbe9dc975304ae13f24ff0a 6a932c699e8cc8fb8ef66642b3db701a5fa04c8b a28e1ecc55619a4d0edd04eff9beb5d5f69e01dd 14bccd8b4e0b329f203504663f2d91f9818e6b55 a76aac7697a9ad2f0d522fbd15b553697f1eb7bc 2543f8de003401f281782f81cabac835d93f789d c4913c9431dfd3ac76ed69ff254da713b8f93b60 e5e2ec48891e5f58be0768c927253e4df2cc3036 d7dc79050f17154e7cf57501cf6cab1b9c18f232 1c27f9d15a345b93f79e8ef61dc8d06aea563a40 d35c28d1932280849a7388695122f5e7d24b9841 154759bed74fb8a0ae564bc28184f29b5af6501d 98e57728a218999b4679a865c60ac6474b3a5724 6519b0b43b8f7b64397d9cbc34dcb4a482f6075d 16bb7ff7c8527b991d9006d992d483ad666520e9 3ccf1dad8c4422fdff56b695fd8b20fcbe0887a7 ed13bf5d794e5b5ce58d1e92a60195c78120cdd5 1ae1ef12fc082a5d91084cdc9faa7382f20ff783 c70fff464ff4c5f7e792a5f3924be3bb71f22221 640204b0f379ad2ec2a3dc1c5b36dc9882e0bdb9 833ca7a4f4b7b360ac07abf122ab531033bc8e36 a1eeffd278089415ca9fa09aa3041297df118ad7 ca6a11019765374231b5e1b462a3d1b3a4b73ef4 850ce40ec9eee29bba1268299e1a0ce654e60d22 ceac25674614e37c60db4fa62fcd9a6c820b5015 7776f5ef4ab79a05f0772ca8024eef05c0159120 20217099318328a3b2a470caf50e057fdc02c522 d7cfb30e4aae9deabc21f9e8cd9d62f9b231b9f4 416c4fba0863792bece8b5196412209c1b984681 7548f0392ac1d58c4ec75d5cded0fd3cf17d2c60 2dbca69f6e50c058a44243abfaf669097a02c879 26eb2c900814707ae962184ad4173e754247a80a
["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["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"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"]
2005 2016 2016 2018 2009 2019 2017 2013 2018 2016 2018 2014 2015 2018 2017 2017 2016 2010 2010 2010 2011 2016 2019 2012 2013 2016 1993 2010 2019 2014 1994 2013 2006 2019 2010 2015 2018 2012 2013 2020 2016 1991 1999 2005 2018 2015 2004 2016 1998 2016 2004 2017 2016
LREC ACL (1) EMNLP EMNLP ACL ACL EMNLP EMNLP CICLing (1) COLING COLING ACL (Student Research Workshop) ACL EMNLP FSMNLP HLT POPL NAACL-HLT (1) ACL (1) EMNLP ACL AAAI/IAAI IWCS CoNLL COLING-ACL ICCM
https://api.semanticscholar.org/b354ad74a2cb74e8f5e440db2524cab37eee3325 https://api.semanticscholar.org/bacb217676a19e2356f9028aa3e6b84f41388633 https://api.semanticscholar.org/854676d238dcca1da95206ce2f4b24841c88d30d https://api.semanticscholar.org/2909e53b5abab88da090b84964ee1758ad7ad0e2 https://api.semanticscholar.org/13a00864cb205684c03a29c0db3e72cf833e2f0a https://api.semanticscholar.org/b47f0211778a2b84202bd5e0ac6003dd1909274d https://api.semanticscholar.org/7b6fc65ac92e16b82697a850998e4ca6fd72f8c2 https://api.semanticscholar.org/d8c4a95262b152ea3ddd047fa92e565ad45f4a42 https://api.semanticscholar.org/68932ae9a9f38e1b322698baab9617ad47cbd7b9 https://www.semanticscholar.org/paper/0c47cad9729c38d9db1f75491b1ee4bd883a5d4e https://www.semanticscholar.org/paper/b1e20420982a4f923c08652941666b189b11b7fe https://www.semanticscholar.org/paper/c6774ef150e54eac63b97b735d1184ac10c66416 https://www.semanticscholar.org/paper/33261d252218007147a71e40f8367ed152fa2fe0 https://api.semanticscholar.org/43c5cc8e9a6938159d9bc1c8f6ef89b503e17d9b https://api.semanticscholar.org/baccebd288e3cf9ee845d8c6525992b457cc34b7 https://api.semanticscholar.org/b871aa33b9cdd2c7af8e01991c6ad3c0f9088aa9 https://api.semanticscholar.org/110d165a0cba0157ecbaa7f117b866c2c1d07d0f https://api.semanticscholar.org/d227033cf926ee41f252c5c39542db9e720b0a47 https://api.semanticscholar.org/51cfde9bbc25a4cff1d5666815674c83886d933e https://api.semanticscholar.org/09749f8c9c0775420efca1035467ca3e4e3621bc https://api.semanticscholar.org/3b0d2ab0a7ce9fa97cb7470b904055f3b81fc73a https://api.semanticscholar.org/6f1631fa69fe6ae17dbe9dc975304ae13f24ff0a https://api.semanticscholar.org/6a932c699e8cc8fb8ef66642b3db701a5fa04c8b https://api.semanticscholar.org/a28e1ecc55619a4d0edd04eff9beb5d5f69e01dd https://api.semanticscholar.org/14bccd8b4e0b329f203504663f2d91f9818e6b55 https://api.semanticscholar.org/a76aac7697a9ad2f0d522fbd15b553697f1eb7bc https://api.semanticscholar.org/2543f8de003401f281782f81cabac835d93f789d https://api.semanticscholar.org/c4913c9431dfd3ac76ed69ff254da713b8f93b60 https://api.semanticscholar.org/e5e2ec48891e5f58be0768c927253e4df2cc3036 https://api.semanticscholar.org/d7dc79050f17154e7cf57501cf6cab1b9c18f232 https://api.semanticscholar.org/1c27f9d15a345b93f79e8ef61dc8d06aea563a40 https://api.semanticscholar.org/d35c28d1932280849a7388695122f5e7d24b9841 https://api.semanticscholar.org/154759bed74fb8a0ae564bc28184f29b5af6501d https://api.semanticscholar.org/98e57728a218999b4679a865c60ac6474b3a5724 https://api.semanticscholar.org/6519b0b43b8f7b64397d9cbc34dcb4a482f6075d https://api.semanticscholar.org/16bb7ff7c8527b991d9006d992d483ad666520e9 https://api.semanticscholar.org/3ccf1dad8c4422fdff56b695fd8b20fcbe0887a7 https://api.semanticscholar.org/ed13bf5d794e5b5ce58d1e92a60195c78120cdd5 https://api.semanticscholar.org/1ae1ef12fc082a5d91084cdc9faa7382f20ff783 https://api.semanticscholar.org/c70fff464ff4c5f7e792a5f3924be3bb71f22221 https://api.semanticscholar.org/640204b0f379ad2ec2a3dc1c5b36dc9882e0bdb9 https://www.semanticscholar.org/paper/13fe71da009484f240c46f14d9330e932f8de210 https://api.semanticscholar.org/833ca7a4f4b7b360ac07abf122ab531033bc8e36 https://api.semanticscholar.org/a1eeffd278089415ca9fa09aa3041297df118ad7 https://api.semanticscholar.org/ca6a11019765374231b5e1b462a3d1b3a4b73ef4 https://api.semanticscholar.org/850ce40ec9eee29bba1268299e1a0ce654e60d22 https://api.semanticscholar.org/ceac25674614e37c60db4fa62fcd9a6c820b5015 https://api.semanticscholar.org/7776f5ef4ab79a05f0772ca8024eef05c0159120 https://api.semanticscholar.org/20217099318328a3b2a470caf50e057fdc02c522 https://api.semanticscholar.org/d7cfb30e4aae9deabc21f9e8cd9d62f9b231b9f4 https://api.semanticscholar.org/416c4fba0863792bece8b5196412209c1b984681 https://api.semanticscholar.org/7548f0392ac1d58c4ec75d5cded0fd3cf17d2c60 https://api.semanticscholar.org/2dbca69f6e50c058a44243abfaf669097a02c879 https://api.semanticscholar.org/26eb2c900814707ae962184ad4173e754247a80a