The Experts below are selected from a list of 159 Experts worldwide ranked by ideXlab platform
Josef Van Genabith - One of the best experts on this subject based on the ideXlab platform.
-
Long Distance Dependency resolution in automatically acquired wide coverage pcfg based lfg approximations
Meeting of the Association for Computational Linguistics, 2004Co-Authors: Aoife Cahill, Michael Burke, Ruth Odonovan, Josef Van GenabithAbstract:This paper shows how finite approximations of Long Distance Dependency (LDD) resolution can be obtained automatically for wide-coverage, robust, probabilistic Lexical-Functional Grammar (LFG) resources acquired from treebanks. We extract LFG subcategorisation frames and paths linking LDD reentrancies from f-structures generated automatically for the Penn-II treebank trees and use them in an LDD resolution algorithm to parse new text. Unlike (Collins, 1999; Johnson, 2000), in our approach resolution of LDDs is done at f-structure (attribute-value structure representations of basic predicate-argument or Dependency structure) without empty productions, traces and coindexation in CFG parse trees. Currently our best automatically induced grammars achieve 80.97% f-score for f-structures parsing section 23 of the WSJ part of the Penn-II treebank and evaluating against the DCU 1051 and 80.24% against the PARC 700 Dependency Bank (King et al., 2003), performing at the same or a slightly better level than state-of-the-art hand-crafted grammars (Kaplan et al., 2004).
-
ACL - Long-Distance Dependency Resolution in Automatically Acquired Wide-Coverage PCFG-Based LFG Approximations
Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics - ACL '04, 2004Co-Authors: Aoife Cahill, Michael Burke, Ruth O'donovan, Josef Van GenabithAbstract:This paper shows how finite approximations of Long Distance Dependency (LDD) resolution can be obtained automatically for wide-coverage, robust, probabilistic Lexical-Functional Grammar (LFG) resources acquired from treebanks. We extract LFG subcategorisation frames and paths linking LDD reentrancies from f-structures generated automatically for the Penn-II treebank trees and use them in an LDD resolution algorithm to parse new text. Unlike (Collins, 1999; Johnson, 2000), in our approach resolution of LDDs is done at f-structure (attribute-value structure representations of basic predicate-argument or Dependency structure) without empty productions, traces and coindexation in CFG parse trees. Currently our best automatically induced grammars achieve 80.97% f-score for f-structures parsing section 23 of the WSJ part of the Penn-II treebank and evaluating against the DCU 1051 and 80.24% against the PARC 700 Dependency Bank (King et al., 2003), performing at the same or a slightly better level than state-of-the-art hand-crafted grammars (Kaplan et al., 2004).
Youmin Zhang - One of the best experts on this subject based on the ideXlab platform.
-
fusion of Long Distance Dependency features for chinese named entity recognition based on markov logic networks
International conference natural language processing, 2012Co-Authors: Zejian Wu, Zhengtao Yu, Youmin ZhangAbstract:For the issue that existing methods for Chinese Named Entity Recognition(NER) fail to consider the Long-Distance dependencies, which is common in the document. This paper, Fusion of Long Distance Dependency, proposes a method for Chinese Named Entity Recognition(NER) based on Markov Logic Networks(MLNs), which comprehensively utilizes local, short Distance Dependency and Long Distance Dependency features by taking advantage of first order logic to represent knowledge, and then integrates all the features into Markov Network for Chinese named entity recognition with the help of MLNs. Validity of proposed method is verified both in open domain and restricted domain, experimental result shows that proposed method has better effect.
Aoife Cahill - One of the best experts on this subject based on the ideXlab platform.
-
Long Distance Dependency resolution in automatically acquired wide coverage pcfg based lfg approximations
Meeting of the Association for Computational Linguistics, 2004Co-Authors: Aoife Cahill, Michael Burke, Ruth Odonovan, Josef Van GenabithAbstract:This paper shows how finite approximations of Long Distance Dependency (LDD) resolution can be obtained automatically for wide-coverage, robust, probabilistic Lexical-Functional Grammar (LFG) resources acquired from treebanks. We extract LFG subcategorisation frames and paths linking LDD reentrancies from f-structures generated automatically for the Penn-II treebank trees and use them in an LDD resolution algorithm to parse new text. Unlike (Collins, 1999; Johnson, 2000), in our approach resolution of LDDs is done at f-structure (attribute-value structure representations of basic predicate-argument or Dependency structure) without empty productions, traces and coindexation in CFG parse trees. Currently our best automatically induced grammars achieve 80.97% f-score for f-structures parsing section 23 of the WSJ part of the Penn-II treebank and evaluating against the DCU 1051 and 80.24% against the PARC 700 Dependency Bank (King et al., 2003), performing at the same or a slightly better level than state-of-the-art hand-crafted grammars (Kaplan et al., 2004).
-
ACL - Long-Distance Dependency Resolution in Automatically Acquired Wide-Coverage PCFG-Based LFG Approximations
Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics - ACL '04, 2004Co-Authors: Aoife Cahill, Michael Burke, Ruth O'donovan, Josef Van GenabithAbstract:This paper shows how finite approximations of Long Distance Dependency (LDD) resolution can be obtained automatically for wide-coverage, robust, probabilistic Lexical-Functional Grammar (LFG) resources acquired from treebanks. We extract LFG subcategorisation frames and paths linking LDD reentrancies from f-structures generated automatically for the Penn-II treebank trees and use them in an LDD resolution algorithm to parse new text. Unlike (Collins, 1999; Johnson, 2000), in our approach resolution of LDDs is done at f-structure (attribute-value structure representations of basic predicate-argument or Dependency structure) without empty productions, traces and coindexation in CFG parse trees. Currently our best automatically induced grammars achieve 80.97% f-score for f-structures parsing section 23 of the WSJ part of the Penn-II treebank and evaluating against the DCU 1051 and 80.24% against the PARC 700 Dependency Bank (King et al., 2003), performing at the same or a slightly better level than state-of-the-art hand-crafted grammars (Kaplan et al., 2004).
Zejian Wu - One of the best experts on this subject based on the ideXlab platform.
-
fusion of Long Distance Dependency features for chinese named entity recognition based on markov logic networks
International conference natural language processing, 2012Co-Authors: Zejian Wu, Zhengtao Yu, Youmin ZhangAbstract:For the issue that existing methods for Chinese Named Entity Recognition(NER) fail to consider the Long-Distance dependencies, which is common in the document. This paper, Fusion of Long Distance Dependency, proposes a method for Chinese Named Entity Recognition(NER) based on Markov Logic Networks(MLNs), which comprehensively utilizes local, short Distance Dependency and Long Distance Dependency features by taking advantage of first order logic to represent knowledge, and then integrates all the features into Markov Network for Chinese named entity recognition with the help of MLNs. Validity of proposed method is verified both in open domain and restricted domain, experimental result shows that proposed method has better effect.
Michael Burke - One of the best experts on this subject based on the ideXlab platform.
-
Long Distance Dependency resolution in automatically acquired wide coverage pcfg based lfg approximations
Meeting of the Association for Computational Linguistics, 2004Co-Authors: Aoife Cahill, Michael Burke, Ruth Odonovan, Josef Van GenabithAbstract:This paper shows how finite approximations of Long Distance Dependency (LDD) resolution can be obtained automatically for wide-coverage, robust, probabilistic Lexical-Functional Grammar (LFG) resources acquired from treebanks. We extract LFG subcategorisation frames and paths linking LDD reentrancies from f-structures generated automatically for the Penn-II treebank trees and use them in an LDD resolution algorithm to parse new text. Unlike (Collins, 1999; Johnson, 2000), in our approach resolution of LDDs is done at f-structure (attribute-value structure representations of basic predicate-argument or Dependency structure) without empty productions, traces and coindexation in CFG parse trees. Currently our best automatically induced grammars achieve 80.97% f-score for f-structures parsing section 23 of the WSJ part of the Penn-II treebank and evaluating against the DCU 1051 and 80.24% against the PARC 700 Dependency Bank (King et al., 2003), performing at the same or a slightly better level than state-of-the-art hand-crafted grammars (Kaplan et al., 2004).
-
ACL - Long-Distance Dependency Resolution in Automatically Acquired Wide-Coverage PCFG-Based LFG Approximations
Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics - ACL '04, 2004Co-Authors: Aoife Cahill, Michael Burke, Ruth O'donovan, Josef Van GenabithAbstract:This paper shows how finite approximations of Long Distance Dependency (LDD) resolution can be obtained automatically for wide-coverage, robust, probabilistic Lexical-Functional Grammar (LFG) resources acquired from treebanks. We extract LFG subcategorisation frames and paths linking LDD reentrancies from f-structures generated automatically for the Penn-II treebank trees and use them in an LDD resolution algorithm to parse new text. Unlike (Collins, 1999; Johnson, 2000), in our approach resolution of LDDs is done at f-structure (attribute-value structure representations of basic predicate-argument or Dependency structure) without empty productions, traces and coindexation in CFG parse trees. Currently our best automatically induced grammars achieve 80.97% f-score for f-structures parsing section 23 of the WSJ part of the Penn-II treebank and evaluating against the DCU 1051 and 80.24% against the PARC 700 Dependency Bank (King et al., 2003), performing at the same or a slightly better level than state-of-the-art hand-crafted grammars (Kaplan et al., 2004).
