Accurate Maximum-Margin Training for Parsing With Context-Free Grammars
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Bauer, Alexander | - |
dc.contributor.author | Braun, Mikio | - |
dc.contributor.author | Mueller, Klaus-Robert | - |
dc.date.accessioned | 2021-09-03T11:10:53Z | - |
dc.date.available | 2021-09-03T11:10:53Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2017-01 | - |
dc.identifier.issn | 2162-237X | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/84976 | - |
dc.description.abstract | The task of natural language parsing can naturally be embedded in the maximum-margin framework for structured output prediction using an appropriate joint feature map and a suitable structured loss function. While there are efficient learning algorithms based on the cutting-plane method for optimizing the resulting quadratic objective with potentially exponential number of linear constraints, their efficiency crucially depends on the inference algorithms used to infer the most violated constraint in a current iteration. In this paper, we derive an extension of the well-known Cocke-Kasami-Younger (CKY) algorithm used for parsing with probabilistic context-free grammars for the case of loss-augmented inference enabling an effective training in the cutting-plane approach. The resulting algorithm is guaranteed to find an optimal solution in polynomial time exceeding the running time of the CKY algorithm by a term, which only depends on the number of possible loss values. In order to demonstrate the feasibility of the presented algorithm, we perform a set of experiments for parsing English sentences. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | MODELS | - |
dc.title | Accurate Maximum-Margin Training for Parsing With Context-Free Grammars | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Mueller, Klaus-Robert | - |
dc.identifier.doi | 10.1109/TNNLS.2015.2497149 | - |
dc.identifier.scopusid | 2-s2.0-85027570857 | - |
dc.identifier.wosid | 000391725000005 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, v.28, no.1, pp.44 - 56 | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | - |
dc.citation.title | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | - |
dc.citation.volume | 28 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 44 | - |
dc.citation.endPage | 56 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Hardware & Architecture | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | MODELS | - |
dc.subject.keywordAuthor | Context-free grammar (CFG) | - |
dc.subject.keywordAuthor | dynamic programming | - |
dc.subject.keywordAuthor | inference | - |
dc.subject.keywordAuthor | margin rescaling (MR) | - |
dc.subject.keywordAuthor | natural language parsing | - |
dc.subject.keywordAuthor | probabilistic context-free grammar (PCFG) | - |
dc.subject.keywordAuthor | slack rescaling (SR) | - |
dc.subject.keywordAuthor | structural support vector machines (SVMs) | - |
dc.subject.keywordAuthor | structured output | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.