Semantic Role Labeling improves incremental parsing

Ioannis Konstas; Frank Keller

Semantic Role Labeling improves incremental parsing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Incremental parsing is the task of assigning a syntactic structure to an input sentence as it unfolds word by word. Incremental parsing is more difficult than full sentence parsing, as incomplete input increases ambiguity. Intuitively, an incremental parser that has access to semantic information should be able to reduce ambiguity by ruling out semantically implausible analyses, even for incomplete input. In this paper, we test this hypothesis by combining an incremental TAG parser with an incremental semantic role labeler in a discriminative framework. We show a substantial improvement in parsing performance compared to the baseline parser, both in full-sentence F-score and in incremental F-score.

Original language	English
Title of host publication	Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing
Publisher	Association for Computational Linguistics
Pages	1191-1201
Number of pages	11
Volume	1
ISBN (Print)	9781941643723
Publication status	Published - 2015
Event	53rd Annual Meeting of the Association for Computational Linguistics 2015 - Beijing, China Duration: 26 Jul 2015 → 31 Jul 2015

Conference

Conference	53rd Annual Meeting of the Association for Computational Linguistics 2015
Abbreviated title	ACL 2015
Country/Territory	China
City	Beijing
Period	26/07/15 → 31/07/15

Access to Document

https://www.aclweb.org/anthology/P15-1115

Cite this

@inproceedings{ed633694499346838b0c84cf7a0a116d,

title = "Semantic Role Labeling improves incremental parsing",

abstract = "Incremental parsing is the task of assigning a syntactic structure to an input sentence as it unfolds word by word. Incremental parsing is more difficult than full sentence parsing, as incomplete input increases ambiguity. Intuitively, an incremental parser that has access to semantic information should be able to reduce ambiguity by ruling out semantically implausible analyses, even for incomplete input. In this paper, we test this hypothesis by combining an incremental TAG parser with an incremental semantic role labeler in a discriminative framework. We show a substantial improvement in parsing performance compared to the baseline parser, both in full-sentence F-score and in incremental F-score.",

author = "Ioannis Konstas and Frank Keller",

year = "2015",

language = "English",

isbn = "9781941643723",

volume = "1",

pages = "1191--1201",

booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing",

publisher = "Association for Computational Linguistics",

note = "53rd Annual Meeting of the Association for Computational Linguistics 2015, ACL 2015 ; Conference date: 26-07-2015 Through 31-07-2015",

}

Konstas, I & Keller, F 2015, Semantic Role Labeling improves incremental parsing. in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. vol. 1, Association for Computational Linguistics, pp. 1191-1201, 53rd Annual Meeting of the Association for Computational Linguistics 2015, Beijing, China, 26/07/15. <https://www.aclweb.org/anthology/P15-1115>

Semantic Role Labeling improves incremental parsing. / Konstas, Ioannis; Keller, Frank.
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. Vol. 1 Association for Computational Linguistics, 2015. p. 1191-1201.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Semantic Role Labeling improves incremental parsing

AU - Konstas, Ioannis

AU - Keller, Frank

PY - 2015

Y1 - 2015

N2 - Incremental parsing is the task of assigning a syntactic structure to an input sentence as it unfolds word by word. Incremental parsing is more difficult than full sentence parsing, as incomplete input increases ambiguity. Intuitively, an incremental parser that has access to semantic information should be able to reduce ambiguity by ruling out semantically implausible analyses, even for incomplete input. In this paper, we test this hypothesis by combining an incremental TAG parser with an incremental semantic role labeler in a discriminative framework. We show a substantial improvement in parsing performance compared to the baseline parser, both in full-sentence F-score and in incremental F-score.

AB - Incremental parsing is the task of assigning a syntactic structure to an input sentence as it unfolds word by word. Incremental parsing is more difficult than full sentence parsing, as incomplete input increases ambiguity. Intuitively, an incremental parser that has access to semantic information should be able to reduce ambiguity by ruling out semantically implausible analyses, even for incomplete input. In this paper, we test this hypothesis by combining an incremental TAG parser with an incremental semantic role labeler in a discriminative framework. We show a substantial improvement in parsing performance compared to the baseline parser, both in full-sentence F-score and in incremental F-score.

M3 - Conference contribution

SN - 9781941643723

VL - 1

SP - 1191

EP - 1201

BT - Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing

PB - Association for Computational Linguistics

T2 - 53rd Annual Meeting of the Association for Computational Linguistics 2015

Y2 - 26 July 2015 through 31 July 2015

ER -

Semantic Role Labeling improves incremental parsing

Abstract

Conference

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