SupWSD: A Flexible Toolkit for Supervised Word Sense Disambiguation

Simone Papandrea; Alessandro Raganato; Claudio Delli Bovi

doi:10.18653/v1/D17-2018

SupWSD: A Flexible Toolkit for Supervised Word Sense Disambiguation

Simone Papandrea, Alessandro Raganato, Claudio Delli Bovi

Language Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Abstract

In this demonstration we present SupWSD, a Java API for supervised Word Sense Disambiguation (WSD). This toolkit includes the implementation of a state-of-the-art supervised WSD system, together with a Natural Language Processing pipeline for preprocessing and feature extraction. Our aim is to provide an easy-to-use tool for the research community, designed to be modular, fast and scalable for training and testing on large datasets. The source code of SupWSD is available at http://github.com/SI3P/SupWSD.

Original language	Other/Unknown
Title of host publication	Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Number of pages	6
Place of Publication	Copenhagen, Denmark
Publisher	The Association for Computational Linguistics
Publication date	1 Sept 2017
Pages	103-108
DOIs	https://doi.org/10.18653/v1/D17-2018
Publication status	Published - 1 Sept 2017
MoE publication type	A4 Article in conference proceedings

Fields of Science

6121 Languages

Access to Document

10.18653/v1/D17-2018

D17-2018Final published version, 589 KB

https://www.aclweb.org/anthology/D17-2018

Cite this

@inproceedings{a108636821d14644b2a662c7f01400b1,

title = "SupWSD: A Flexible Toolkit for Supervised Word Sense Disambiguation",

abstract = "In this demonstration we present SupWSD, a Java API for supervised Word Sense Disambiguation (WSD). This toolkit includes the implementation of a state-of-the-art supervised WSD system, together with a Natural Language Processing pipeline for preprocessing and feature extraction. Our aim is to provide an easy-to-use tool for the research community, designed to be modular, fast and scalable for training and testing on large datasets. The source code of SupWSD is available at http://github.com/SI3P/SupWSD.",

keywords = "6121 Spr{\aa}kvetenskaper",

author = "Simone Papandrea and Alessandro Raganato and {Delli Bovi}, Claudio",

year = "2017",

month = sep,

day = "1",

doi = "10.18653/v1/D17-2018",

language = "odefinierat/ok{\"a}nt",

pages = "103--108",

booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",

publisher = "The Association for Computational Linguistics",

address = "F{\"o}renta Staterna (USA)",

}

SupWSD: A Flexible Toolkit for Supervised Word Sense Disambiguation. / Papandrea, Simone; Raganato, Alessandro; Delli Bovi, Claudio.
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Copenhagen, Denmark: The Association for Computational Linguistics, 2017. p. 103-108.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

TY - GEN

T1 - SupWSD: A Flexible Toolkit for Supervised Word Sense Disambiguation

AU - Papandrea, Simone

AU - Raganato, Alessandro

AU - Delli Bovi, Claudio

PY - 2017/9/1

Y1 - 2017/9/1

N2 - In this demonstration we present SupWSD, a Java API for supervised Word Sense Disambiguation (WSD). This toolkit includes the implementation of a state-of-the-art supervised WSD system, together with a Natural Language Processing pipeline for preprocessing and feature extraction. Our aim is to provide an easy-to-use tool for the research community, designed to be modular, fast and scalable for training and testing on large datasets. The source code of SupWSD is available at http://github.com/SI3P/SupWSD.

AB - In this demonstration we present SupWSD, a Java API for supervised Word Sense Disambiguation (WSD). This toolkit includes the implementation of a state-of-the-art supervised WSD system, together with a Natural Language Processing pipeline for preprocessing and feature extraction. Our aim is to provide an easy-to-use tool for the research community, designed to be modular, fast and scalable for training and testing on large datasets. The source code of SupWSD is available at http://github.com/SI3P/SupWSD.

KW - 6121 Språkvetenskaper

U2 - 10.18653/v1/D17-2018

DO - 10.18653/v1/D17-2018

M3 - Konferensbidrag

SP - 103

EP - 108

BT - Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

PB - The Association for Computational Linguistics

CY - Copenhagen, Denmark

ER -