Toward the automatic extraction of knowledge of usable goods

Mei Uemura, Naho Orita, Naoaki Okazaki, Kentaro Inui

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Knowledge of usable goods (e.g., toothbrush is used to clean the teeth and treadmill is used for exercise) is ubiquitous and in constant demand. This study proposes semantic labels to capture aspects of knowledge of usable goods and builds a benchmark corpus, Usable Goods Corpus, to explore this new semantic labeling task. Our human annotation experiment shows that human annotators can generally identify pieces of information of usable goods in text. Our first attempt toward the automatic identification of such knowledge shows that a model using conditional random fields approaches the human annotation (F score 73.2%). These results together suggest future directions to build a large-scale corpus and improve the automatic identification of knowledge of usable goods.

Original languageEnglish
Title of host publicationProceedings of the 30th Pacific Asia Conference on Language, Information and Computation, PACLIC 2016
EditorsJong C. Park, Jin-Woo Chung
PublisherInstitute for the Study of Language and Information
Pages277-285
Number of pages9
ISBN (Electronic)9788968174285
Publication statusPublished - 2016
Externally publishedYes
Event30th Pacific Asia Conference on Language, Information and Computation, PACLIC 2016 - Seoul, Korea, Republic of
Duration: 2016 Oct 282016 Oct 30

Publication series

NameProceedings of the 30th Pacific Asia Conference on Language, Information and Computation, PACLIC 2016

Other

Other30th Pacific Asia Conference on Language, Information and Computation, PACLIC 2016
Country/TerritoryKorea, Republic of
CitySeoul
Period16/10/2816/10/30

ASJC Scopus subject areas

  • Language and Linguistics
  • Computer Science (miscellaneous)
  • Information Systems

Fingerprint

Dive into the research topics of 'Toward the automatic extraction of knowledge of usable goods'. Together they form a unique fingerprint.

Cite this