Expanding hierarchical contexts for constructing a semantic word network

Ustalov, Dmitry

URL:	http://www.dialog-21.ru/media/3982/dialogue2017_v1...
Weitere URL:	http://www.dialog-21.ru/media/3959/ustalovda.pdf
Dokumenttyp:	Konferenzveröffentlichung
Erscheinungsjahr:	2017
Buchtitel:	Computational Linguistics and Intellectual Technologies : Papers from the Annual conference "Dialogue 2017", Moscow, May 31-June 3, 2017
Titel einer Zeitschrift oder einer Reihe:	Dialogue
Band/Volume:	16,1
Seitenbereich:	369-381
Veranstaltungstitel:	Computational Linguistics and Intellectual Technologies 2017
Veranstaltungsort:	Moscow, Russia
Veranstaltungsdatum:	May 31 - June 3, 2017
Ort der Veröffentlichung:	Moscow, Russia
Verlag:	RSUH
ISSN:	2221-7932 , 2075-7182
Verwandte URLs:	http://www.dialog-21.ru/media/4160/ustal...
Sprache der Veröffentlichung:	Englisch
Einrichtung:	Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Information Systems III: Enterprise Data Analysis (Ponzetto 2016-)
Fachgebiet:	004 Informatik
Freie Schlagwörter (Englisch):	lexical semantics , hyponym , hypernym , subsumption , semantic network , crowdsourcing , Russian
Abstract:	A semantic word network is a network that represents the semantic relations between individual words or their lexical senses. This paper proposes Watlink, an unsupervised method for inducing a semantic word network (SWN) by constructing and expanding the hierarchical contexts using both the available dictionary resources and distributional semantics’ methods for is-a relations. It has three steps: context construction, context expansion, and context disambiguation. The proposed method has been evaluated on two di erent datasets for the Russian language. The former is a well-known lexical ontology built by the group of expert lexicographers. The latter, LRWC (“Lexical Relations from the Wisdom of the Crowd”), is a new resource created using crowdsourcing that contains both positive and negative human judgements for subsumptions. The proposed method outperformed the other relation extraction methods on both datasets according to recall and F1-score. Both the implementation of the Watlink method and the LRWC dataset are publicly available under libré licenses.