Meaning space at SemEval-2018 Task 10: Combining explicitly encoded knowledge with information extracted from word embeddings

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

This paper presents the two systems submitted
by the meaning space team in Task 10 of the
SemEval competition 2018 entitled Capturing
discriminative attributes. The systems consist
of combinations of approaches exploiting explicitly
encoded knowledge about concepts in
WordNet and information encoded in distributional
semantic vectors. Rather than aiming
for high performance, we explore which
kind of semantic knowledge is best captured
by different methods. The results indicate that
WordNet glosses on different levels of the hierarchy
capture many attributes relevant for this
task. In combination with exploiting word embedding
similarities, this source of information
yielded our best results. Our best performing
system ranked 5th out of 13 final ranks. Our
analysis yields insights into the different kinds
of attributes represented by different sources
of knowledge.
Original languageEnglish
Title of host publicationProceedings of the 12th International Workshop on Semantic Evaluation (SemEval-2018)
Place of PublicationNew Orleans, US
PublisherAssociation for Computational Linguistics (ACL)
Pages940-946
Number of pages7
Publication statusPublished - Apr 2018
EventSemEval-2018: International Workshop on Semantic Evaluation 2018 - New Orleans, United States
Duration: 5 Jun 20186 Jun 2018
http://alt.qcri.org/semeval2018/

Conference

ConferenceSemEval-2018
Abbreviated titleSemEval-2018
CountryUnited States
CityNew Orleans
Period5/06/186/06/18
Internet address

Fingerprint Dive into the research topics of 'Meaning space at SemEval-2018 Task 10: Combining explicitly encoded knowledge with information extracted from word embeddings'. Together they form a unique fingerprint.

Cite this