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

Pia Sommerauer, Antske Fokkens, Piek Vossen


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.
Anthology ID:
S18-1154
Volume:
Proceedings of The 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
940–946
Language:
URL:
https://www.aclweb.org/anthology/S18-1154
DOI:
10.18653/v1/S18-1154
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1154.pdf