Mahtab at SemEval-2017 Task 2: Combination of Corpus-based and Knowledge-based Methods to Measure Semantic Word Similarity

Niloofar Ranjbar, Fatemeh Mashhadirajab, Mehrnoush Shamsfard, Rayeheh Hosseini pour, Aryan Vahid pour


Abstract
In this paper, we describe our proposed method for measuring semantic similarity for a given pair of words at SemEval-2017 monolingual semantic word similarity task. We use a combination of knowledge-based and corpus-based techniques. We use FarsNet, the Persian Word Net, besides deep learning techniques to extract the similarity of words. We evaluated our proposed approach on Persian (Farsi) test data at SemEval-2017. It outperformed the other participants and ranked the first in the challenge.
Anthology ID:
S17-2040
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
256–260
Language:
URL:
https://www.aclweb.org/anthology/S17-2040
DOI:
10.18653/v1/S17-2040
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2040.pdf