TY - GEN
T1 - Measuring Similarity from Word Pair Matrices with Syntagmatic and Paradigmatic Associations
AU - Matsuoka, Jin
AU - Lepage, Yves
N1 - Publisher Copyright:
© 2014 Proceedings of the 4th Workshop on Cognitive Aspects of the Lexicon, CogALex 2014 at the 25th International Conference on Computational Linguistics, COLING 2014. All rights reserved.
PY - 2014
Y1 - 2014
N2 - Two types of semantic similarity are usually distinguished: attributional and relational similarities. These similarities measure the degree between words or word pairs. Attributional similarities are bidrectional, while relational similarities are one-directional. It is possible to compute such similarities based on the occurrences of words in actual sentences. Inside sentences, syntagmatic associations and paradigmatic associations can be used to characterize the relations between words or word pairs. In this paper, we propose a vector space model built from syntagmatic and paradigmatic associations to measure relational similarity between word pairs from the sentences contained in a small corpus. We conduct two experiments with different datasets: SemEval-2012 task 2, and 400 word analogy quizzes. The experimental results show that our proposed method is effective when using a small corpus.
AB - Two types of semantic similarity are usually distinguished: attributional and relational similarities. These similarities measure the degree between words or word pairs. Attributional similarities are bidrectional, while relational similarities are one-directional. It is possible to compute such similarities based on the occurrences of words in actual sentences. Inside sentences, syntagmatic associations and paradigmatic associations can be used to characterize the relations between words or word pairs. In this paper, we propose a vector space model built from syntagmatic and paradigmatic associations to measure relational similarity between word pairs from the sentences contained in a small corpus. We conduct two experiments with different datasets: SemEval-2012 task 2, and 400 word analogy quizzes. The experimental results show that our proposed method is effective when using a small corpus.
UR - http://www.scopus.com/inward/record.url?scp=85081053291&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:85081053291
T3 - Proceedings of the 4th Workshop on Cognitive Aspects of the Lexicon, CogALex 2014 at the 25th International Conference on Computational Linguistics, COLING 2014
SP - 77
EP - 86
BT - Proceedings of the 4th Workshop on Cognitive Aspects of the Lexicon, CogALex 2014 at the 25th International Conference on Computational Linguistics, COLING 2014
A2 - Zock, Michael
A2 - Rapp, Reinhard
A2 - Rapp, Reinhard
A2 - Huang, Chu-Ren
PB - Association for Computational Linguistics (ACL)
T2 - 4th Workshop on Cognitive Aspects of the Lexicon, CogALex 2014 at the 25th International Conference on Computational Linguistics, COLING 2014
Y2 - 23 August 2014
ER -