Toward Simulating the Human Way of Comparing Concepts
Summary :
An ability to assess similarity lies close to the core of cognition. Its understanding support the comprehension of human success in tasks like problem solving, categorization, memory retrieval, inductive reasoning, etc, and this is the main reason that it is a common research topic. In this paper, we introduce the idea of semantic differences and commonalities between words to the similarity computation process. Five new semantic similarity metrics are obtained after applying this scheme to traditional WordNet-based measures. We also combine the node based similarity measures with a corpus-independent way of computing the information content. In an experimental evaluation of our approach on two standard word pairs datasets, four of the measures outperformed their classical version, while the other performed as well as their unmodified counterparts.
- Publication
- IEICE TRANSACTIONS on Information Vol.E94-D No.7 pp.1419-1429
- Publication Date
- 2011/07/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E94.D.1419
- Type of Manuscript
- PAPER
- Category
- Data Engineering, Web Information Systems
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Keyword
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Raul Ernesto MENENDEZ-MORA, Ryutaro ICHISE, "Toward Simulating the Human Way of Comparing Concepts" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 7, pp. 1419-1429, July 2011, doi: 10.1587/transinf.E94.D.1419.
Abstract: An ability to assess similarity lies close to the core of cognition. Its understanding support the comprehension of human success in tasks like problem solving, categorization, memory retrieval, inductive reasoning, etc, and this is the main reason that it is a common research topic. In this paper, we introduce the idea of semantic differences and commonalities between words to the similarity computation process. Five new semantic similarity metrics are obtained after applying this scheme to traditional WordNet-based measures. We also combine the node based similarity measures with a corpus-independent way of computing the information content. In an experimental evaluation of our approach on two standard word pairs datasets, four of the measures outperformed their classical version, while the other performed as well as their unmodified counterparts.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.1419/_p
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@ARTICLE{e94-d_7_1419,
author={Raul Ernesto MENENDEZ-MORA, Ryutaro ICHISE, },
journal={IEICE TRANSACTIONS on Information},
title={Toward Simulating the Human Way of Comparing Concepts},
year={2011},
volume={E94-D},
number={7},
pages={1419-1429},
abstract={An ability to assess similarity lies close to the core of cognition. Its understanding support the comprehension of human success in tasks like problem solving, categorization, memory retrieval, inductive reasoning, etc, and this is the main reason that it is a common research topic. In this paper, we introduce the idea of semantic differences and commonalities between words to the similarity computation process. Five new semantic similarity metrics are obtained after applying this scheme to traditional WordNet-based measures. We also combine the node based similarity measures with a corpus-independent way of computing the information content. In an experimental evaluation of our approach on two standard word pairs datasets, four of the measures outperformed their classical version, while the other performed as well as their unmodified counterparts.},
keywords={},
doi={10.1587/transinf.E94.D.1419},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - Toward Simulating the Human Way of Comparing Concepts
T2 - IEICE TRANSACTIONS on Information
SP - 1419
EP - 1429
AU - Raul Ernesto MENENDEZ-MORA
AU - Ryutaro ICHISE
PY - 2011
DO - 10.1587/transinf.E94.D.1419
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2011
AB - An ability to assess similarity lies close to the core of cognition. Its understanding support the comprehension of human success in tasks like problem solving, categorization, memory retrieval, inductive reasoning, etc, and this is the main reason that it is a common research topic. In this paper, we introduce the idea of semantic differences and commonalities between words to the similarity computation process. Five new semantic similarity metrics are obtained after applying this scheme to traditional WordNet-based measures. We also combine the node based similarity measures with a corpus-independent way of computing the information content. In an experimental evaluation of our approach on two standard word pairs datasets, four of the measures outperformed their classical version, while the other performed as well as their unmodified counterparts.
ER -
English
