IEICE TRANSACTIONS on Information
Measuring the Degree of Synonymy between Words Using Relational Similarity between Word Pairs as a Proxy
Summary :
Two types of similarities between words have been studied in the natural language processing community: synonymy and relational similarity. A high degree of similarity exist between synonymous words. On the other hand, a high degree of relational similarity exists between analogous word pairs. We present and empirically test a hypothesis that links these two types of similarities. Specifically, we propose a method to measure the degree of synonymy between two words using relational similarity between word pairs as a proxy. Given two words, first, we represent the semantic relations that hold between those words using lexical patterns. We use a sequential pattern clustering algorithm to identify different lexical patterns that represent the same semantic relation. Second, we compute the degree of synonymy between two words using an inter-cluster covariance matrix. We compare the proposed method for measuring the degree of synonymy against previously proposed methods on the Miller-Charles dataset and the WordSimilarity-353 dataset. Our proposed method outperforms all existing Web-based similarity measures, achieving a statistically significant Pearson correlation coefficient of 0.867 on the Miller-Charles dataset.
- Publication
- IEICE TRANSACTIONS on Information Vol.E95-D No.8 pp.2116-2123
- Publication Date
- 2012/08/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E95.D.2116
- Type of Manuscript
- PAPER
- Category
- Natural Language Processing
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Keyword
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Danushka BOLLEGALA, Yutaka MATSUO, Mitsuru ISHIZUKA, "Measuring the Degree of Synonymy between Words Using Relational Similarity between Word Pairs as a Proxy" in IEICE TRANSACTIONS on Information,
vol. E95-D, no. 8, pp. 2116-2123, August 2012, doi: 10.1587/transinf.E95.D.2116.
Abstract: Two types of similarities between words have been studied in the natural language processing community: synonymy and relational similarity. A high degree of similarity exist between synonymous words. On the other hand, a high degree of relational similarity exists between analogous word pairs. We present and empirically test a hypothesis that links these two types of similarities. Specifically, we propose a method to measure the degree of synonymy between two words using relational similarity between word pairs as a proxy. Given two words, first, we represent the semantic relations that hold between those words using lexical patterns. We use a sequential pattern clustering algorithm to identify different lexical patterns that represent the same semantic relation. Second, we compute the degree of synonymy between two words using an inter-cluster covariance matrix. We compare the proposed method for measuring the degree of synonymy against previously proposed methods on the Miller-Charles dataset and the WordSimilarity-353 dataset. Our proposed method outperforms all existing Web-based similarity measures, achieving a statistically significant Pearson correlation coefficient of 0.867 on the Miller-Charles dataset.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E95.D.2116/_p
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@ARTICLE{e95-d_8_2116,
author={Danushka BOLLEGALA, Yutaka MATSUO, Mitsuru ISHIZUKA, },
journal={IEICE TRANSACTIONS on Information},
title={Measuring the Degree of Synonymy between Words Using Relational Similarity between Word Pairs as a Proxy},
year={2012},
volume={E95-D},
number={8},
pages={2116-2123},
abstract={Two types of similarities between words have been studied in the natural language processing community: synonymy and relational similarity. A high degree of similarity exist between synonymous words. On the other hand, a high degree of relational similarity exists between analogous word pairs. We present and empirically test a hypothesis that links these two types of similarities. Specifically, we propose a method to measure the degree of synonymy between two words using relational similarity between word pairs as a proxy. Given two words, first, we represent the semantic relations that hold between those words using lexical patterns. We use a sequential pattern clustering algorithm to identify different lexical patterns that represent the same semantic relation. Second, we compute the degree of synonymy between two words using an inter-cluster covariance matrix. We compare the proposed method for measuring the degree of synonymy against previously proposed methods on the Miller-Charles dataset and the WordSimilarity-353 dataset. Our proposed method outperforms all existing Web-based similarity measures, achieving a statistically significant Pearson correlation coefficient of 0.867 on the Miller-Charles dataset.},
keywords={},
doi={10.1587/transinf.E95.D.2116},
ISSN={1745-1361},
month={August},}
Copy
TY - JOUR
TI - Measuring the Degree of Synonymy between Words Using Relational Similarity between Word Pairs as a Proxy
T2 - IEICE TRANSACTIONS on Information
SP - 2116
EP - 2123
AU - Danushka BOLLEGALA
AU - Yutaka MATSUO
AU - Mitsuru ISHIZUKA
PY - 2012
DO - 10.1587/transinf.E95.D.2116
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E95-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2012
AB - Two types of similarities between words have been studied in the natural language processing community: synonymy and relational similarity. A high degree of similarity exist between synonymous words. On the other hand, a high degree of relational similarity exists between analogous word pairs. We present and empirically test a hypothesis that links these two types of similarities. Specifically, we propose a method to measure the degree of synonymy between two words using relational similarity between word pairs as a proxy. Given two words, first, we represent the semantic relations that hold between those words using lexical patterns. We use a sequential pattern clustering algorithm to identify different lexical patterns that represent the same semantic relation. Second, we compute the degree of synonymy between two words using an inter-cluster covariance matrix. We compare the proposed method for measuring the degree of synonymy against previously proposed methods on the Miller-Charles dataset and the WordSimilarity-353 dataset. Our proposed method outperforms all existing Web-based similarity measures, achieving a statistically significant Pearson correlation coefficient of 0.867 on the Miller-Charles dataset.
ER -
English
