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Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL
Open accessAbstract
This paper presents a simple unsupervised learning algorithm for recognizing synonyms, based on statistical data acquired by querying a Web search engine. The algorithm, called PMI-IR, uses Pointwise Mutual Information (PMI) and Information Retrieval (IR) to measure the similarity of pairs of words. PMI-IR is empirically evaluated using 80 synonym test questions from the Test of English as a Foreign Language (TOEFL) and 50 synonym test questions from a collection of tests for students of English as a Second Language (ESL). On both tests, the algorithm obtains a score of 74%. PMI-IR is contrasted with Latent Semantic Analysis (LSA), which achieves a score of 64% on the same 80 TOEFL questions. The paper discusses potential applications of the new unsupervised learning algorithm and some implications of the results for LSA and LSI (Latent Semantic Indexing).
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Cited by
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Metadata
- Authors
- Turney, P. D.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2001
- Pages
- 491-502
- ISSN
- 0302-9743 (ISSN-L)
- OpenAlex
- W1567365482
- Cited by
- 1,285
- References
- 0
- FWCI
- 9.83
- Type
- conference paper
- Language
- en
- Open access
- green
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