Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL
- Cite this paper as:
- Turney P.D. (2001) Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL. In: De Raedt L., Flach P. (eds) Machine Learning: ECML 2001. ECML 2001. Lecture Notes in Computer Science, vol 2167. Springer, Berlin, Heidelberg
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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