@article{
 title = {Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL},
 type = {article},
 year = {2002},
 pages = {12},
 volume = {2167},
 websites = {http://cogprints.org/1796/},
 publisher = {Springer-Verlag},
 series = {Lecture Notes in Computer Science},
 editors = {[object Object],[object Object]},
 id = {c7011f39-3a49-3436-9c07-19e1f1e46bd0},
 created = {2012-12-24T15:02:36.000Z},
 file_attached = {true},
 profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},
 group_id = {066b42c8-f712-3fc3-abb2-225c158d2704},
 last_modified = {2017-03-14T14:36:19.698Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Turney2002a},
 private_publication = {false},
 abstract = {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).},
 bibtype = {article},
 author = {Turney, Peter D},
 journal = {Lecture Notes in Computer Science},
 number = {September}
}

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