Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL
<|reference_start|>Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL: 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).<|reference_end|>
@article{turney2002mining,
    title={Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL},
    author={Peter D. Turney (National Research Council of Canada)},
    journal={Proceedings of the Twelfth European Conference on Machine
  Learning, (2001), Freiburg, Germany, 491-502},
    year={2002},
    number={NRC-44893},
    archivePrefix={arXiv},
    eprint={cs/0212033},
    primaryClass={cs.LG cs.CL cs.IR}
}