Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL

TL;DR

This paper introduces PMI-IR, an unsupervised method for recognizing synonyms using web search data, which outperforms LSA on TOEFL synonym questions with 74% accuracy.

Contribution

The paper presents PMI-IR, a novel unsupervised algorithm leveraging web data for synonym recognition, demonstrating superior performance over LSA on standard tests.

Findings

PMI-IR achieves 74% accuracy on TOEFL synonym questions.

PMI-IR outperforms LSA, which scores 64%.

The approach has potential applications in NLP tasks.

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).