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This paper studies different corpus-based algorithms through which to answer SAT sentence completion questions. SAT sentence completion questions assess how well different words fit into a sentence, and the ability to answer such types of questions have wide implications in optical character and speech recognition post-processing as well as word-suggestion programs. In our study, we analyze several statistical corpus-based methods through which to answer such questions, including normalized pointwise information, co-occurrence frequencies, latent semantic analysis, and the word2vec neural net implementations of continuous bag of words (CBOW) and continuous skip-gram (CSKIP) models. We find that the co-occurrence frequency method by itself has a near state-of-the-art performance with 52% correctness and that combining the co-occurrence frequency method with CBOW and CSKIP results in a state-of-the-art performance of 59%. The results of this study demonstrate that local context is a fairly strong measure in determining how well a word fits in a sentence and that exploration of non-similarity based methods may be required to further increase the ability of computers to answer such questions.

Assessing the Effectiveness of Corpus-Based Methods in Solving SAT Sentence Completion Questions