Statistical measures of word similarity have application in many areas of natural language processing, such as language modeling and information retrieval. We report a comparative study of two methods for estimating word cooccurrence frequencies required by word similarity measures. Our frequency estimates are generated from a terabyte-sized corpus of Web data, and we study the impact of corpus size on the effectiveness of the measures. We base the evaluation on one TOEFL question set and two practice questions sets, each consisting of a number of multiple choice questions seeking the best synonym for a given target word. For two question sets, a context for the target word is provided, and we examine a number of word similarity measures that exploit this context. Our best combination of similarity measure and frequency estimation method answers 6-8% more questions than the best results previously reported for the same question sets.

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An usual approach to address mismatching vocabulary problem is to augment the original query using dictionaries and other lexical resources and/or by looking at pseudo-relevant documents. Either way, terms are added to form a new query that will be used to score all documents in a subsequent retrieval pass, and as consequence the original query's focus may drift because of the newly added terms. We propose a new method to address the mismatching vocabulary problem, expanding original query terms only when necessary and complementing the user query for missing terms while scoring documents. It allows related semantic aspects to be included in a conservative and selective way, thus reducing the possibility of query drift. Our results using replacements for the missing query terms in modified document and passages retrieval methods show significant improvement over the original ones.

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We present a framework for the fast computation of lexical affinity models. The framework is composed of a novel algorithm to efficiently compute the co-occurrence distribution between pairs of terms, an independence model, and a parametric affinity model. In comparison with previous models, which either use arbitrary windows to compute similarity between words or use lexical affinity to create sequential models, in this paper we focus on models intended to capture the co-occurrence patterns of any pair of words or phrases at any distance in the corpus. The framework is flexible, allowing fast adaptation to applications and it is scalable. We apply it in combination with a terabyte corpus to answer natural language tests, achieving encouraging results.

Using our question answering system, questions from the TREC 2001 evaluation were executed over a series of Web data collections, with the sizes of the collections increasing from 25 gigabytes up to nearly a terabyte.