NgramQuery - Smart Information Extraction from Google N-gram using External Resources

Abstract

This paper describes the implementation of a generalized query language on Google Ngram database. This language allows for very expressive queries that exploit semantic similarity acquired both from corpora (e.g. LSA) and from WordNet, and phonetic similarity available from the CMU Pronouncing Dictionary. It contains a large number of new operators, which combined in a proper query can help users to extract n-grams having similarly close syntactic and semantic relational properties. We also characterize the operators with respect to their corpus affiliation and their functionality. The query syntax is considered next given in terms of Backus-Naur rules followed by a few interesting examples of how the tool can be used. We also describe the command-line arguments the user could input comparing them with the ones for retrieving n-grams through the interface of Google Ngram database. Finally we discuss possible improvements on the extraction process and some relevant query completeness issues.

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