Near-Synonym Choice in an Intelligent Thesaurus

Proceedings of NAACL HLT 2007, pages 356–363, Rochester, NY, April 2007. c⃝2007 Association for Computational Linguistics Near-Synonym Choice in an Intelligent Thesaurus Diana Inkpen School of Information Technology and Engineering, University of Ottawa 800 King Edward, Ottawa, ON, Canada, K1N 6N5 diana@site.uottawa.ca Abstract An intelligent thesaurus assists a writer with alternative choices of words and or- ders them by their suitability in the writ- ing context. In this paper we focus on methods for automatically choosing near- synonyms by their semantic coherence with the context. Our statistical method uses the Web as a corpus to compute mutual information scores. Evaluation experiments show that this method per- forms better than a previous method on the same task. We also propose and evalu- ate two more methods, one that uses anti- collocations, and one that uses supervised learning. To asses the difficulty of the task, we present results obtained by hu- man judges. 1 Introduction When composing a text, a writer can access a the- saurus to retrieve words that are similar to a given target word, when there is a need to avoid repeating the same word, or when the word does not seem to be the best choice in the context. Our intelligent thesaurus is an interactive appli- cation that presents the user with a list of alterna- tive words (near-synonyms), and, unlike standard thesauri, it orders the choices by their suitability to the writing context. We investigate how the collo- cational properties of near-synonyms can help with choosing the best words. This problem is difficult because the near-synonyms have senses that are very close to each other, and therefore they occur in sim- ilar contexts; we need to capture the subtle differ- ences specific to each near-synonym. Our thesaurus brings up only alternatives that have the same part-of-speech with the target word. The choices could come from various inventories of near-synonyms or similar words, for example the Roget thesaurus (Roget, 1852), dictionaries of syn- onyms (Hayakawa, 1994), or clusters acquired from corpora (Lin, 1998). In this paper we focus on the task of automat- ically selecting the best near-synonym that should be used in a particular context. The natural way to validate an algorithm for this task would be to ask human readers to evaluate the quality of the algo- rithm’s output, but this kind of evaluation would be very laborious. Instead, we validate the algorithms by deleting selected words from sample sentences, to see whether the algorithms can restore the miss- ing words. That is, we create a lexical gap and eval- uate the ability of the algorithms to fill the gap. Two examples are presented in Figure 1. All the near- synonyms of the original word, including the word itself, become the choices in the solution set (see the figure for two examples of solution sets). The task is to automatically fill the gap with the best choice in the particular context. We present a method of scor- ing the choices. The highest scoring near-synonym will be …

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