Emad Nabil

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Nonstandard words such as proper nouns, abbreviations, and acronyms are a major obstacle in natural language text processing and information retrieval. Acronyms, in particular, are difficult to read and process because they are often domain-specific with high degree of polysemy. In this paper, we propose a language modeling approach for the automatic disambiguation of acronym senses using context information. First, a dictionary of all possible expansions of acronyms is generated automatically. The dictionary is used to search for all possible expansions or senses to expand a given acronym. The extracted dictionary consists of about 17 thousands acronym-expansion pairs defining 1,829 expansions from different fields where the average number of expansions per acronym was 9.47. Training data is automatically collected from downloaded documents identified from the results of search engine queries. The collected data is used to build a unigram language model that models the context of each candidate expansion. At the in-context expansion prediction phase, the relevance of acronym expansion candidates is calculated based on the similarity between the context of each specific acronym occurrence and the language model of each candidate expansion. Unlike other work in the literature, our approach has the option to reject to expand an acronym if it is not confident on disambiguation. We have evaluated the performance of our language modeling approach and compared it with tf-idf discriminative approach.