A Language Modeling Approach for Acronym Expansion Disambiguation

  • Akram Gaballah AhmedEmail author
  • Mohamed Farouk Abdel Hady
  • Emad Nabil
  • Amr Badr
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9041)


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.


word sense disambiguation information extraction language modeling 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Akram Gaballah Ahmed
    • 2
    Email author
  • Mohamed Farouk Abdel Hady
    • 1
  • Emad Nabil
    • 2
  • Amr Badr
    • 2
  1. 1.MicrosoftRedmondUSA
  2. 2.Faculty of Computers and InformationCairo UniversityCairoEgypt

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