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A Supervised Learning Approach to Acronym Identification

By David Nadeau and Peter Turney

Abstract

This paper addresses the task of finding acronym-definition pairs in text. Most of the previous work on the topic is about systems that involve manually generated rules or regular expressions. In this paper, we present a supervised learning approach to the acronym identification task. Our approach reduces the search space of the supervised learning system by putting some weak constraints on the kinds of acronym-definition pairs that can be identified. We obtain results comparable to hand-crafted systems that use stronger constraints. We describe our method for reducing the search space, the features used by our supervised learning system, and our experiments with various learning schemes

Topics: Language
Publisher: Springer
Year: 2005
OAI identifier: oai:cogprints.org:4399

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Citations

  1. (2003). A simple algorithm for identifying abbreviation definitions in biomedical texts,
  2. (2000). Acrophile: An Automated Acronym Extractor and Server,
  3. (1999). Automatic extraction of acronyms from text.
  4. (2002). Creating an Online Dictionary of Abbreviations from MEDLINE,
  5. (2001). Extraction and Disambiguation of Acronym-Meaning Pairs in Medline",
  6. (2001). Hybrid Text Mining for Finding Abbreviations and Their Definitions,
  7. (2002). Mapping abbreviations to full forms in biomedical articles,
  8. (1999). Recognizing acronyms and their definitions, International journal on Document Analysis and Recognition,
  9. (2002). S-RAD A Simple and Robust Abbreviation Dictionary,
  10. (1998). Tagging Romanian Texts: a Case Study for QTAG, a Language Independent Probabilistic Tagger,

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