Robust Approach to Abbreviating Terms: A Discriminative Latent Variable Model with Global Information

Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pages 905–913, Suntec, Singapore, 2-7 August 2009. c⃝2009 ACL and AFNLP Robust Approach to Abbreviating Terms: A Discriminative Latent Variable Model with Global Information Xu Sun†, Naoaki Okazaki†, Jun’ichi Tsujii†‡§ †Department of Computer Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan ‡School of Computer Science, University of Manchester, UK §National Centre for Text Mining, UK {sunxu, okazaki, tsujii}@is.s.u-tokyo.ac.jp Abstract The present paper describes a robust ap- proach for abbreviating terms. First, in order to incorporate non-local informa- tion into abbreviation generation tasks, we present both implicit and explicit solu- tions: the latent variable model, or alter- natively, the label encoding approach with global information. Although the two ap- proaches compete with one another, we demonstrate that these approaches are also complementary. By combining these two approaches, experiments revealed that the proposed abbreviation generator achieved the best results for both the Chinese and English languages. Moreover, we directly apply our generator to perform a very dif- ferent task from tradition, the abbreviation recognition. Experiments revealed that the proposed model worked robustly, and out- performed five out of six state-of-the-art abbreviation recognizers. 1 Introduction Abbreviations represent fully expanded forms (e.g., hidden markov model) through the use of shortened forms (e.g., HMM). At the same time, abbreviations increase the ambiguity in a text. For example, in computational linguistics, the acronym HMM stands for hidden markov model, whereas, in the field of biochemistry, HMM is gen- erally an abbreviation for heavy meromyosin. As- sociating abbreviations with their fully expanded forms is of great importance in various NLP ap- plications (Pakhomov, 2002; Yu et al., 2006; HaCohen-Kerner et al., 2008). The core technology for abbreviation disam- biguation is to recognize the abbreviation defini- tions in the actual text. Chang and Sch¨utze (2006) reported that 64,242 new abbreviations were intro- duced into the biomedical literatures in 2004. As such, it is important to maintain sense inventories (lists of abbreviation definitions) that are updated with the neologisms. In addition, based on the one-sense-per-discourse assumption, the recogni- tion of abbreviation definitions assumes senses of abbreviations that are locally defined in a docu- ment. Therefore, a number of studies have at- tempted to model the generation processes of ab- breviations: e.g., inferring the abbreviating mech- anism of the hidden markov model into HMM. An obvious approach is to manually design rules for abbreviations. Early studies attempted to determine the generic rules that humans use to intuitively abbreviate given words (Barrett and Grems, 1960; Bourne and Ford, 1961). Since the late 1990s, researchers have presented var- ious methods by which to extract abbreviation definitions that appear in actual texts (Taghva and Gilbreth, 1999; Park and Byrd, 2001; Wren and Garner, 2002; Schwartz and Hearst, 2003; Adar, 2004; Ao and Takagi, 2005). For example, Schwartz and Hearst (2003) implemented a simple algorithm that mapped all alpha-numerical letters in an abbreviation to its expanded form, starting from the end of both the abbreviation and …

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