Abstract
Acronym extraction for business documents has been neglected in favor of acronym extraction for biomedical documents. Although there are overlapping challenges, the semi-structured and non-predictive nature of business documents hinder the effectiveness of the extraction methods used on biomedical documents and fail to deliver the expected performance. A classifier-based extraction subsystem is presented as part of the wider project, Binocle, for the analysis of French business corpora. Explicit and implicit acronym presentation cases are identified using textual and syntactical hints. Among the 7 features extracted from each candidate instance, we introduce “similarity” features, which compare a candidate’s characteristics with average length-related values calculated from a generic acronym repository. Commonly used rules for evaluating the candidate (matching first letters, ordered instances, etc.) are scored and aggregated in a single composite feature that permits a supple classification. One hundred and thirty-eight French business documents from 14 public organizations were used for the training and evaluation corpora, yielding a recall of 90.9% at a precision level of 89.1% for a search space size of 3 sentences.
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Ménard, P.A., Ratté, S. Classifier-based acronym extraction for business documents. Knowl Inf Syst 29, 305–334 (2011). https://doi.org/10.1007/s10115-010-0341-9
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DOI: https://doi.org/10.1007/s10115-010-0341-9