Classifying Out-of-vocabulary Terms in a Domain-Specific Social Media Corpus

SoHyun Park, Afsaneh Fazly, Annie Lee, Brandon Seibel, Wenjie Zi, Paul Cook


Abstract
In this paper we consider the problem of out-of-vocabulary term classification in web forum text from the automotive domain. We develop a set of nine domain- and application-specific categories for out-of-vocabulary terms. We then propose a supervised approach to classify out-of-vocabulary terms according to these categories, drawing on features based on word embeddings, and linguistic knowledge of common properties of out-of-vocabulary terms. We show that the features based on word embeddings are particularly informative for this task. The categories that we predict could serve as a preliminary, automatically-generated source of lexical knowledge about out-of-vocabulary terms. Furthermore, we show that this approach can be adapted to give a semi-automated method for identifying out-of-vocabulary terms of a particular category, automotive named entities, that is of particular interest to us.
Anthology ID:
L16-1474
Volume:
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:
May
Year:
2016
Address:
Portorož, Slovenia
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
2971–2975
Language:
URL:
https://aclanthology.org/L16-1474
DOI:
Bibkey:
Cite (ACL):
SoHyun Park, Afsaneh Fazly, Annie Lee, Brandon Seibel, Wenjie Zi, and Paul Cook. 2016. Classifying Out-of-vocabulary Terms in a Domain-Specific Social Media Corpus. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 2971–2975, Portorož, Slovenia. European Language Resources Association (ELRA).
Cite (Informal):
Classifying Out-of-vocabulary Terms in a Domain-Specific Social Media Corpus (Park et al., LREC 2016)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/L16-1474.pdf