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:
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/L16-1474.pdf