Classifying Out-of-vocabulary Terms in a Domain-Specific Social Media Corpus
So‐Hyun Park, Afsaneh Fazly, Annie Lee, Brandon Seibel, Wenjie Zi, Paul F. Cook · 2016
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.