Deep Lexical Acquisition of Type Properties in Low-resource Languages: A Case Study in Wambaya

摘要

We present a case study on applying common methods for the prediction of lexical properties to a low-resource language, namely Wambaya. Leveraging a small corpus leads to a typical high-precision, low-recall system; using the Web as a corpus has no utility for this language, but a machine learning approach seems to utilise the available resources most effectively. This motivates a semi-supervised approach to lexicon extension.