Supervised Word Sense Disambiguation on Polysemy with Neural Network Models: A Case Study of BUN in Taiwan Hakka

Abstract

While word sense disambiguation (WSD) has been extensively studied in natural language processing, such a task in low-resource languages still receives little attention. Findings based on a few dominant languages may lead to narrow applications. A language-specific WSD system is in need to implement in low-resource languages, for instance, in Taiwan Hakka. This study examines the performance of DNN and Bi-LSTM in WSD tasks on polysemous BUNin Taiwan Hakka. Both models are trained and tested on a small amount of hand-crafted labeled data. Two experiments are designed with four kinds of input features and two window spans to explore what information is needed for the models to achieve their best performance. The results show that to achieve the best performance, DNN and Bi-LSTM models prefer different kinds of input features and window spans.