ROMJIST Volume 21, No. 4, 2018, pp. 446-459
Aliaksei KOLESAU, Dmitrij ŠEŠOK, Mindaugas RYBOKAS A Character-Based Part-of-Speech Tagger with Feedforward Neural Networks
ABSTRACT: This article presents a simple method to perform part-of-speech (POS) tagging with feedforward neural networks applied to learnable character embeddings. The motivation of the research is based on the fact that for some languages a human can find out the part of speech for a word just by its spelling even without knowing the meaning of the word (see C. Fries’s example “woggles ugged diggles”). One of the goals was to achieve high accuracy tagging without using semantic information (e.g. without word embeddings). This allows performing tagging for out of vocabulary words. Also, the dependency of the performance from context size was studied. The plausibility of the method was proved by building a POS-tagger with the accuracy comparable to state-of-the-art resultsKEYWORDS: POS tagging, character embeddings, feedforward neural networksRead full text (pdf)