Analyzing word embeddings and improving POS tagger of tigrinya

摘要

In this paper, we analyze word embeddings for a morphologically rich language, Tigrinya. Tigrinya is a Semitic language spoken natively in Eritrea and Ethiopia by over seven million people. The unique and complex morphology of Semitic languages, which includes Arabic, Amharic, and Hebrew, is commonly known as 'root and template pattern' morphology. This morphology generates a large number of inflected forms that often cause out-of-vocabulary (OOV) challenges in language processing. This problem is more challenging for low resource languages, such as Tigrinya, that offers very little support of annotated resources. Word embedding methods, given a large raw text corpus, form semantic and syntactic vector representation of words. Therefore, we construct a new text corpus and investigate the optimal settings for generating word vectors for Tigrinya. We also utilize word embeddings to improve the performance of a Tigrinya part-of-speech tagger created from a small tagged corpus.