Predicting morphologically-complex unknown words in Igbo

Abstract

The effective handling of previously unseen words is an important factor in the performance of part-of-speech taggers. Some trainable POS taggers use suffix (sometimes prefix) strings as cues in handling unknown words (in effect serving as a proxy for actual linguistic affixes). In the context of creating a tagger for the African language Igbo, we compare the performance of some existing taggers, implementing such an approach, to a novel method for handling morphologically complex unknown words, based on morphological reconstruction (i.e. a linguistically-informed segmentation into root and affixes). The novel method outperforms these other systems by several percentage points, achieving accuracies of around 92 % on morphologically-complex unknown words.