Joint PoS Tagging and Stemming for Agglutinative Languages
This addresses sparsity issues in NLP tasks for agglutinative languages, but it is incremental as it builds on existing HMM methods.
The paper tackles the sparsity problem in part-of-speech tagging for agglutinative languages by proposing an unsupervised Bayesian model using Hidden Markov Models for joint PoS tagging and stemming, showing that this joint approach improves PoS tagging scores for Turkish, Finnish, and English.
The number of word forms in agglutinative languages is theoretically infinite and this variety in word forms introduces sparsity in many natural language processing tasks. Part-of-speech tagging (PoS tagging) is one of these tasks that often suffers from sparsity. In this paper, we present an unsupervised Bayesian model using Hidden Markov Models (HMMs) for joint PoS tagging and stemming for agglutinative languages. We use stemming to reduce sparsity in PoS tagging. Two tasks are jointly performed to provide a mutual benefit in both tasks. Our results show that joint POS tagging and stemming improves PoS tagging scores. We present results for Turkish and Finnish as agglutinative languages and English as a morphologically poor language.