Decision Trees and NLP: A Case Study in POS Tagging
Giorgos Orphanos, Dimitris Kalles, Thanasis Papagelis, Dimitris Christodoulakis · 2009
This paper presents a machine learning approach to the problems of part-of-speech disambiguation and unknown word guessing, as they appear in Modern Greek. Both problems are cast as classification tasks carried out by decision trees. The data model acquired is capable of capturing the idiosyncratic behavior of underlying linguistic phenomena. Decision trees are induced with three algorithms; the first two produce generalized trees, while the third produces binary trees. To meet the requirements of the linguistic datasets, all three algorithms are able to handle set-valued attributes. Evaluation results reveal a subtle differentiation in the performance of the three algorithms, which achieve an accuracy range of 93-95% in POS disambiguation and 82-88% in guessing the POS of unknown words.