Vietnamese Noun Phrase Chunking Based on Conditional Random Fields

Abstract

Noun phrase chunking is an important and useful task in many natural language processing applications. It is studied well for English, however with Vietnamese it is still an open problem. This paper presents a Vietnamese Noun Phrase chunking approach based on Conditional random fields (CRFs) models. We also describe a method to build Vietnamese corpus from a set of hand annotated sentences. For evaluation, we perform several experiments using different feature settings. Outcome results on our corpus show a high performance with the average of recall and precision 82.72% and 82.62% respectively.