New Trends in Natural Language Processing. Statistical Natural LanguageProcessing

摘要

The field of natural language processing (NLP) has seen a dramatic shift in bothresearch direction and methodology in the past several years. In the past, most work in computational linguistics tended to focus on purely symbolic methods. Recently, more and more work is shifting toward hybrid methods that combine new empirical corpus based methods, including the use of probabilistic and information theoretic techniques, with traditional symbolic methods. This work is made possible by the recent availability of linguistic databases that add rich linguistic annotated on to corpora of natural language text. Already, these methods have led to a dramatic improvement in the performance of a variety of NLP systems with similar improvement likely in the coming years. This paper focuses on these trends, surveying in particular three areas of recent progress: part of speech tagging, stochastic parsing and //xical semantics.