Towards Robust High Performance Word Sense Disambiguation of English Verbs Using Rich Linguistic Features

Combining Contextual Features for Word Sense Disambiguation

In this paper we present a maximum entropy Word Sense Disambiguation system we developed which performs competitively on SENSEVAL-2 test data for English verbs. We demonstrate that using richer linguistic contextual features significantly improves tagging accuracy, and compare the system’s performance with human annotator performance in light of both fine-grained and coarse-grained sense distin...

Simple Features for Chinese Word Sense Disambiguation

In this paper we report on our experiments on automatic Word Sense Disambiguation using a maximum entropy approach for both English and Chinese verbs. We compare the difficulty of the sensetagging tasks in the two languages and investigate the types of contextual features that are useful for each language. Our experimental results suggest that while richer linguistic features are useful for Eng...

Aligning Features with Sense Distinction Dimensions

In this paper we present word sense disambiguation (WSD) experiments on ten highly polysemous verbs in Chinese, where significant performance improvements are achieved using rich linguistic features. Our system performs significantly better, and in some cases substantially better, than the baseline on all ten verbs. Our results also demonstrate that features extracted from the output of an auto...

Towards High-performance Word Sense Disambiguation by Combining Rich Linguistic Knowledge and Machine Learning Approaches

Graded and Word-Sense-Disambiguation Decisions in Corpus Pattern Analysis: a Pilot Study

We present a pilot analysis of a new linguistic resource, VPS-GradeUp (available at http://hdl.handle.net/11234/1-1585). The resource contains 11,400 graded human decisions on usage patterns of 29 English lexical verbs, randomly selected from the Pattern Dictionary of English Verbs (Hanks, 200