NUS-PT: exploiting parallel texts for word sense disambiguation in the English all-words tasks

  • Authors:

  • Yee Seng Chan;Hwee Tou Ng;Zhi Zhong

  • Affiliations:

  • National University of Singapore, Singapore;National University of Singapore, Singapore;National University of Singapore, Singapore

  • Venue:
  • SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
  • Year:
  • 2007

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Abstract

We participated in the SemEval-2007 coarse-grained English all-words task and fine-grained English all-words task. We used a supervised learning approach with SVM as the learning algorithm. The knowledge sources used include local collocations, parts-of-speech, and surrounding words. We gathered training examples from English-Chinese parallel corpora, SemCor, and DSO corpus. While the fine-grained sense inventory of WordNet was used to train our system employed for the fine-grained English all-words task, our system employed for the coarse-grained English all-words task was trained with the coarse-grained sense inventory released by the task organizers. Our scores (for both recall and precision) are 0.825 and 0.587 for the coarse-grained English all-words task and fine-grained English all-words task respectively. These scores put our systems in the first place for the coarse-grained English all-words task and the second place for the fine-grained English all-words task.