NUS-PT: Exploiting Parallel Texts for Word Sense Disambiguation in the English All-Words Tasks

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 task1 and the second place for the fine-grained English all-words task.