Supervised Word Sense Disambiguation with Support Vector Machines and multiple knowledge sources
Yoong Keok Lee, Hwee Tou Ng, Tee Kiah Chia · 2004
We participated in the SENSEVAL-3 English lexical sample task and multilingual lexical sample task. We adopted a supervised learning approach with Support Vector Machines, using only the official training data provided. No other external resources were used. The knowledge sources used were partof-speech of neighboring words, single words in the surrounding context, local collocations, and syntactic relations. For the translation and sense subtask of the multilingual lexical sample task, the English sense given for the target word was also used as an additional knowledge source. For the English lexical sample task, we obtained fine-grained and coarse-grained score (for both recall and precision)