Word Sense Disambiguation With Massive Contextual Texts

Word sense disambiguation is crucial in natural language processing. Both unsupervised knowledge-based and supervised methodologies try to disambiguate ambiguous words through context. However, they both suffer from data sparsity, a common problem in natural language. Furthermore, the supervised methods are previously limited in the all-word WSD tasks. This paper attempts to collect all publicly available contexts to enrich the ambiguous word's sense representation and apply these contexts to the simplified Lesk and our M-IMS systems. Evaluations performed on the concatenation of several benchmark fine-grained all-word WSD datasets show that the simplified Lesk improves by 9.4% significantly and our M-IMS has shown some improvement as well.