Word Relatives in Context for Word Sense Disambiguation

The current situation for Word Sense Disambiguation (WSD) is somewhat stuck due to lack of training data. We present in this paper a novel disambiguation algorithm that improves previous systems based on acquisition of examples by incorporating local context information. With a basic configuration, our method is able to obtain state-of-the-art performance. We complemented this work by evaluating other well-known methods in the same dataset, and analysing the comparative results per word. We observed that each algorithm performed better for different types of words, and each of them failed for some particular words. We proposed then a simple unsupervised voting scheme that improved significantly over single systems, achieving the best unsupervised performance on both the Senseval 2 and Senseval 3 lexical sample datasets.