Word Sense Disambiguation using a Bidirectional LSTM
Table 2: Results for Senseval 2 and 3 on the English lexical sample task.
['[BOLD] Method', '[BOLD] F1 score  [BOLD] SE2', '[BOLD] F1 score  [BOLD] SE3']
100JHU(R) was developed by \newciteyarowsky2001johns and achieved the best score on the English lexical sample task of SE2 with a F1 score of 64.2. Their system utilized a rich feature space based on raw words, lemmas, POS tags, bag-of-words, bi-gram, and tri-gram collocations, etc. as inputs to an ensemble classifier. htsa3 by \newcitegrozea2004finding was the winner of the SE3 lexical sample task with a F1 score of 72.9. This system was based mainly on raw words, lemmas, and POS tags. These were used as inputs to a regularized least square classifier. IMS+adapted CW is a more recent system, by \newcitetaghipour2015semi, that uses separately trained word embeddings as input. However, it also relies on a rich set of other features including POS tags, collocations and surrounding words to achieve their reported result.