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Due to recent technical and scientific advances, we have a wealth ofinformation hidden in unstructured text data such as offline/online narratives,research articles, and clinical reports. To mine these data properly,attributable to their innate ambiguity, a Word Sense Disambiguation (WSD)algorithm can avoid numbers of difficulties in Natural Language Processing(NLP) pipeline. However, considering a large number of ambiguous words in onelanguage or technical domain, we may encounter limiting constraints for properdeployment of existing WSD models. This paper attempts to address the problemof one-classifier-per-one-word WSD algorithms by proposing a singleBidirectional Long Short-Term Memory (BLSTM) network which by consideringsenses and context sequences works on all ambiguous words collectively.Evaluated on SensEval-3 benchmark, we show the result of our model iscomparable with top-performing WSD algorithms. We also discuss how applyingadditional modifications alleviates the model fault and the need for moretraining data.

One Single Deep Bidirectional LSTM Network for Word Sense Disambiguation of Text Data