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

Autor:	Pesaranghader, Ahmad, Pesaranghader, Ali, Matwin, Stan, Sokolova, Marina
Rok vydání:	2018
Předmět:	Computer Science - Learning Computer Science - Computation and Language Computer Science - Information Retrieval Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Due to recent technical and scientific advances, we have a wealth of information 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 one language or technical domain, we may encounter limiting constraints for proper deployment of existing WSD models. This paper attempts to address the problem of one-classifier-per-one-word WSD algorithms by proposing a single Bidirectional Long Short-Term Memory (BLSTM) network which by considering senses and context sequences works on all ambiguous words collectively. Evaluated on SensEval-3 benchmark, we show the result of our model is comparable with top-performing WSD algorithms. We also discuss how applying additional modifications alleviates the model fault and the need for more training data. Comment: 12 pages, 1 figure, to appear in the Proceedings of the 31st Canadian Conference on Artificial Intelligence, 8-11 May, 2018, Toronto, Canada
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1802.09059 Zobrazit plný text záznamu View this record from Arxiv