Paper Info
Title
One Single Deep Bidirectional LSTM Network for Word Sense Disambiguation of Text Data.
Abstract
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.
Year
DOI
Venue
2018
10.1007/978-3-319-89656-4_8
Canadian Conference on AI
DocType
Volume
Citations 
Conference
abs/1802.09059
0
PageRank 
References 
Authors
0.34
23
4
Name
Order
Citations
PageRank
Ahmad Pesaranghader1284.20
Ali Pesaranghader2293.16
Stan Matwin33025344.20
Marina Sokolova472028.40