Abstract
Word sense disambiguation (WSD) is a task of determining a reasonable sense of a word in a particular context. Although recent studies have demonstrated some progress in the advancement of neural language models, the scope of research is still such that the senses of several words can only be determined in a few domains. Therefore, it is necessary to move toward developing a highly scalable process that can address a lot of senses occurring in various domains. This paper introduces a new large WSD dataset that is automatically constructed from the Oxford Dictionary, which is widely used as a standard source for the meaning of words. We propose a new WSD model that individually determines the sense of the word in accordance with its part of speech in the context. In addition, we introduce a hybrid sense prediction method that separately classifies the less frequently used senses for achieving a reasonable performance. We have conducted comparative experiments to demonstrate that the proposed method is more reliable compared with the baseline approaches. Also, we investigated the adaptation of the method to a realistic environment with the use of news articles.
Highlights
Natural language understanding refers to a series of processes in which a computer reads a text written in human language, analyzes it, and facilitates the reasoning to generate new information [31]
In the field of natural language processing, Word sense disambiguation (WSD) refers to a task that determines a reasonable sense of a word that can have multiple meanings or seems to be ambiguous in a given context
PROPOSED MODEL FOR WORD SENSE DISAMBIGUATION we propose a new WSD model that can effectively address large amounts of versatile WSD data automatically constructed from the Oxford Dictionary
Summary
Natural language understanding refers to a series of processes in which a computer reads a text written in human language, analyzes it, and facilitates the reasoning to generate new information [31]. The requirement for classification is the contextual information for the word in the sentence To address this problem, [16], [39], [48] proposed models for classifying word senses based on traditional machine learning algorithms. These studies define the context information of a target word as the word’s part of speech and its surrounding information. While frequently used senses often have a relatively large number of example sentences, there may be very few examples for rarely encountered senses Because of this balance problem, our dictionary-based dataset makes it difficult for the neural language models to learn the accurate contextual information involving rare senses.
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