Research Area: Machine Learning
Word sense disambiguation is a process to correctly identify the meanings of words in a given context. Being important in many natural language processing applications, this process is crucial in automatically understanding natural language expressions. Herein, we propose a variation of a well-known unsupervised graph-based word sense disambiguation method that utilizes all possible semantic information from a used lexical resource to increase graph-semantic connectivity for identifying the intended meanings of words in a given context. If the words have multiple potential meanings (senses) based on context, the proposed method builds an expanded graph representing most relevant semantic information of the words to be disambiguated. Nodes in the graph correspond to the context expansion set, which contains all associated information of each possible meaning of the word (word sense), and edges represent the semantic similarity between the expanded sets (nodes). Simultaneously, actual meaning is assigned to each target word using a locate graph centrality measure, which provides the degree of importance between graph nodes. Unlike most existing graph-based word sense disambiguation methods, wherein semantic relations (edges) between nodes are measured at the word level, the proposed method measures graph node semantic relations at the sentence level by expanding the words’ context, which contains all associated information for each possible word sense. Consequently, the proposed method can capture a higher degree of semantic information than existing approaches, thereby increasing semantic connectivity through a graph’s edges. Empirical results on benchmark datasets demonstrate that the proposed method outperforms all compared state-of-the-art graph-based word sense disambiguation approaches reported herein. We also report results obtained by applying the proposed method to a sentiment analysis task. These results demonstrate that the proposed method can determine the overall sentiment orientation of a given textual context.
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Author(s) Name: Khaled Abdalgader, Aysha Al Shibli
Journal name: Expert Systems with Applications
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Publisher name: Elsevier
DOI: 10.1016/j.eswa.2020.114313
Volume Information: Volume 168, 15 April 2021, 114313
