Abstract
Wikification (entity annotation) is a challenging task in Natural Language Processing (NLP). It is a method to automatically enrich a text with links to Wikipedia as a knowledge base. Wikification starts from detecting ambiguous mentions in the document, and later tries to disambiguate those mentions. In the core of the Wikification task, there is one other important NLP task: word representation. This paper proposes a new word representation for senses of a mention with Graph convolutional networks architecture. Senses are the possible meanings of one mention, based on the knowledge base. In our representation modeling, we used the context document and the first paragraph of each Wikipedia page to enhance our contextual representation. Using the nearest neighbor algorithm for disambiguating the mentions via our sense representations, we show the efficiency of our representations. The results of comparing our method with recent state-of-the-art methods show the efficiency of our solution.
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Notes
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A mention can be one or more tokens.
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The notation we used for GCN in this paper are the same as notations in [59].
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We used this dataset of the second category from: https://github.com/asajadi/wikisim/tree/master/datasets.
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Saeidi, M., Milios, E., Zeh, N. (2021). Graph Representation Learning in Document Wikification. In: Barney Smith, E.H., Pal, U. (eds) Document Analysis and Recognition – ICDAR 2021 Workshops. ICDAR 2021. Lecture Notes in Computer Science(), vol 12917. Springer, Cham. https://doi.org/10.1007/978-3-030-86159-9_37
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