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Smart Context Generation for Disambiguation to Wikipedia

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Artificial Intelligence and Natural Language (AINL 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 930))

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Abstract

Wikification is a crucial NLP task that aims to identify entities in text and disambiguate their meaning. Being partially solved for English, the problem still remains fairly untouched for Russian. In this article we present a novel approach to Disambiguation to Wikipedia applied to the Russian language. Inspired by the Neural Machine Translation task our method implements encoder-decoder neural network architecture. It translates text tokens into concept embeddings that are subsequently used as context for disambiguation. In order to test our hypothesis we add our context features to GLOW system considered a baseline. Moreover, we present commonly available dataset for the Disambiguation to Wikipedia task.

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Notes

  1. 1.

    https://github.com/ispras-texterra/ainl-2018-d2w-dataset.

  2. 2.

    Note, that token embedding size is \(101 = 100 + \) extra position to encode END_TOKEN. Similar idea is for concept embedding size and START_CONCEPT/END_CONCEPT.

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Correspondence to Andrey Sysoev .

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Sysoev, A., Nikishina, I. (2018). Smart Context Generation for Disambiguation to Wikipedia. In: Ustalov, D., Filchenkov, A., Pivovarova, L., Žižka, J. (eds) Artificial Intelligence and Natural Language. AINL 2018. Communications in Computer and Information Science, vol 930. Springer, Cham. https://doi.org/10.1007/978-3-030-01204-5_2

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  • DOI: https://doi.org/10.1007/978-3-030-01204-5_2

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-01204-5

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