Semantic association computation: a comprehensive survey

Abstract

Semantic association computation is the process of quantifying the strength of a semantic connection between two textual units, based on different types of semantic relations. Semantic association computation is a key component of various applications belonging to a multitude of fields, such as computational linguistics, cognitive psychology, information retrieval and artificial intelligence. The field of semantic association computation has been studied for decades. The aim of this paper is to present a comprehensive survey of various approaches for computing semantic associations, categorized according to their underlying sources of background knowledge. Existing surveys on semantic computation have focused on a specific aspect of semantic associations, such as utilizing distributional semantics in association computation or types of spatial models of semantic associations. However, this paper has put a multitude of computational aspects and factors in one picture. This makes the article worth reading for those researchers who want to start off in the field of semantic associations computation. This paper introduces the fundamental elements of the association computation process, evaluation methodologies and pervasiveness of semantic measures in a variety of fields, relying on natural language semantics. Along the way, there is a detailed discussion on the main categories of background knowledge sources, classified as formal and informal knowledge sources, and the underlying design models, such as spatial, combinatorial and network models, that are used in the association computation process. The paper classifies existing approaches of semantic association computation into two broad categories, based on their utilization of background knowledge sources: knowledge-rich approaches; and knowledge-lean approaches. Each category is divided further into sub-categories, according to the type of underlying knowledge sources and design models of semantic association. A comparative analysis of strengths and limitations of various approaches belonging to each research stream is also presented. The paper concludes the survey by analyzing the pivotal factors that affect the performance of semantic association measures.

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Fig. 1

Notes

  1. 1.

    A measure for assessing the degree to which two or more raters agree on assigning data to different categories

  2. 2.

    Further details can be found on http://nltk.googlecode.com/svn/trunk/nltk_data/index.xml and https://catalog.ldc.upenn.edu/byproject.

  3. 3.

    https://en.wiktionary.org/wiki/Wiktionary:Statistics.

  4. 4.

    Freely available at http://wordnet.princeton.edu/wordnet/.

  5. 5.

    http://www.cyc.com/.

  6. 6.

    http://www.britannica.com/.

  7. 7.

    http://en.wikipedia.org/.

  8. 8.

    http://www.nlm.nih.gov/medlineplus/.

  9. 9.

    http://www.nlm.nih.gov/mesh/.

  10. 10.

    The layout of this section is devised following the work of Mohammad and Hirst (2012). However, they focused only on distributional measures of similarity.

  11. 11.

    http://wacky.sslmit.unibo.it/doku.php?id=corpora.

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Jabeen, S., Gao, X. & Andreae, P. Semantic association computation: a comprehensive survey. Artif Intell Rev 53, 3849–3899 (2020). https://doi.org/10.1007/s10462-019-09781-w

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Keywords

  • Semantic association computation
  • Knowledge sources
  • Semantic similarity
  • Semantic relatedness
  • Semantic distance
  • Semantic models