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A comparison of graph-based word sense induction clustering algorithms in a pseudoword evaluation framework

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Abstract

This article presents a comparison of different Word Sense Induction (wsi) clustering algorithms on two novel pseudoword data sets of semantic-similarity and co-occurrence-based word graphs, with a special focus on the detection of homonymic polysemy. We follow the original definition of a pseudoword as the combination of two monosemous terms and their contexts to simulate a polysemous word. The evaluation is performed comparing the algorithm’s output on a pseudoword’s ego word graph (i.e., a graph that represents the pseudoword’s context in the corpus) with the known subdivision given by the components corresponding to the monosemous source words forming the pseudoword. The main contribution of this article is to present a self-sufficient pseudoword-based evaluation framework for wsi graph-based clustering algorithms, thereby defining a new evaluation measure (top2) and a secondary clustering process (hyperclustering). To our knowledge, we are the first to conduct and discuss a large-scale systematic pseudoword evaluation targeting the induction of coarse-grained homonymous word senses across a large number of graph clustering algorithms.

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Notes

  1. An ego word graph of a word w is a graph that represents the context of w in the corpus; alternatively, it can be seen as the neighbourhood of w in a word graph that globally represents the corpus. See Sect. 5.1.1 for the definition of ego word graph in our framework.

  2. http://wordnetweb.princeton.edu/perl/webwn, Miller (1995).

  3. See for example the results at task 14 of SemEval 2010 (Manandhar et al. 2010), where adjusted mutual information was introduced to correct the bias: https://www.cs.york.ac.uk/semeval2010_WSI/task_14_ranking.html.

  4. In this example a context is informally understood as the lemmatised versions of content words co-occurring with the target word. A formal definition of the kind of context used in our work will be given in Sect. 5.1.1.

  5. If \(\gamma \ne \emptyset \) and/or \(\delta \ne \emptyset \), we are actually considering a non-exhaustive partition or a subpartition, i.e., a collection of disjoint, non-empty subsets whose union is not necessarily the whole set.

  6. http://corpora.uni-leipzig.de.

  7. https://sourceforge.net/p/jobimtext/wiki/Home/.

  8. Precisely, the implementation found in https://sourceforge.net/p/jobimtext/wiki/Sense_Clustering/ with parameters: -n 200 -N 200.

  9. On this topic cf. Lyons (1968).

  10. The quintiles are the four values that divide a quantity in five parts: in this case, they are the multiples of ca 4.52, i.e., 4.52, 9.04, 13.56 and 18.08.

  11. On this graph-theoretical topic, see e.g., Haynes et al. (1998).

  12. Despite some similarities, our definition of hypergraph is different than the common graph-theoretical concept that goes by the same name, namely that of a graph \(G=(V,E)\) whose edges can be generic subsets of v. See Berge and Minieka (1973) for more details about the subject.

  13. We define the clustering of a set \({\mathcal {S}}\) as a finite collection of non-empty subsets of \({\mathcal {S}}\) whose union is the whole \({\mathcal {S}}\). In this paper, we often assume a clustering to also be a partition, i.e., that the subsets are all disjoint, but for some algorithms like MaxMax this is not always the case.

  14. barque is another word for ship, while pennywhistle is a small, inexpensive flute.

  15. A clustering coefficient of a node or a graph can be defined in different ways. The first definition of a local clustering coefficient is found in Watts and Strogatz (1998); a global one based on triangles is in Feld (1981); Karlberg (1997).

  16. The mean absolute deviation of a data set of observations is the average of the absolute values of the differences between the mean data set value and the observations (Dixon and Massey 1957).

  17. We could normalise the mad score with respect to the number of total clustered elements. However, since the order of our ego graphs is nearly constant, we just take the absolute mean deviations. The same goes for the mean number of clusters.

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  1. DISCo, Università degli Studi di Milano - Bicocca, Viale Sarca 336, Ed. U14, 20126, Milan, Italy

    Flavio Massimiliano Cecchini & Elisabetta Fersini

  2. Informatikum, Universität Hamburg, Vogt-Kölln-Straße 30, 22527, Hamburg, Germany

    Martin Riedl & Chris Biemann

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Cecchini, F.M., Riedl, M., Fersini, E. et al. A comparison of graph-based word sense induction clustering algorithms in a pseudoword evaluation framework. Lang Resources & Evaluation 52, 733–770 (2018). https://doi.org/10.1007/s10579-018-9415-1

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