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Approaching terminological ambiguity in cross-disciplinary communication as a word sense induction task: a pilot study


Cross-disciplinary communication is often impeded by terminological ambiguity. Hence, cross-disciplinary teams would greatly benefit from using a language technology-based tool that allows for the (at least semi-) automated resolution of ambiguous terms. Although no such tool is readily available, an interesting theoretical outline of one does exist. The main obstacle for the concrete realization of this tool is the current lack of an effective method for the automatic detection of the different meanings of ambiguous terms across different disciplinary jargons. In this paper, we set up a pilot study to experimentally assess whether the word sense induction technique of ‘context clustering’, as implemented in the software package ‘SenseClusters’, might be a solution. More specifically, given several sets of sentences coming from a cross-disciplinary corpus containing a specific ambiguous term, we verify whether this technique can classify each sentence in accordance to the meaning of the ambiguous term in that sentence. For the experiments, we first compile a corpus that represents the disciplinary jargons involved in a project on Bone Tissue Engineering. Next, we conduct two series of experiments. The first series focuses on determining appropriate SenseClusters parameter settings using manually selected test data for the ambiguous target terms ‘matrix’ and ‘model’. The second series evaluates the actual performance of SenseClusters using randomly selected test data for an extended set of target terms. We observe that SenseClusters can successfully classify sentences from a cross-disciplinary corpus according to the meaning of the ambiguous term they contain. Hence, we argue that this implementation of context clustering shows potential as a method for the automatic detection of the meanings of ambiguous terms in cross-disciplinary communication.

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  1. Note that, in this paper, we remain agnostic with respect to the relation between ambiguity and related phenomena like polysemy, fuzziness, vagueness and generality.

  2. There are many general concerns about the use of sense inventory based approaches. For example, there is the difficulty of demarcating the semantic information that should be included in a sense description, and that of distinguishing between closely related senses (Edmonds and Kilgarriff 2002). Relying on sense inventories is particularly problematic in the context of CD projects. Such projects require the compilation of a custom inventory by selecting sense descriptions from existing ‘disciplinary’ sense inventories. Yet, it is unclear how one can ensure that relevant sense descriptions are selected from relevant sense inventories. Moreover, new sense descriptions would need to be developed for terms that are not included in existing inventories.

  3. In this paper, we use the notions ‘word’ and ‘term’ interchangeably, though we use the latter especially when we want to stress that a lexical unit has a meaning.

  4. For more information, go to

  5. The more specialized a corpus is, the less broad definitions it contains. This means that references to more general or high-level components of the meanings of terms will be scarce, and thus are less likely to be picked up by means of a context clustering technique.

  6. By spanning different disciplines, the corpus becomes highly variegated as one meaning (e.g. ‘having the capacity to cause rotation’) will often be referred to by different terms (e.g. ‘couple’ in kinematics and ‘force’ in kinetics). This poses a challenge for context clustering, as not only term ambiguity is present but also (latent) synonymy.

  7. For more information, go to

  8. The sub-corpora do not perfectly mirror the original texts as the accuracy of the recognition results was only sanity-checked.

  9. The underlying reason is that SenseClusters is based on the distributional hypothesis as mentioned earlier in Section 2. See also Subsection 3.4.

  10. We define ‘best result’ as the highest accuracy.

  11. Because the settings combination of feature type ‘co-occurrences’ and a window size of ‘6’ yielded better results than the settings combination of feature type ‘co-occurrence’ and a window size of ‘3’, we omitted the latter combination of parameter settings in the fourth round of experiments.



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The work presented in this paper was carried out in the context of a PhD fellowship funded by the Research Foundation—Flanders (FWO). We thank Prof. Dr. Liesbet Geris for sharing her cross-disciplinary experiences as the Scientific Coordinator of Prometheus and providing us with the necessary information for the corpus compilation. We also want to thank Prof. Dr. Stephan van der Waart van Gulik for his constructive feedback which helped to improve the paper significantly.

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Mennes, J., Pedersen, T. & Lefever, E. Approaching terminological ambiguity in cross-disciplinary communication as a word sense induction task: a pilot study. Lang Resources & Evaluation 53, 889–917 (2019).

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  • Cross-disciplinary communication
  • Disambiguation
  • Word sense induction
  • SenseClusters
  • Terminological ambiguity