Abstract
Automatic methods for wordnet development in languages other than English generally exploit information found in Princeton WordNet (PWN) and translations extracted from parallel corpora. A common approach consists in preserving the structure of PWN and transferring its content in new languages using alignments, possibly combined with information extracted from multilingual semantic resources. Even if the role of PWN remains central in this process, these automatic methods offer an alternative to the manual elaboration of new wordnets. However, their limited coverage has a strong impact on that of the resulting resources. Following this line of research, we apply a cross-lingual word sense disambiguation method to wordnet development. Our approach exploits the output of a data-driven sense induction method that generates sense clusters in new languages, similar to wordnet synsets, by identifying word senses and relations in parallel corpora. We apply our cross-lingual word sense disambiguation method to the task of enriching a French wordnet resource, the WOLF, and show how it can be efficiently used for increasing its coverage. Although our experiments involve the English–French language pair, the proposed methodology is general enough to be applied to the development of wordnet resources in other languages for which parallel corpora are available. Finally, we show how the disambiguation output can serve to reduce the granularity of new wordnets and the degree of polysemy present in PWN.
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Notes
In these projects, the expand model was occasionally combined with the merge model which is based on monolingual resources and permits to include language-specific properties in the wordnets of different languages.
The BabelNet resource is available here: http://babelnet.org.
Compared to the initial version of WOLF (0.1.4), version 0.1.6 has an extended coverage on adverbs as a result of the work by Sagot et al. (2009).
Sentence pairs with a great difference in length, where one sentence is more than three times longer than the corresponding sentence in the other language.
The weights of the features are omitted for the sake of readability.
The table does not include information on all the neighboring PWN synsets, which was used during WSD. This information can however be easily recovered from PWN.
All accuracy scores reported for our system in Table 5 have been computed with respect to the judgments of the two annotators. More precisely, we first computed an accuracy score separately for each annotator and then retained the average of the two scores.
This information can also be highly useful for the evaluation of WSD systems as it would permit to penalize differently WSD errors involving close and distant senses (Resnik and Yarowsky 1999).
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Apidianaki, M., Sagot, B. Data-driven synset induction and disambiguation for wordnet development. Lang Resources & Evaluation 48, 655–677 (2014). https://doi.org/10.1007/s10579-014-9291-2
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DOI: https://doi.org/10.1007/s10579-014-9291-2