Taming Sense Sparsity:a Common-Sense Approach
Authors: Antonio Lieto, Enrico Mensa, Daniele P. Radicioni (Università di Torino, 2016)
Topic: Lexical and Semantic Resources and Analysis

Abstract
We present a novel algorithm and a linguistic resource named \SST after `Common SEnse STrainer'. The resource contains a list of the main senses associated to a given term, and it was obtained by applying a simple set of pruning heuristics to the senses provided in the NASARI vectors for the set of $15K$ most frequent English terms. The preliminary experimentation provided encouraging results.

Introduction
Many NLP tasks involve word sense disambiguation (WSD) and word sense induction (WSI), and require using lexical resources such as WordNet AUTHOR and BabelNet AUTHOR that provide a rich mapping of terms (or word forms) onto the corresponding senses (word meanings). These widely used resources provide in fact subtle distinctions between the possible senses of a term. It is largely acknowledged that while fine-grained sense distinctions are necessary for some precise tasks (such as machine translation), for other sorts of applications (such as text categorization and information extraction) coarse-grained sense inventories are preferable AUTHOR. In these cases, fine-grained distinctions may be unnecessary and even detrimental to WSD and WSI, so that in the last few years many efforts concentrated on clustering senses. Most works focused on producing coarser-grained sense inventories, to the ends of grouping together the closest (partially overlapped) senses of a word; to these ends, various techniques have been carried out, that are briefly surveyed in Section related-work. Differently from existing approaches, we propose a simple yet effective method that relies on recently developed resources that are assumed to also grasp common-sense knowledge AUTHOR, which is assumed to be both widely accessible and elementary knowledge AUTHOR, and to reflect typicality traits encoded as prototypical knowledge AUTHOR. The research question presently addressed is thus: To what extent can we individuate few principal ---common-sense--- senses for a term, and in how far is it possible to approximate human performance? Although it is known that even human annotators provide quite different response when annotating text with senses AUTHOR, we presently explore the hypothesis that wide-coverage resources are sufficient to individuate the main senses associated to English terms.

Conclusion
In this paper we have illustrated the \SST algorithm to extract the most salient (under the common-sense perspective) senses associated to a given term; also, we have introduced the \SST resource, which has been built by starting from the 15K top frequency English terms. The resource currently provides senses in a flat manner, but, if required, senses can be organized in a sorted fashion by extending the metrics used for filtering. Our work relies on a recently developed resource such as NASARI that is multilingual in nature.-linguistic perspective, which is a well-known issue, e.g., for applications in the legal field AUTHOR. Consequently, different from most previous approaches, \SST can be linked to various existing resources aimed at grasping common-sense to complete the ideal chain connecting lexicon, semantics and formal (ontological) description. The experimentation revealed a reasonable agreement with human responses, and pointed out some difficulties in fully assessing this sort of resource. These issues, along with improvements to the heuristics implemented by the algorithm and a different evaluation based on a shared NLP task, will be addressed in future work.