Enrico Mensa


2020

pdf bib
LessLex: Linking Multilingual Embeddings to SenSe Representations of LEXical Items
Davide Colla | Enrico Mensa | Daniele P. Radicioni
Computational Linguistics, Volume 46, Issue 2 - June 2020

We present LESSLEX, a novel multilingual lexical resource. Different from the vast majority of existing approaches, we ground our embeddings on a sense inventory made available from the BabelNet semantic network. In this setting, multilingual access is governed by the mapping of terms onto their underlying sense descriptions, such that all vectors co-exist in the same semantic space. As a result, for each term we have thus the “blended” terminological vector along with those describing all senses associated to that term. LESSLEX has been tested on three tasks relevant to lexical semantics: conceptual similarity, contextual similarity, and semantic text similarity. We experimented over the principal data sets for such tasks in their multilingual and crosslingual variants, improving on or closely approaching state-of-the-art results. We conclude by arguing that LESSLEX vectors may be relevant for practical applications and for research on conceptual and lexical access and competence.

2017

pdf bib
TTCS: a Vectorial Resource for Computing Conceptual Similarity
Enrico Mensa | Daniele P. Radicioni | Antonio Lieto
Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications

In this paper we introduce the TTCS, a linguistic resource that relies on BabelNet, NASARI and ConceptNet, that has now been used to compute the conceptual similarity between concept pairs. The conceptual representation herein provides uniform access to concepts based on BabelNet synset IDs, and consists of a vector-based semantic representation which is compliant with the Conceptual Spaces, a geometric framework for common-sense knowledge representation and reasoning. The TTCS has been evaluated in a preliminary experimentation on a conceptual similarity task.

pdf bib
MERALI at SemEval-2017 Task 2 Subtask 1: a Cognitively Inspired approach
Enrico Mensa | Daniele P. Radicioni | Antonio Lieto
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

In this paper we report on the participation of the MERALI system to the SemEval Task 2 Subtask 1. The MERALI system approaches conceptual similarity through a simple, cognitively inspired, heuristics; it builds on a linguistic resource, the TTCS-e, that relies on BabelNet, NASARI and ConceptNet. The linguistic resource in fact contains a novel mixture of common-sense and encyclopedic knowledge. The obtained results point out that there is ample room for improvement, so that they are used to elaborate on present limitations and on future steps.