Enrico Mensa

A Resource-Driven Approach for Anchoring Linguistic Resources to Conceptual Spaces

In this paper we introduce the ttcs system, so named after Terms To Conceptual Spaces, that exploits a resource-driven approach relying on BabelNet, NASARI and ConceptNet. ttcs takes in input a term and its context of usage and produces as output a specific type of vector-based semantic representation, where conceptual information is encoded through the Conceptual Spaces a geometric framework for common-sense knowledge representation and reasoning. The system has been evaluated in a twofold experimentation. In the first case we assessed the quality of the extracted common-sense conceptual information with respect to human judgments with an online questionnaire. In the second one we compared the performances of a conceptual categorization system that was run twice, once fed with extracted annotations and once with hand-crafted annotations. In both cases the results are encouraging and provide precious insights to make substantial improvements.

Trait-oriented programming in Java 8

Java 8 was released recently. Along with lambda expressions, a new language construct is introduced: default methods in interfaces. The intent of this feature is to allow interfaces to be extended over time preserving backward compatibility. In this paper, we show a possible, different use of these interfaces: we introduce a trait-oriented programming style based on an interface-as-trait idea, with the aim of improving code modularity. Starting from the most common operators on traits, we introduce some programming patterns mimicking such operators and discuss this approach.

Towards a unifying framework for conceptual represention and reasoning in cognitive systems

In this paper we present the rationale adopted for the integration of the knowledge level of DUAL-PECCS, a cognitive system for conceptual representation and categorization, with two different cognitive architectures: SOAR and LIDA. In previous works we already showed how the representational and reasoning framework adopted in DUAL-PECCS was integrable with diverse cognitive architectures, i.e. ACT-R and CLARION, making different representational assumptions and adopting diverse knowledge processing mechanisms. The additional integrations presented here suggest that the underlying knowledge representation and reasoning structure adopted in DUAL-PECCS can be used as a unifying framework for the knowledge level of agents endowed with different cognitive architectures. The current version of the system has been experimentally assessed in a task of conceptual categorization where a target concept illustrated by a simple common-sense linguistic description had to be identified by resorting to a mix of categorization strategies. The output has then been compared to human and artificial responses. The novel integration allowed us to extend our previous evaluation.

Semantic Measures for Keywords Extraction

In this paper we introduce a minimalist hypothesis for keywords extraction: keywords can be extracted from text documents by considering concepts underlying document terms. Furthermore, central concepts are individuated as the concepts that are more related to title concepts. Namely, we propose five metrics, that are diverse in essence, to compute the centrality of concepts in the document body with respect to those in the title. We finally report about an experimentation over a popular data set of human annotated news articles; the results confirm the soundness of our hypothesis.

Tell Me Why: Computational Explanation of Conceptual Similarity Judgments

In this paper we introduce a system for the computation of explanations that accompany scores in the conceptual similarity task. In this setting the problem is, given a pair of concepts, to provide a score that expresses in how far the two concepts are similar. In order to explain how explanations are automatically built, we illustrate some basic features of COVER, the lexical resource that underlies our approach, and the main traits of the MeRaLi system, that computes conceptual similarity and explanations, all in one. To assess the computed explanations, we have designed a human experimentation, that provided interesting and encouraging results, which we report and discuss in depth.

Semantic Models for the Geological Mapping Process

The geologic mapping process requires the organization of data according tothe general knowledge about the objects in the map, namely the geologic units, and tothe objectives of a graphic representation of such objects in a map, following some established model of geotectonic evolution. Semantics can greatly help such a process in providing a terminological base to name and classify the objects of the map and supporting the application of reasoning mechanisms for the derivation of novel properties and relations about the objects of the map.

COVER: a linguistic resource combining common sense and lexicographic information

Lexical resources are fundamental to tackle many tasks that are central to present and prospective research in Text Mining, Information Retrieval, and connected to Natural Language Processing. In this article we introduce COVER, a novel lexical resource, along with COVERAGE, the algorithm devised to build it. In order to describe concepts, COVER proposes a compact vectorial representation that combines the lexicographic precision characterizing BabelNet and the rich common-sense knowledge featuring ConceptNet. We propose COVER as a reliable and mature resource, that has been employed in as diverse tasks as conceptual categorization, keywords extraction, and conceptual similarity. The experimental assessment is performed on the last task: we report and discuss the obtained results, pointing out future improvements. We conclude that COVER can be directly exploited to build applications, and coupled with existing resources, as well.

Grasping Metaphors: Lexical Semantics in Metaphor Analysis.

Metaphors represent to date an extraordinary challenge for computational linguistics. Dealing with metaphors has relevant consequences on our ability to build agents and systems that understand Natural Language and text documents: annotating metaphoric constructions by linking the metaphor elements to existing resources is a crucial step to make text documents more easily accessible by machines. Our approach tackles metaphors by considering concepts and their abstractness. We report the encouraging results obtained in a preliminary experimentation; we elaborate on present limitations, and individuate the needed improvements, which will be the base for future work.