Annibale Elia

Terminology and Knowledge Representation. Italian Linguistic Resources for the Archaeological Domain

Knowledge representation is heavily based on using terminology, due to the fact that many terms have precise meanings in a specific domain but not in others. As a consequence, terms becomes unambiguous and clear, and at last, being useful for conceptualizations, are used as a starting point for formalizations. Starting from an analysis of problems in existing dictionaries, in this paper we present formalized Italian Linguistic Resources (LRs) for the Archaeological domain, in which we integrate/couple formal ontology classes and properties into/to electronic dictionary entries, using a standardized conceptual reference model. We also add Linguistic Linked Open Data (LLOD) references in order to guarantee the interoperability between linguistic and language resources, and therefore to represent knowledge.

Multimodal Deception Detection: A t-pattern Approach

This work proposes a new approach to deception detection, based on finding significant differences between liars and truth tellers through the analysis of their behavior, verbal and non-verbal. This is based on the combination of two factors: multimodal data collection, and t-pattern analysis. Multimodal approach has been acknowledged in literature about deception detection and on several studies concerning the understanding of any communicative phenomenon. We believe a methodology such as T-pattern analysis could be able to get the best advantages from an approach that combines data coming from multiple signaling systems. In fact, T-pattern analysis is a recent methodology for the analysis of behavior that unveil the complex structure at the basis of the organization of human behavior. For this work, we conducted an experimental study and analyzed data related to a single subject. Results showed how T-pattern analysis allowed to find differences between truth telling and lying. This work aims at making progress in the state of knowledge about deception detection, with the final goal to propose a useful tool for the improvement of public security and well-being.

How to Populate Ontologies

The Cultural Heritage (CH) domain brings critical challenges as for the application of Natural Language Processing (NLP) and ontology population (OP) techniques. Actually, CH embraces a wide range of content, variable by type and properties and semantically interlinked whit other domains.This paper presents an on-going research on language treatment based on Lexicon-Grammar (LG) approach for improving knowledge management in the CH domain. We intend to show how our language formalization technique can be applied for both processing and populating a domain ontology.

NooJ Local Grammars for Innovative Startup Language

In this work, we take a linguistic knowledge approach to identify innovative language used to describe new entrepreneurial activity. Starting from the Theory of the Speech Acts, an innovative startup needs to express itself through an innovative language continuously upgraded, to describe processes and products. We use several levels of NooJ to process the type of knowledge presented in business documents to recognize new linguistic resources. This approach, based on Lexicon-Grammar (LG) framework, is extendible to every knowledge domain although we process business documents in Agri-food sector that more than others presents critical issues in knowledge management and representation. Regarding the results of our analysis with NooJ, we note that 10% of tokens retrieved are unknown words.

Automatic Population of Italian Medical Thesauri: A Morphosemantic Approach

In the age of Semantic Web, one of the most valuable challenges is the one connected with the information extraction from raw data. Information must be managed with sophisticated linguistic and computational architectures, which are able to approach the semantic dimension of words and sentences. In this paper we propose a morphosemantic method for the automatic creation and population of medical lexical resources. Our approach is grounded on a list of neoclassical formative elements pertaining to the medical domain an on a large sized corpus of medical diagnoses. The outcomes of this work are automatically built electronic dictionaries and thesauri and an annotated corpus for the NLP in the medical domain.

Morphosemantic strategies for the automatic enrichment of Italian lexical databases in the medical domain

Because of the importance of the information conveyed by the clinical documents and owing to the large quantity of raw texts produced in the healthcare system, it became a determinant challenge, in the NLP research field, to arrange the extraction and the management of meaningful data, starting from real text occurrences. In this paper we approach a corpus of 5000 medical diagnoses with sophisticated linguistic and computational devices, which are able to access the semantic dimension of words and sentences contained in it. Our morphosemantic method is grounded on a list of neoclassical formative elements pertaining to the medical domain which has been used for the automatic creation and population of medical lexical resources. The outcomes of this work are automatically built electronic dictionaries and thesauri and an annotated corpus for the NLP in the medical domain.

Semantic Predicates in the Business Language

In recent years, the interest in the use of language for business has grown. It is recognized that the hidden persuasive linguistic potential improves the company’s positioning in the public consciousness. The language of the business world is multifarious: we try to identify its features and behaviour, considering the evolution that it has faced primarily with the globalization of markets. Business activities are so complex that they require the application of several disciplines at the same time and therefore the use of specific languages and technical terminology. In order to reach an efficient analysis of business language, this study explores the role of semantic predicates constructed from lexical and the syntactic structures in which they are placed within business communication contexts. From the point of view of LG framework, a set of lexical-syntactic structures defines the value of semantic predicates, while the arguments selected by each semantic predicate are given the value of actants, subjects included. The features of each verb are expressed by the application of the rules of co-occurrence and selection restriction, through which verbs select semantically their arguments to construct acceptable simple sentences. In this way, the entries belonging to electronic dictionaries should be classified presuming their similarity and proximity. Even if the list of semantic tags is not simply identifiable, grammars could be built for single sets of semantic predicates. LG descriptions assign correlated predicates and arguments by applying electronic dictionaries of Italian. Using NooJ environment and Italian linguistic resources to automatically processing natural language, we will process a corpus of business documents. We will show and describe the syntactic structures, semantic and syntactic properties of predicates, in order to build formal grammar for business language.

Morphological Analysis and Generation of Monolingual and Bilingual Medical Lexicons

To efficiently extract and manage extremely large quantities of meaningful data in a delicate sector like healthcare requires sophisticated linguistic strategies and computational solutions. In the research described here we approach the semantic dimension of the formative elements of medical words in monolingual and bilingual environments. The purpose is to automatically build Italian–English medical lexical resources by grounding their analysis and generation on the manipulation of their consituent morphemes. This approach has a significant impact on the automatic analysis of neologisms, typical for the medical domain. We created two electronic dictionaries of morphemes and a morphological finite state transducer, which, together, find all possible combinations of prefixes, confixes, and suffixes, and are able to annotate and translate the terms contained in a medical corpus, according to the meaning of the morphemes that compose these words. In order to enable the machine to “understand” also medical multiword expressions, we designed a syntactic grammar net that includes several paths based on different combinations of nouns, adjectives, and prepositions.