Serena Pelosi

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.

A Method for Topic Detection in Great Volumes of Data

Topics extraction has become increasingly important due to its effectiveness in many tasks, including information filtering, information retrieval and organization of document collections in digital libraries. The Topic Detection consists to find the most significant topics within a document corpus. In this paper we explore the adoption of a methodology of feature reduction to underline the most significant topics within a document corpus. We used an approach based on a clustering algorithm (X-means) over the \(tf-idf\) matrix calculated starting from the corpus, by which we describe the frequency of terms, represented by the columns, that occur in the documents, represented by the rows. To extract the topics, we build n binary problems, where n is the numbers of clusters produced by an unsupervised clustering approach and we operate a supervised feature selection over them, considering the top features as the topic descriptors. We will show the results obtained on two different corpora. Both collections are expressed in Italian: the first collection consists of documents of the University of Naples Federico II, the second one consists in a collection of medical records.

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.

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.

Morphological Relations for the Automatic Expansion of Italian Sentiment Lexicons

This paper introduces a morphological method for the expansion of Italian Sentiment Lexicons. The purpose of the work is to exploit the existing resources of Nooj in order to make unknown words automatically inherit the semantic information associated to the known items, tanks to derivation phenomena. The research did not focused only on the propagation of the semantic tags, but explored also the reversion, the intensification and the weakening of the words by the effect of special kinds of morphemes.

Feature-Based Customer Review Summarization

To systematically monitor the online customer satisfaction means to deal with a large amount of non-structured data and with many Natural Language Processing challenges. The purpose of the present research is to automatically identify the benefits and the drawbacks expressed by internet users in Italian customer reviews in free text format. The work is grounded on Italian lexical and grammatical resources that, together, are able to investigate the semantic relation between the product features and the opinions expressed on them. On the base of these resources we built DOXA, a linguistic-based application that gives a feedback of statistics about the positive or negative nature of the opinions and about the semantic categories of the features.