PARSEME-IT - Issues in verbal Multiword Expressions identification and classification

La seconda edizione del PARSEME shared task si è basata su nuove linee guida e metodologie che hanno riguardato in particolar modo la lingua italiana con l'introduzione di nuove categorie di verbi non considerate nella precedente edizione. Il contributo presenta le novità introdotte, i risultati ottenuti e le problematiche che sono emerse durante l'annotazione relativamente ad alcune categorie di verbi.The second edition of the PARSEME shared task was based on new guidelines and methodologies that particularly concerned the Italian language with the introduction of new categories of verbs not considered in the previous edition. This contribution presents the novelties introduced, the results obtained and the problems that emerged during the annotation process and concerning some categories of verb

An Ontology-Based Method for Extracting and Classifying Domain-Specific Compositional Nominal Compounds

In this paper, we present our preliminary study on an ontology-based method to extract and classify compositional nominal compounds in specific domains of knowledge. This method is based on the assumption that, applying a conceptual model to represent knowledge domain, it is possible to improve the extraction and classification of lexicon occurrences for that domain in a semi-automatic way. We explore the possibility of extracting and classifying a specific construction type (nominal compounds) spanning a specific domain (Cultural Heritage) and a specific language (Italian)

Semi-automatic Parsing for Web Knowledge Extraction through Semantic Annotation

Parsing Web information, namely parsing content to find relevant documents on the basis of a user’s query, represents a crucial step to guarantee fast and accurate Information Retrieval (IR). Generally, an automated approach to such task is considered faster and cheaper than manual systems. Nevertheless, results do not seem have a high level of accuracy, indeed, as also Hjorland (2007) states, using stochastic algorithms entails: • Low precision due to the indexing of common Atomic Linguistic Units (ALUs) or sentences. • Low recall caused by the presence of synonyms. • Generic results arising from the use of too broad or too narrow terms. Usually IR systems are based on invert text index, namely an index data structure storing a mapping from content to its locations in a database file, or in a document or a set of documents. In this paper we propose a system, by means of which we will develop a search engine able to process online documents, starting from a natural language query, and to return information to users. The proposed approach, based on the Lexicon-Grammar (LG) framework and its language formalization methodologies, aims at integrating a semantic annotation process for both query analysis and document retrieval

Formal Linguistic Models and Knowledge Processing. A Structuralist Approach to Rule-Based Ontology Learning and Population

2013 - 2014The main aim of this research is to propose a structuralist approach for knowledge processing by means of ontology learning and population, achieved starting from unstructured and structured texts. The method suggested includes distributional semantic approaches and NL formalization theories, in order to develop a framework, which relies upon deep linguistic analysis... [edited by author]XIII n.s

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