Using distributional similarity to organise biomedical terminology

We investigate an application of distributional similarity techniques to the problem of structural organisation of biomedical terminology. Our application domain is the relatively small GENIA corpus. Using terms that have been accurately marked-up by hand within the corpus, we consider the problem of automatically determining semantic proximity. Terminological units are dened for our purposes as normalised classes of individual terms. Syntactic analysis of the corpus data is carried out using the Pro3Gres parser and provides the data required to calculate distributional similarity using a variety of dierent measures. Evaluation is performed against a hand-crafted gold standard for this domain in the form of the GENIA ontology. We show that distributional similarity can be used to predict semantic type with a good degree of accuracy

Automatic domain-specific learning: towards a methodology for ontology enrichment

[EN] At the current rate of technological development, in a world where enormous amount of data are constantly created and in which the Internet is used as the primary means for information exchange, there exists a need for tools that help processing, analyzing and using that information. However, while the growth of information poses many opportunities for social and scientific advance, it has also highlighted the difficulties of extracting meaningful patterns from massive data. Ontologies have been claimed to play a major role in the processing of large-scale data, as they serve as universal models of knowledge representation, and are being studied as possible solutions to this. This paper presents a method for the automatic expansion of ontologies based on corpus and terminological data exploitation. The proposed ¿ontology enrichment method¿ (OEM) consists of a sequence of tasks aimed at classifying an input keyword automatically under its corresponding node within a target ontology. Results prove that the method can be successfully applied for the automatic classification of specialized units into a reference ontology.Financial support for this research has been provided by the DGI, Spanish Ministry of Education and Science, grant FFI2011-29798-C0201.Ureña Gómez-Moreno, P.; Mestre-Mestre, EM. (2017). Automatic domain-specific learning: towards a methodology for ontology enrichment. LFE. Revista de Lenguas para Fines Específicos. 23(2):63-85. http://hdl.handle.net/10251/148357S638523

A literature survey of methods for analysis of subjective language

Subjective language is used to express attitudes and opinions towards things, ideas and people. While content and topic centred natural language processing is now part of everyday life, analysis of subjective aspects of natural language have until recently been largely neglected by the research community. The explosive growth of personal blogs, consumer opinion sites and social network applications in the last years, have however created increased interest in subjective language analysis. This paper provides an overview of recent research conducted in the area

Ontology learning from Italian legal texts

The paper reports on the methodology and preliminary results of a case study in automatically extracting ontological knowledge from Italian legislative texts. We use a fully-implemented ontology learning system (T2K) that includes a battery of tools for Natural Language Processing (NLP), statistical text analysis and machine language learning. Tools are dynamically integrated to provide an incremental representation of the content of vast repositories of unstructured documents. Evaluated results, however preliminary, show the great potential of NLP-powered incremental systems like T2K for accurate large-scale semi-automatic extraction of legal ontologies

D6.1: Technologies and Tools for Lexical Acquisition

This report describes the technologies and tools to be used for Lexical Acquisition in PANACEA. It includes descriptions of existing technologies and tools which can be built on and improved within PANACEA, as well as of new technologies and tools to be developed and integrated in PANACEA platform. The report also specifies the Lexical Resources to be produced. Four main areas of lexical acquisition are included: Subcategorization frames (SCFs), Selectional Preferences (SPs), Lexical-semantic Classes (LCs), for both nouns and verbs, and Multi-Word Expressions (MWEs)