Alicante, Anita (2013) Barrier and Syntactic Features for Information Retrieval. [Tesi di dottorato]
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| Tipologia del documento: | Tesi di dottorato |
|---|---|
| Lingua: | English |
| Titolo: | Barrier and Syntactic Features for Information Retrieval |
| Autori: | Autore Email Alicante, Anita anita.alicante@unina.it |
| Data: | 2 Aprile 2013 |
| Numero di pagine: | 146 |
| Istituzione: | Università degli Studi di Napoli Federico II |
| Dipartimento: | Matematica e applicazioni "Renato Caccioppoli" |
| Scuola di dottorato: | Scienze matematiche e informatiche |
| Dottorato: | Scienze computazionali e informatiche |
| Ciclo di dottorato: | 25 |
| Coordinatore del Corso di dottorato: | nome email Moscariello, Gioconda gioconda.moscariello@unina.it |
| Tutor: | nome email Corazza, Anna anna.corazza@unina.it Bonatti, Piero Andrea pieroandrea.bonatti@unina.it |
| Data: | 2 Aprile 2013 |
| Numero di pagine: | 146 |
| Parole chiave: | Information Retrieval Information Extraction Concept Location |
| Settori scientifico-disciplinari del MIUR: | Area 01 - Scienze matematiche e informatiche > INF/01 - Informatica |
| Depositato il: | 04 Apr 2013 11:27 |
| Ultima modifica: | 10 Dic 2014 14:11 |
| URI: | http://www.fedoa.unina.it/id/eprint/9354 |
Abstract
Information Retrieval (IR) goal consists in retrieving all the documents in a collection that are relevant to a given query. A subtask of IR is Information Extraction (IE) which includes machine learning approaches automatically extract from the documents information about, for example, entities or relations or events etc. In this thesis a novel type of features, called barrier features, is introduced. They are based on PoS-tagging. We use these features to solve several IR and IE problems. In details we build several IR or IE systems and overcame both the state-of-art methods and baseline systems built without these features. Again exploiting syntactic information in the second part of this thesis we apply constituency and dependency parsing, to two different areas: to support Concept Location in Software Engineering and to study the influence of the constituent order on the data-driven parsing in Computational Linguistic. In the former we have evaluated the use of off-the-shelf and trained natural language analyzers to parse identifier names, extract an ontology and use it to support concept location; in the latter we use two state-of-the-art data-driven parsers to study influence of the constituent order on the data-driven parsing of Italian.
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