Alicante, Anita (2013) Barrier and Syntactic Features for Information Retrieval. [Tesi di dottorato]

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Item Type: Tesi di dottorato
Lingua: English
Title: Barrier and Syntactic Features for Information Retrieval
Creators:
CreatorsEmail
Alicante, Anitaanita.alicante@unina.it
Date: 2 April 2013
Number of Pages: 146
Institution: Università degli Studi di Napoli Federico II
Department: 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:
nomeemail
Moscariello, Giocondagioconda.moscariello@unina.it
Tutor:
nomeemail
Corazza, Annaanna.corazza@unina.it
Bonatti, Piero Andreapieroandrea.bonatti@unina.it
Date: 2 April 2013
Number of Pages: 146
Uncontrolled Keywords: Information Retrieval Information Extraction Concept Location
Settori scientifico-disciplinari del MIUR: Area 01 - Scienze matematiche e informatiche > INF/01 - Informatica
Date Deposited: 04 Apr 2013 11:27
Last Modified: 10 Dec 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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