Alicante, Anita
(2013)
Barrier and Syntactic Features for Information Retrieval.
[Tesi di dottorato]
| Item Type: |
Tesi di dottorato
|
| Lingua: |
English |
| Title: |
Barrier and Syntactic Features for Information Retrieval |
| Creators: |
| Creators | Email |
|---|
| Alicante, Anita | anita.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: |
| nome | email |
|---|
| Moscariello, Gioconda | gioconda.moscariello@unina.it |
|
| Tutor: |
| nome | email |
|---|
| Corazza, Anna | anna.corazza@unina.it | | Bonatti, Piero Andrea | pieroandrea.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 |
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| 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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