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Evaluation of Natural Language and Speech Tool for Italian : International Workshop, EVALITA 2011, Rome, January 24-25, 2012, Revised Selected Papers / edited by Bernardo Magnini, Francesco Cutugno, Mauro Falcone, Emanuele Pianta.

SpringerLink Books Computer Science (2011-2024) Available online

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Format:
Book
Contributor:
Magnini, Bernardo, Editor.
Cutugno, Francesco, Editor.
Falcone, Mauro, Editor.
Pianta, Emanuele, Editor.
SpringerLink (Online service)
Series:
Computer Science (SpringerNature-11645)
Lecture notes in computer science. Lecture notes in artificial intelligence 2945-9141 ; 7689
Lecture Notes in Artificial Intelligence, 2945-9141 ; 7689
Language:
English
Subjects (All):
Artificial intelligence.
Natural language processing (Computer science).
Romance languages.
Database management.
Pattern recognition systems.
Information storage and retrieval systems.
Artificial Intelligence.
Natural Language Processing (NLP).
Romance Languages.
Database Management.
Automated Pattern Recognition.
Information Storage and Retrieval.
Local Subjects:
Artificial Intelligence.
Natural Language Processing (NLP).
Romance Languages.
Database Management.
Automated Pattern Recognition.
Information Storage and Retrieval.
Physical Description:
1 online resource (XIV, 339 pages) : 38 illustrations
Edition:
1st ed. 2013.
Contained In:
Springer Nature eBook
Place of Publication:
Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
System Details:
text file PDF
Summary:
EVALITA (http://www.evalita.it/) is the reference evaluation campaign of both Natural Language Processing and Speech Technologies for the Italian language. The objective of the shared tasks proposed at EVALITA is to promote the development of language technologies for Italian, providing a common framework where different systems and approaches can be evaluated and compared in a consistent manner. This volume collects the final and extended contributions presented at EVALITA 2011, the third edition of the evaluation campaign. The 36 revised full papers were carefully reviewed and selected from a total of 87 submissions. The papers are organized in topical sections roughly corresponding to evaluation tasks: parsing - dependency parsing track, parsing - constituency parsing track, domain adaptation for dependency parsing, named entity recognition on transcribed broadcast news, cross-document coreference resolution of named person entities, anaphora resolution, supersense tagging, frame labeling over italian texts, lemmatisation, automatic speech recognition - large vocabulary transcription, forced alignment on spontaneous speech.
Contents:
The EVALITA Dependency Parsing Task: From 2007 to 2011
Use of Semantic Information in a Syntactic Dependency Parser
Parsit at Evalita 2011 Dependency Parsing Task
An Ensemble Model for the EVALITA 2011 Dependency Parsing Task
Tuning DeSR for Dependency Parsing of Italian
Domain Adaptation for Dependency Parsing at Evalita 2011
Experiments in Newswire-to-Law Adaptation of Graph-Based Dependency Parsers
Domain Adaptation by Active Learning
Named Entity Recognition on Transcribed Broadcast News at EVALITA 2011
A Simple Yet Effective Approach for Named Entity Recognition from Transcribed Broadcast News
The Tanl Tagger for Named Entity Recognition on Transcribed Broadcast News at Evalita 2011
The News People Search Task at EVALITA 2011: Evaluating Cross-Document Coreference Resolution of Named Person Entities in Italian News
Exploiting Background Knowledge for Clustering Person Names
Description and Results of the SuperSense Tagging Task
Super-Sense Tagging Using Support Vector Machines and Distributional Features
Generative and Discriminative Learning in Semantic Role Labeling for Italian
Structured Kernel-Based Learning for the Frame Labeling over Italian Texts
The Lemmatisation Task at the EVALITA 2011 Evaluation Campaign
The Vocapia Research ASR Systems for Evalita 2011
The SPPAS Participation to the Forced-Alignment Task
SAD-Based Italian Forced Alignment Strategies.
Other Format:
Printed edition:
ISBN:
978-3-642-35828-9
9783642358289
Access Restriction:
Restricted for use by site license.

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