Anaphora Resolution in Arabic/English Machine Translation Systems | A Statistical, Knowledge-Poor Approach to Arabic Pronominal Anaphora Resolution

Rania Al-Sabbagh

ISBN 10: 3838386841 / ISBN 13: 9783838386843

Published by LAP LAMBERT Academic Publishing, 2010

About this Item

Anaphora Resolution in Arabic/English Machine Translation Systems | A Statistical, Knowledge-Poor Approach to Arabic Pronominal Anaphora Resolution | Rania Al-Sabbagh | Taschenbuch | 104 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783838386843 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 107451324

Bibliographic Details

• Title: Anaphora Resolution in Arabic/English Machine Translation Systems | A Statistical, Knowledge-Poor Approach to Arabic Pronominal Anaphora Resolution
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2010
• Language: English
• ISBN 10: 3838386841
• ISBN 13: 9783838386843
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 7D millimeters 220 millimeters width by 150 millimeters height by 7 millimeters depth
• Weight: 173 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Anaphora Resolution (AR) is the process of determining the antecedent of a given anaphor. The present book focuses on pronominal AR in Modern Standard Arabic (MSA) which is a crucially required task for many Natural Language Processing (NLP) applications, including but not limited to Machine Translation (MT). Current MT systems poorly handle anaphora resolution when working on Arabic-English translation or vice versa, given that anaphora resolution is one of the least studied issues in Arabic NLP due to the fact that AR requires many NLP morphological, semantic and syntactic tools and resources; many of which are not available for MSA. Motivated by the poor performance of current MT systems in terms of Arabic AR and the lack of sufficient NLP research on the issue, this book proposes a statistical, corpus-based approach to AR in MSA that successfully overcomes the major bottleneck of Arabic NLP, namely the lack of basic language processing tools and resources. Such an approach achieves a promising performance rate of 87.6% and sheds light on further future work which can significantly improve performance.

About the Author

Rania Al-Sabbagh is a PhD student at the Department of Linguistics, University of Illinois at Urbana-Champaign (UIUC). She is a winner of the Artificial Intelligence/Cognitive Science Award for 2010, the Beckman Institute, UIUC. Her research interests include computational semantics, pragmatics and discourse analysis for Arabic and its dialects.