TAILIEUCHUNG - Báo cáo khoa học: "Recall-Oriented Learning of Named Entities in Arabic Wikipedia"

We consider the problem of NER in Arabic Wikipedia, a semisupervised domain adaptation setting for which we have no labeled training data in the target domain. To facilitate evaluation, we obtain annotations for articles in four topical groups, allowing annotators to identify domain-specific entity types in addition to standard categories. Standard supervised learning on newswire text leads to poor target-domain recall. We train a sequence model and show that a simple modification to the online learner—a loss function encouraging it to “arrogantly” favor recall over precision— substantially improves recall and F1 . . | Recall-Oriented Learning of Named Entities in Arabic Wikipedia Behrang Mohit Nathan Schneider Rishav Bhowmick Kemal Oflazer Noah A. Smith School of Computer Science Carnegie Mellon University . Box 24866 Doha Qatar Pittsburgh PA 15213 USA behrang@ nschneid@cs. rishavb@qatar. ko@cs. nasmith@cs. Abstract We consider the problem of NER in Arabic Wikipedia a semisupervised domain adaptation setting for which we have no labeled training data in the target domain. To facilitate evaluation we obtain annotations for articles in four topical groups allowing annotators to identify domain-specific entity types in addition to standard categories. Standard supervised learning on newswire text leads to poor target-domain recall. We train a sequence model and show that a simple modification to the online learner a loss function encouraging it to arrogantly favor recall over precision substantially improves recall and Fl. We then adapt our model with self-training on unlabeled target-domain data enforcing the same recall-oriented bias in the selftraining stage yields marginal 1 Introduction This paper considers named entity recognition NER in text that is different from most past research on NER. Specifically we consider Arabic Wikipedia articles with diverse topics beyond the commonly-used news domain. These data challenge past approaches in two ways First Arabic is a morphologically rich language Habash 2010 . Named entities are referenced using complex syntactic constructions cf. English NEs which are primarily sequences of proper nouns . The Arabic script suppresses most vowels increasing lexical ambiguity and lacks capitalization a key clue for English NER. Second much research has focused on the use of news text for system building and evaluation. Wikipedia articles are not news belonging instead to a wide range of domains that are not clearly 1The annotated dataset and a supplementary document with additional details of this work can be found at http .

• Báo cáo khoa học
• Recall Oriented Learning
• Named Entities
• Arabic Wikipedia
• scientific reports
• model language
• process natural language
• Dialog Control
• Natural Language System
• Dialog Control in a Natural Language System
• Expanding the Horizons
• Natural Language Interfaces
• Phil Hayes
• PROSPECTS OR PRACTICAL
• NATURAL LANGUAGE SYSTEMS
• Workshops
• NATURAL LANGUAGE
• INTERACTION WITH MACHINES
• Long Papers
• COMPUTER INTEBFACE DESIGN MURRAY TUROFF DEPARTMENT OF COMPUTER
• IiVFORMATION SCIENCE IIEW JERSEY INSTITUTE OF TECHNOLOGY
• EVALUATION OF NATURAL LANGUAGE
• INTERFACES TO DATABASE SYSTEMS
• A PANEL DISCUSSION
• database query system
• J
• Norwood Crout
• INTERFACES TO DATA BASE SYSTEMS
• Bozena Henisz
• INTERPRETING NATURAL LANGUAGE
• DATABASE UPDATES
• S
• Jermld Kaplan Jim David
• TRANSPORTABLE NATURAL LANGUAGE
• INTERFACES TO DATABASES
• Gary G
• Hendrlx and William H
• Lewis
• DATABASES THEORETICAL
• TECHNICAL ISSUES
• TRANSPORTABLE NATURAL LANGUAGE INTERFACES
• PROBLEMS AND TECHNIQUES
• Barbara J
• Grosz
• THEORETICAL TECHNICAL ISSUES
• NATURAL LANGUAGE ACCESS TO DATABASES
• R
• Petrick
• Domain Independent
• Database Access Systems
• ISSUES IN NATURAL LANGUAGE
• ACCESS TO DATABASES
• A LOGIC PROGRAMMING PERSPECTIVE
• Sharon C
• Salveter David Maier
• PLANNING NATURAL LANGUAGE
• EXPRESSIONS REFERRING
• Douglas E
• Appelt
• THE TEXT SYSTEM
• NATURAL LANGUAGE GENERATION
• Kathleen R
• M
• MENTING A DATABASE
• KNOWLEDGE REPRESENTATION
• THE KEY TO THE SELECTION PROBLEM
• E
• Jeffrey Conklin David D
• McDonald
• A KNOWLEDGE ENGINEERING
• NATURAL LANGUAGE UNDERSTANDING
• Stuart C
• Shapiro
• NATURAL LANGUAGE TEXTS
• NECESSARILY GRAMMATICAL
• EVEN COMPLETE
• UNGRAMHATICALITY
• EXTRA GRAMMATICALITY
• NATURAL LANGUAGE UNDERSTANDING SYSTEMS
• Taxonomy
• Descriptions
• Individuals in Natural
• Language Understanding
• The Use of Ooject Special Knowledge
• Natural Language Processing
• Mark H
• Bursteln