TAILIEUCHUNG - Báo cáo khoa học: "Semantic Role Labeling Systems for Arabic using Kernel Methods"

There is a widely held belief in the natural language and computational linguistics communities that Semantic Role Labeling (SRL) is a significant step toward improving important applications, . question answering and information extraction. In this paper, we present an SRL system for Modern Standard Arabic that exploits many aspects of the rich morphological features of the language. The experiments on the pilot Arabic Propbank data show that our system based on Support Vector Machines and Kernel Methods yields a global SRL F1 score of , which improves the current state-of-the-art in Arabic SRL. . | Semantic Role Labeling Systems for Arabic using Kernel Methods Mona Diab CCLS Columbia University New York NY 10115 USA mdiab@ Alessandro Moschitti Daniele Pighin DISI University of Trento FBK-irst DISI University of Trento Trento I-38100 Italy Trento I-38100 Italy moschitti@ pighin@ Abstract There is a widely held belief in the natural language and computational linguistics communities that Semantic Role Labeling SRL is a significant step toward improving important applications . question answering and information extraction. In this paper we present an SRL system for Modern Standard Arabic that exploits many aspects of the rich morphological features of the language. The experiments on the pilot Arabic Propbank data show that our system based on Support Vector Machines and Kernel Methods yields a global SRL Fl score of which improves the current state-of-the-art in Arabic SRL. 1 Introduction Shallow approaches to semantic processing are making large strides in the direction of efficiently and effectively deriving tacit semantic information from text. Semantic Role Labeling SRL is one such approach. With the advent of faster and more powerful computers more effective machine learning algorithms and importantly large data resources annotated with relevant levels of semantic information such as the FrameNet Baker et al. 1998 and ProbBank Kingsbury and Palmer 2003 we are seeing a surge in efficient approaches to SRL Carreras and Marquez 2005 . SRL is the process by which predicates and their arguments are identified and their roles are defined in a sentence. For example in the English sentence John likes apples. the predicate is likes whereas John and apples t c r the scmaniic role labels agent ARG0 and theme ARG1 . The crucial fact about semantic roles is that regardless of the overt syntactic structure variation the underlying predicates remain the same. Hence for the sentence John opened the door and the door opened .

• Báo cáo khoa học
• Semantic Role Labeling
• Systems for Arabic
• Kernel Methods
• báo cáo khoa học
• báo cáo ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Towards Open Domain Semantic Role Labeling
• Danilo Croce
• Cristina Giannone
• ngôn ngữ tự nhiên
• Open Domain Semantic Role Labeling
• Modeling Word Spans
• Fei Huang
• Starting From Scratch
• Michael Connor
• Semantics Driven Shallow Parsing
• Chinese Semantic Role Labeling
• Weiwei Sun
• Improving Chinese Semantic Role Labeling
• Rich Syntactic Features
• Unsupervised Argument Identification
• Omri Abend
• A Semantic Role Labeling System
• Incorporating CCG CFG
• Dependency Features
• Adapting Self training
• Rasoul Samad Zadeh Kaljahi
• Past Present and Future
• Long Papers
• Sentence Simplification
• David Vickrey and Daphne Koller
• A Hybrid Convolution Tree Kernel
• Wanxiang Che Harbin
• mô hình ngôn ngữ
• FrameNet
• VerbNet and PropBank
• Learning Predictive Structures
• Semantic Role Labeling of NomBank
• Chang Liu and Hwee Tou Ng
• Prediction of Thematic Rank
• Structured Semantic Role Labeling
• Weiwei Sun and Zhifang Sui and Meng Wang
• Different Syntactic Views
• Sameer Pradhan
• Joint Learning Improves Semantic
• Role Labeling Kristina Toutanova
• Computer Science Stanford
• Coreference Resolution
• Simone Paolo Ponzetto and Michael Strube
• scientific reports
• model language
• process natural language
• Semi Supervised
• BMC Bioinformatics
• Biomedical semantic role labeling
• Biological processes
• Predicate argument structures
• Biomedical literature
• Speech and language processing
• Natural language processing
• Computational linguistics
• Speech recognition
• Dependency parsing
• Automatic speech recognition