TAILIEUCHUNG - Báo cáo khoa học: "Semantic Role Labeling: Past, Present and Future"

Semantic Role Labeling (SRL) consists of, given a sentence, detecting basic event structures such as “who” did “what” to “whom”, “when” and “where”. From a linguistic point of view, a key component of the task corresponds to identifying the semantic arguments filling the roles of the sentence predicates. Typical predicate semantic arguments include Agent, Patient, and Instrument, but semantic roles may also be found as adjuncts (., Locative, Temporal, Manner, and Cause). The identification of such event frames holds potential for significant impact in many NLP applications, such as Information Extraction, Question Answering, Summarization and Machine Translation. Recently, the. | Semantic Role Labeling Past Present and Future Lluis Marquez TALP Research Center Software Department Technical University of Catalonia lluism@ 1 Introduction Semantic Role Labeling SRL consists of given a sentence detecting basic event structures such as who did what to whom when and where . From a linguistic point of view a key component of the task corresponds to identifying the semantic arguments filling the roles of the sentence predicates. Typical predicate semantic arguments include Agent Patient and Instrument but semantic roles may also be found as adjuncts . Locative Temporal Manner and Cause . The identification of such event frames holds potential for significant impact in many NLP applications such as Information Extraction Question Answering Summarization and Machine Translation. Recently the compilation and manual annotation with semantic roles of several corpora has enabled the development of supervised statistical approaches to SRL which has become a well-defined task with a substantial body of work and comparative evaluation. Significant advances in many directions have been reported over the last several years including but not limited to machine learning algorithms and architectures specialized for the task feature engineering inference to force coherent solutions and system combinations. However despite all the efforts and the considerable degree of maturity of the SRL technology the use of SRL systems in real-world applications has so far been limited and certainly below the initial expectations. This fact has to do with the weaknesses and limitations of current systems which have been highlighted by many of the evaluation exercises and keep unresolved for a few years . poor generalization across corpora low scalability and efficiency knowledge poor features too high complexity absolute performance below 90 etc. . 2 Content Overview and Outline This tutorial has two differentiated parts. In the first one the state-of-the-art .

• Báo cáo khoa học
• Semantic Role Labeling
• Past Present and Future
• Long Papers
• 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
• Sentence Simplification
• David Vickrey and Daphne Koller
• Systems for Arabic
• Kernel Methods
• 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