TAILIEUCHUNG - Báo cáo khoa học: "Unsupervised Learning of Narrative Event Chains"

Hand-coded scripts were used in the 1970-80s as knowledge backbones that enabled inference and other NLP tasks requiring deep semantic knowledge. We propose unsupervised induction of similar schemata called narrative event chains from raw newswire text. A narrative event chain is a partially ordered set of events related by a common protagonist. We describe a three step process to learning narrative event chains. The first uses unsupervised distributional methods to learn narrative relations between events sharing coreferring arguments. The second applies a temporal classifier to partially order the connected events. . | Unsupervised Learning of Narrative Event Chains Nathanael Chambers and Dan Jurafsky Department of Computer Science Stanford University Stanford CA 94305 natec jurafsky @ Abstract Hand-coded scripts were used in the 1970-80S as knowledge backbones that enabled inference and other NLP tasks requiring deep semantic knowledge. We propose unsupervised induction of similar schemata called narrative event chains from raw newswire text. A narrative event chain is a partially ordered set of events related by a common protagonist. We describe a three step process to learning narrative event chains. The first uses unsupervised distributional methods to learn narrative relations between events sharing coreferring arguments. The second applies a temporal classifier to partially order the connected events. Finally the third prunes and clusters self-contained chains from the space of events. We introduce two evaluations the narrative cloze to evaluate event relatedness and an order coherence task to evaluate narrative order. We show a 36 improvement over baseline for narrative prediction and 25 for temporal coherence. 1 Introduction This paper induces a new representation of structured knowledge called narrative event chains or narrative chains . Narrative chains are partially ordered sets of events centered around a common protagonist. They are related to structured sequences of participants and events that have been called scripts Schank and Abelson 1977 or Fillmorean frames. These participants and events can be filled in and instantiated in a particular text situation to draw inferences. Chains focus on a single actor to facili tate learning and thus this paper addresses the three tasks of chain induction narrative event induction temporal ordering of events and structured selection pruning the event space into discrete sets . Learning these prototypical schematic sequences of events is important for rich understanding of text. Scripts were central to natural .

• Báo cáo khoa học
• Unsupervised Learning
• Narrative Event Chains
• Nathanael Chambers and Dan Jurafsky
• báo cáo khoa học
• báo cáo ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Learning Condensed Feature Representations
• Large Unsupervised Data Sets
• Supervised Learning
• ngôn ngữ tự nhiên
• Semantic Relation Composition
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• Unsupervised co learning
• downward entailing operators
• Cristian Danescu
• Narrative Schemas and their Participants
• Unsupervised Multilingual Learning
• Morphological Segmentation
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• Analyzing the Errors
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• Unsupervised Learning of Acoustic
• Sub word Units
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• Supervised
• Sentence Compression
• mô hình ngôn ngữ
• Field Segmentation Models
• Information Extraction
• Annealing Techniques
• Unsupervised Statistical Language Learning
• Noah A
• Smith and Jason Eisner
• Unsupervised Learning of Arabic Stemming
• a Parallel Corpus
• Monica Rogati Scott McCarley Yiming Yang
• Dependency Structure
• Language Modeling
• Unsupervised Learning – Clustering
• Partitional clustering
• Hierarchical clustering
• Expectation Maximization (EM) algorithm
• Incremental Clustering
• k Means algorithm
• Multi criteria decision making
• Combinative distance based
• Indian banks
• Hybrid unsupervised learning
• Multi criteria decision making approach
• Public sector banks
• Combinative Distance based Assessment
• K Means Clustering Performance
• BMC Bioinformatics
• Cell detection
• Deep learning
• Semi supervised learning
• Unsupervised domain adaptation
• Generalization capabilities
• Animal behavior
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• Active shape model
• Social sciences and humanities
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• Corruption on twitter
• Unsupervised machine learning
• Natural language processing
• Lecture Introduction to Machine learning and Data mining
• Bài giảng Học máy và khai phá dữ liệu
• Machine learning and data mining
• Partition based clustering
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