TAILIEUCHUNG - Báo cáo khoa học: "Pattern Learning for Relation Extraction with a Hierarchical Topic Model"

We describe the use of a hierarchical topic model for automatically identifying syntactic and lexical patterns that explicitly state ontological relations. We leverage distant supervision using relations from the knowledge base FreeBase, but do not require any manual heuristic nor manual seed list selections. Results show that the learned patterns can be used to extract new relations with good precision. | Pattern Learning for Relation Extraction with a Hierarchical Topic Model Enrique Alfonseca Katja Filippova Jean-Yves Delort Guillermo Garrido Google Research NLP IR Group UNED Brandschenkestrasse 110 Juan del Rosal 16. 8002 Zurich Switzerland 28040 Madrid Spain ealfonseca katjaf jydelort @ ggarrido@ Abstract We describe the use of a hierarchical topic model for automatically identifying syntactic and lexical patterns that explicitly state ontological relations. We leverage distant supervision using relations from the knowledge base FreeBase but do not require any manual heuristic nor manual seed list selections. Results show that the learned patterns can be used to extract new relations with good precision. 1 Introduction The detection of relations between entities for the automatic population of knowledge bases is very useful for solving tasks such as Entity Disambiguation Information Retrieval and Question Answering. The availability of high-coverage generalpurpose knowledge bases enable the automatic identification and disambiguation of entities in text and its applications Bunescu and Pasca 2006 Cucerzan 2007 McNamee and Dang 2009 Kwok et al. 2001 Pasca et al. 2006 Weld et al. 2008 Pereira et al. 2009 Kasneci et al. 2009 . Most early works in this area were designed for supervised Information Extraction competitions such as MUC Sundheim and Chinchor 1993 and ACE ACE 2004 Doddington et al. 2004 Li et al. 2011 which rely on the availability of annotated data. Open Information Extraction Sekine 2006 Banko et al. 2007 Bollegala et al. 2010 started as an effort to approach relation extraction in Work done during an internship at Google Zurich. 54 a completely unsupervised way by learning regularities and patterns from the web. Two example systems implementing this paradigm are TEXTRUNNER Yates et al. 2007 and Reverb Fader et al. 2011 . These systems do not need any manual data or rules but the relational facts they extract are not immediately .

• Báo cáo khoa học
• Pattern Learning
• Relation Extraction
• Hierarchical Topic Model
• báo cáo khoa học
• báo cáo ngôn ngữ
• ngôn ngữ tự nhiên
• Pattern Recognition
• Machine Learning
• Related fields of pattern recognition
• Two paradigms of pattern recognition
• Applications of pattern recognitions
• Ebook Pattern Recognition and Machine Learning
• Mathematical notation
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• Probability distributions
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• Bayesian linear regression
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• Semantic Class Learning from the Web
• Hyponym Pattern Linkage Graphs
• Zornitsa Kozareva
• xử lý ngôn ngữ tự nhiên
• JavaScript
• Design Patterns
• The Observer Pattern
• The Proxy Pattern
• The Decorator Pattern
• The Adapter Pattern
• International Journal of Computer Networks and Communications
• E learning based trainings for expenditure treasurers
• E learning based trainings
• Effective training pattern
• Education and training
• Information sciences
• New hybrid method
• Predicting univariate
• Multivariate time series
• Pattern forecasting
• Time series forecasting
• Hybrid model
• Optimize range of EV
• Efficient temperature control modules
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• Monotone missing pattern