Congle Zhang


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Exploiting Parallel News Streams for Unsupervised Event Extraction
Congle Zhang | Stephen Soderland | Daniel S. Weld
Transactions of the Association for Computational Linguistics, Volume 3

Most approaches to relation extraction, the task of extracting ground facts from natural language text, are based on machine learning and thus starved by scarce training data. Manual annotation is too expensive to scale to a comprehensive set of relations. Distant supervision, which automatically creates training data, only works with relations that already populate a knowledge base (KB). Unfortunately, KBs such as FreeBase rarely cover event relations (e.g. “person travels to location”). Thus, the problem of extracting a wide range of events — e.g., from news streams — is an important, open challenge. This paper introduces NewsSpike-RE, a novel, unsupervised algorithm that discovers event relations and then learns to extract them. NewsSpike-RE uses a novel probabilistic graphical model to cluster sentences describing similar events from parallel news streams. These clusters then comprise training data for the extractor. Our evaluation shows that NewsSpike-RE generates high quality training sentences and learns extractors that perform much better than rival approaches, more than doubling the area under a precision-recall curve compared to Universal Schemas.


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Harvesting Parallel News Streams to Generate Paraphrases of Event Relations
Congle Zhang | Daniel S. Weld
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Adaptive Parser-Centric Text Normalization
Congle Zhang | Tyler Baldwin | Howard Ho | Benny Kimelfeld | Yunyao Li
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


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Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations
Raphael Hoffmann | Congle Zhang | Xiao Ling | Luke Zettlemoyer | Daniel S. Weld
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies


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Learning 5000 Relational Extractors
Raphael Hoffmann | Congle Zhang | Daniel S. Weld
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Machine Reading at the University of Washington
Hoifung Poon | Janara Christensen | Pedro Domingos | Oren Etzioni | Raphael Hoffmann | Chloe Kiddon | Thomas Lin | Xiao Ling | Mausam | Alan Ritter | Stefan Schoenmackers | Stephen Soderland | Dan Weld | Fei Wu | Congle Zhang
Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading