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Understanding Multimodal Procedural Knowledge by Sequencing Multimodal Instructional Manuals ...
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OntoNotes Release 2.0
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Weischedel, Ralph; Pradhan, Sameer; Ramshaw, Lance; Palmer, Martha; Xue, Nianwen; Marcus, Mitchell; Taylor, Ann; Greenberg, Craig; Hovy, Eduard; Belvin, Robert; Houston, Ann. - : Linguistic Data Consortium, 2008. : https://www.ldc.upenn.edu, 2008
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Abstract:
*Introduction* The OntoNotes project is a collaborative effort between BBN Technologies, the University of Colorado, the University of Pennsylvania, and the University of Southern California's Information Sciences Institute. The goal of the project is to annotate a large corpus comprising various genres of text (news, conversational telephone speech, weblogs, use net, broadcast, talk shows) in three languages (English, Chinese, and Arabic) with structural information (syntax and predicate argument structure) and shallow semantics (word sense linked to an ontology and coreference). OntoNotes Release 2.0 is a continuation of the OntoNotes project and is supported by the Defense Advanced Research Projects Agency, GALE Program Contract No. HR0011-06-C-0022. OntoNotes Release 1.0 (LDC2007T21) contains 400k words of Chinese newswire data (from Xinhua News Agency and Sinorama Magazine) and 300k words of English newswire data (from the Wall Street Journal). OntoNotes Release 2.0 adds the following to the corpus: 274k words of Chinese broadcast news data (from China Broadcating System, China Central TV, China National Radio, China Television System and Voice of America); and 200k words of English broadcast news data (from ABC, CNN, NBC, Public Radio International and Voice of America). Natural language applications like machine translation, question answering, and summarization currently are forced to depend on impoverished text models like bags of words or n-grams, while the decisions that they are making ought to be based on the meanings of those words in context. That lack of semantics causes problems throughout the applications. Misinterpreting the meaning of an ambiguous word results in failing to extract data, incorrect alignments for translation, and ambiguous language models. Incorrect coreference resolution results in missed information (because a connection is not made) or incorrectly conflated information (due to false connections). OntoNotes builds on two time-tested resources, following the Penn Treebank for syntax and the Penn PropBank for predicate-argument structure. Its semantic representation will include word sense disambiguation for nouns and verbs, with each word sense connected to an ontology, and coreference. The current goals call for annotation of over a million words each of English and Chinese, and half a million words of Arabic over five years. The authors wish to make this resource available to the natural language research community so that decoders for these phenomena can be trained to generate the same structure in new documents. Lessons learned over the years have shown that the quality of annotation is crucial if it is going to be used for training machine learning algorithms. Taking this cue, each layer of annotation in OntoNotes will have at least 90% inter-annotator agreement. Pilot studies have shown that predicate structure, word sense, ontology linking, and coreference can all be annotated rapidly and with better than 90% consistency. *Samples* For an example of the data in this corpus, please examine the following samples * Chinese * English *Sponsorship* This work is supported in part by the Defense Advanced Research Projects Agency, GALE Program Grant No. HR0011-06-1-0003. The content of this publication does not necessarily reflect the position or policy of the Government, and no official endorsement should be inferred. The World is a co-production of Public Radio International and the British Broadcasting Corporation and is produced at WGBH Boston.
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URL: https://catalog.ldc.upenn.edu/LDC2008T04
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| 14 |
Extracting Dynamic Evidence Networks
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In: DTIC AND NTIS (2004)
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Challenges in Information Retrieval and Language Modeling
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In: R. Manmatha (2003)
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Challenges in Information Retrieval and Language Modeling
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In: Andrew McCallum (2003)
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Algorithms That Learn to Extract Information BBN: Description of the Sift System as Used for MUC-7
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In: DTIC (1998)
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BBN: Description of the PLUM System as Used for MUC-6
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In: DTIC (1995)
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