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Open Language Learning for Information Extraction

by Michael Schmitz, Robert Bart, Stephen Soderl, Oren Etzioni
"... Open Information Extraction (IE) systems extract relational tuples from text, without requiring a pre-specified vocabulary, by identifying relation phrases and associated arguments in arbitrary sentences. However, stateof-the-art Open IE systems such as REVERB and WOE share two important weaknesses ..."
Abstract - Cited by 7 (0 self) - Add to MetaCart
Open Information Extraction (IE) systems extract relational tuples from text, without requiring a pre-specified vocabulary, by identifying relation phrases and associated arguments in arbitrary sentences. However, stateof-the-art Open IE systems such as REVERB and WOE share two important weaknesses

Wrapper Induction for Information Extraction

by Nicholas Kushmerick , 1997
"... The Internet presents numerous sources of useful information---telephone directories, product catalogs, stock quotes, weather forecasts, etc. Recently, many systems have been built that automatically gather and manipulate such information on a user's behalf. However, these resources are usually ..."
Abstract - Cited by 624 (30 self) - Add to MetaCart
The Internet presents numerous sources of useful information---telephone directories, product catalogs, stock quotes, weather forecasts, etc. Recently, many systems have been built that automatically gather and manipulate such information on a user's behalf. However, these resources

Incorporating non-local information into information extraction systems by Gibbs sampling

by Jenny Rose Finkel, Trond Grenager, Christopher Manning - IN ACL , 2005
"... Most current statistical natural language processing models use only local features so as to permit dynamic programming in inference, but this makes them unable to fully account for the long distance structure that is prevalent in language use. We show how to solve this dilemma with Gibbs sampling, ..."
Abstract - Cited by 730 (25 self) - Add to MetaCart
use this technique to augment an existing CRF-based information extraction system with long-distance dependency models, enforcing label consistency and extraction template consistency constraints. This technique results in an error reduction of up to 9 % over state-of-the-art systems on two

Learning Information Extraction Rules for Semi-structured and Free Text

by Stephen Soderland, Claire Cardie, Raymond Mooney - Machine Learning , 1999
"... . A wealth of on-line text information can be made available to automatic processing by information extraction (IE) systems. Each IE application needs a separate set of rules tuned to the domain and writing style. WHISK helps to overcome this knowledge-engineering bottleneck by learning text extract ..."
Abstract - Cited by 437 (10 self) - Add to MetaCart
with a syntactic analyzer and semantic tagging, WHISK can also handle extraction from free text such as news stories. Keywords: natural language processing, information extraction, rule learning 1. Information extraction As more and more text becomes available on-line, there is a growing need

DBpedia: A Nucleus for a Web of Open Data

by Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Zachary Ives, et al. - PROC. 6TH INT’L SEMANTIC WEB CONF , 2007
"... DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. DBpedia allows you to ask sophisticated queries against datasets derived from Wikipedia and to link other datasets on the Web to Wikipedia data. We describe the extractio ..."
Abstract - Cited by 651 (37 self) - Add to MetaCart
DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. DBpedia allows you to ask sophisticated queries against datasets derived from Wikipedia and to link other datasets on the Web to Wikipedia data. We describe

Learning in graphical models

by Michael I. Jordan - STATISTICAL SCIENCE , 2004
"... Statistical applications in fields such as bioinformatics, information retrieval, speech processing, image processing and communications often involve large-scale models in which thousands or millions of random variables are linked in complex ways. Graphical models provide a general methodology for ..."
Abstract - Cited by 806 (10 self) - Add to MetaCart
Statistical applications in fields such as bioinformatics, information retrieval, speech processing, image processing and communications often involve large-scale models in which thousands or millions of random variables are linked in complex ways. Graphical models provide a general methodology

Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part-of-Speech Tagging

by Eric Brill - Computational Linguistics , 1995
"... this paper, we will describe a simple rule-based approach to automated learning of linguistic knowledge. This approach has been shown for a number of tasks to capture information in a clearer and more direct fashion without a compromise in performance. We present a detailed case study of this learni ..."
Abstract - Cited by 924 (8 self) - Add to MetaCart
this paper, we will describe a simple rule-based approach to automated learning of linguistic knowledge. This approach has been shown for a number of tasks to capture information in a clearer and more direct fashion without a compromise in performance. We present a detailed case study

Principles and Practice in Second Language Acquisition

by Stephen D Krashen , 1982
"... This is the original version of Principles and Practice, as published in 1982, with only minor changes. It is gratifying to point out that many of the predictions made in this book were confirmed by subsequent research, for example, the superiority of comprehensible-input based methods and sheltered ..."
Abstract - Cited by 775 (4 self) - Add to MetaCart
because they are getting comprehensible input at the same time. I now think it is very important to make a strong effort to inform students about the process of language acquisition, so they can continue to improve on their own.

Inductive learning algorithms and representations for text categorization,”

by Susan Dumais , John Platt , Mehran Sahami , David Heckerman - in Proceedings of the International Conference on Information and Knowledge Management, , 1998
"... ABSTRACT Text categorization -the assignment of natural language texts to one or more predefined categories based on their content -is an important component in many information organization and management tasks. We compare the effectiveness of five different automatic learning algorithms for text ..."
Abstract - Cited by 652 (8 self) - Add to MetaCart
ABSTRACT Text categorization -the assignment of natural language texts to one or more predefined categories based on their content -is an important component in many information organization and management tasks. We compare the effectiveness of five different automatic learning algorithms for text

Gradient-based learning applied to document recognition

by Yann Lecun, Léon Bottou, Yoshua Bengio, Patrick Haffner - Proceedings of the IEEE , 1998
"... Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradientbased learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify hi ..."
Abstract - Cited by 1533 (84 self) - Add to MetaCart
to deal with the variability of two dimensional (2-D) shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation, recognition, and language modeling. A new learning paradigm, called graph
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