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In natural language processing, information extraction (IE) is a type of information retrieval whose goal is to automatically extract structured information, i.e. categorized and contextually and semantically well-defined data from a certain domain, from unstructured machine-readable documents.

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Knowledge Bases

Temporal taggers


A survey of named entity recognition and classification  Nadeau David, Satoshi Sekine
Boilerplate Detection using Shallow Text Features  Christian Kohlschütter, Peter Fankhauser, Wolfgang Nejdl
CiteSeer: An Automatic Citation Indexing System  C. Lee Giles, Kurt D. Bollacker, Steve Lawrence - Description of CiteSeer algorithms to extract author, title and citations
ClausIE: Clause-Based Open Information Extraction  Luciano Del Corro, Rainer Gemulla
ClausIE: Clause-Based Open Information Extraction  Luciano Del Corro, Rainer Gemulla
Collaboratively built semi-structured content and Artificial Intelligence: The story so far  Eduard Hovy, Roberto Navigli, Simone Paolo Ponzetto
Data-Intensive Question Answering  Eric Brill, Jimmy Lin, Michele Banko, Susan Dumais, Andrew Ng
Extraction of historical events from wikipedia  Daniel Hienert, Francesco Luciano
Information Extraction  Sunita Sarawagi - A very comprehensive survey on information extraction.
Information extraction bibliography  
Learning Everything about Anything: Webly-Supervised Visual Concept Learning  Santosh Kumar Divvala, Ali Farhadi, Carlos Guestrin
Learning to Extract International Relations from Political Context  Brendan O’Connor, Brandon M. Stewart, Noah A. Smith
Leveraging Linguistic Structure For Open Domain Information  Gabor Angeli, Melvin Johnson Premkumar, Christopher D. Manning
Semi-supervised named entity recognition: Learning to recognize 100 entity types with little supervision  David Nadeau
Unsupervised Named-Entity Recognition: Generating Gazetteers and Resolving Ambiguity  David Nadeau, Peter D. Turney, Stan Matwin
Using predicate-argument structures for information extraction  Mihai Surdeanu, Sanda Harabagiu, John Williams, Paul Aarseth