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EMNLP
2010
14 years 7 months ago
Collective Cross-Document Relation Extraction Without Labelled Data
We present a novel approach to relation extraction that integrates information across documents, performs global inference and requires no labelled text. In particular, we tackle ...
Limin Yao, Sebastian Riedel, Andrew McCallum
KDD
2009
ACM
219views Data Mining» more  KDD 2009»
15 years 10 months ago
Structured correspondence topic models for mining captioned figures in biological literature
A major source of information (often the most crucial and informative part) in scholarly articles from scientific journals, proceedings and books are the figures that directly pro...
Amr Ahmed, Eric P. Xing, William W. Cohen, Robert ...
BMCBI
2008
122views more  BMCBI 2008»
14 years 9 months ago
OpenDMAP: An open source, ontology-driven concept analysis engine, with applications to capturing knowledge regarding protein tr
Background: Information extraction (IE) efforts are widely acknowledged to be important in harnessing the rapid advance of biomedical knowledge, particularly in areas where import...
Lawrence Hunter, Zhiyong Lu, James Firby, William ...
ACL
2010
14 years 7 months ago
Unsupervised Ontology Induction from Text
Extracting knowledge from unstructured text is a long-standing goal of NLP. Although learning approaches to many of its subtasks have been developed (e.g., parsing, taxonomy induc...
Hoifung Poon, Pedro Domingos
EKAW
2008
Springer
14 years 11 months ago
Unsupervised Discovery of Compound Entities for Relationship Extraction
In this paper we investigate unsupervised population of a biomedical ontology via information extraction from biomedical literature. Relationships in text seldom connect simple ent...
Cartic Ramakrishnan, Pablo N. Mendes, Shaojun Wang...