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SIGIR
2004
ACM
13 years 11 months ago
Focused named entity recognition using machine learning
In this paper we study the problem of finding most topical named entities among all entities in a document, which we refer to as focused named entity recognition. We show that th...
Li Zhang, Yue Pan, Tong Zhang
CLEF
2008
Springer
13 years 7 months ago
Experiments with Geographic Evidence Extracted from Documents
Abstract. For the 2008 participation at GeoCLEF, we focused on improving the extraction of geographic signatures from documents and optimising their use for GIR. The results show t...
Nuno Cardoso, Patrícia Sousa, Mário ...
ACL
2004
13 years 7 months ago
Discovering Relations among Named Entities from Large Corpora
Discovering the significant relations embedded in documents would be very useful not only for information retrieval but also for question answering and summarization. Prior method...
Takaaki Hasegawa, Satoshi Sekine, Ralph Grishman
WWW
2008
ACM
14 years 6 months ago
Representing a web page as sets of named entities of multiple types: a model and some preliminary applications
As opposed to representing a document as a "bag of words" in most information retrieval applications, we propose a model of representing a web page as sets of named enti...
Nan Di, Conglei Yao, Mengcheng Duan, Jonathan J. H...
WWW
2010
ACM
14 years 22 days ago
A scalable machine-learning approach for semi-structured named entity recognition
Named entity recognition studies the problem of locating and classifying parts of free text into a set of predefined categories. Although extensive research has focused on the de...
Utku Irmak, Reiner Kraft