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» Evaluating machine learning for information extraction
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ICML
2005
IEEE
16 years 15 days ago
2D Conditional Random Fields for Web information extraction
The Web contains an abundance of useful semistructured information about real world objects, and our empirical study shows that strong sequence characteristics exist for Web infor...
Jun Zhu, Zaiqing Nie, Ji-Rong Wen, Bo Zhang, Wei-Y...
ICCS
2005
Springer
15 years 5 months ago
On the Need to Bootstrap Ontology Learning with Extraction Grammar Learning
The main claim of this paper is that machine learning can help integrate the construction of ontologies and extraction grammars and lead us closer to the Semantic Web vision. The p...
Georgios Paliouras
ICML
2000
IEEE
16 years 15 days ago
Maximum Entropy Markov Models for Information Extraction and Segmentation
Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many text-related tasks, such as part-of-speech t...
Andrew McCallum, Dayne Freitag, Fernando C. N. Per...
ECIR
1998
Springer
15 years 1 months ago
Coupled Hierarchical IR and Stochastic Models for Surface Information Extraction
We present in this paper a combination of Machine Learning based Information Retrieval (IR) techniques and stochastic language modelling in a hierarchical system that extracts sur...
Hugo Zaragoza, Patrick Gallinari
BMCBI
2006
151views more  BMCBI 2006»
14 years 11 months ago
Machine learning and word sense disambiguation in the biomedical domain: design and evaluation issues
Background: Word sense disambiguation (WSD) is critical in the biomedical domain for improving the precision of natural language processing (NLP), text mining, and information ret...
Hua Xu, Marianthi Markatou, Rositsa Dimova, Hongfa...