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» Document Summarization Using Conditional Random Fields
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ICDAR
2009
IEEE
14 years 12 months ago
Document Image Binarisation Using Markov Field Model
This paper presents a new approach for the binarization of seriously degraded manuscript. We introduce a new technique based on a Markov Random Field (MRF) model of the document. ...
Thibault Lelore, Frédéric Bouchara
ICASSP
2011
IEEE
14 years 5 months ago
Automatic speech recognition using Hidden Conditional Neural Fields
Hidden Conditional Random Fields(HCRF) is a very promising approach to model speech. However, because HCRF computes the score of a hypothesis by summing up linearly weighted featu...
Yasuhisa Fujii, Kazumasa Yamamoto, Seiichi Nakagaw...
ECIR
2007
Springer
15 years 3 months ago
Multinomial Randomness Models for Retrieval with Document Fields
Document fields, such as the title or the headings of a document, offer a way to consider the structure of documents for retrieval. Most of the proposed approaches in the literatu...
Vassilis Plachouras, Iadh Ounis
IR
2006
15 years 2 months ago
Table extraction for answer retrieval
The ability to find tables and extract information from them is a necessary component of many information retrieval tasks. Documents often contain tables in order to communicate d...
Xing Wei, W. Bruce Croft, Andrew McCallum
EMNLP
2011
14 years 1 months ago
Generating Aspect-oriented Multi-Document Summarization with Event-aspect model
In this paper, we propose a novel approach to automatic generation of aspect-oriented summaries from multiple documents. We first develop an event-aspect LDA model to cluster sen...
Peng Li, Yinglin Wang, Wei Gao, Jing Jiang