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» Document Summarization Using Conditional Random Fields
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ICDAR
2009
IEEE
13 years 5 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
12 years 11 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
13 years 9 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
13 years 7 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
12 years 7 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