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ICASSP
2011
IEEE
12 years 11 months ago
Application specific loss minimization using gradient boosting
Gradient boosting is a flexible machine learning technique that produces accurate predictions by combining many weak learners. In this work, we investigate its use in two applica...
Bin Zhang, Abhinav Sethy, Tara N. Sainath, Bhuvana...
KDD
2005
ACM
153views Data Mining» more  KDD 2005»
14 years 7 months ago
Improving discriminative sequential learning with rare--but--important associations
Discriminative sequential learning models like Conditional Random Fields (CRFs) have achieved significant success in several areas such as natural language processing, information...
Xuan Hieu Phan, Minh Le Nguyen, Tu Bao Ho, Susumu ...
ER
2007
Springer
129views Database» more  ER 2007»
14 years 1 months ago
A Methodology and Toolkit for Deploying Contract Documents as E-contracts
Recent research in e-contracts is concerned with the development of frameworks and tools to support contracts. EREC framework is one that enables modelling and deployment of e-con...
Anushree Khandekar, P. Radha Krishna, Kamalakar Ka...
FLAIRS
2003
13 years 8 months ago
Orthographic Case Restoration Using Supervised Learning Without Manual Annotation
One challenge in text processing is the treatment of case insensitive documents such as speech recognition results. The traditional approach is to re-train a language model exclud...
Cheng Niu, Wei Li 0003, Jihong Ding, Rohini K. Sri...
ACL
2012
11 years 9 months ago
Building Trainable Taggers in a Web-based, UIMA-Supported NLP Workbench
Argo is a web-based NLP and text mining workbench with a convenient graphical user interface for designing and executing processing workflows of various complexity. The workbench...
Rafal Rak, BalaKrishna Kolluru, Sophia Ananiadou