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» Improving Language Models by Clustering Training Sentences
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FMSD
2008
110views more  FMSD 2008»
13 years 9 months ago
Automatic symbolic compositional verification by learning assumptions
Abstract Compositional reasoning aims to improve scalability of verification tools by reducing the original verification task into subproblems. The simplification is typically base...
Wonhong Nam, P. Madhusudan, Rajeev Alur
EMNLP
2010
13 years 7 months ago
Exploiting Conversation Structure in Unsupervised Topic Segmentation for Emails
This work concerns automatic topic segmentation of email conversations. We present a corpus of email threads manually annotated with topics, and evaluate annotator reliability. To...
Shafiq R. Joty, Giuseppe Carenini, Gabriel Murray,...
IR
2010
13 years 7 months ago
Learning to rank with (a lot of) word features
In this article we present Supervised Semantic Indexing (SSI) which defines a class of nonlinear (quadratic) models that are discriminatively trained to directly map from the word...
Bing Bai, Jason Weston, David Grangier, Ronan Coll...
CE
2007
133views more  CE 2007»
13 years 9 months ago
Reusable, lifelike virtual humans for mentoring and role-playing
Lifelike, interactive digital characters, serving as mentors and role-playing actors, have been shown to significantly improve learner motivation and retention. However, the cost...
Edward M. Sims
ICTAI
2007
IEEE
14 years 3 months ago
Dragon Toolkit: Incorporating Auto-Learned Semantic Knowledge into Large-Scale Text Retrieval and Mining
The majority of text retrieval and mining techniques are still based on exact feature (e.g. words) matching and unable to incorporate text semantics. Many researchers believe that...
Xiaohua Zhou, Xiaodan Zhang, Xiaohua Hu