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PRL
2008
181views more  PRL 2008»
14 years 9 months ago
Extractive spoken document summarization for information retrieval
The purpose of extractive summarization is to automatically select a number of indicative sentences, passages, or paragraphs from the original document according to a target summa...
Berlin Chen, Yi-Ting Chen
ACL
2007
14 years 11 months ago
Learning to Extract Relations from the Web using Minimal Supervision
We present a new approach to relation extraction that requires only a handful of training examples. Given a few pairs of named entities known to exhibit or not exhibit a particula...
Razvan C. Bunescu, Raymond J. Mooney
IROS
2006
IEEE
132views Robotics» more  IROS 2006»
15 years 3 months ago
Supervised Learning of Topological Maps using Semantic Information Extracted from Range Data
Abstract— This paper presents an approach to create topological maps from geometric maps obtained with a mobile robot in an indoor-environment using range data. Our approach util...
Óscar Martínez Mozos, Wolfram Burgar...
IS
2008
14 years 9 months ago
Mining relational data from text: From strictly supervised to weakly supervised learning
This paper approaches the relation classification problem in information extraction framework with different machine learning strategies, from strictly supervised to weakly superv...
Zhu Zhang
ACL
1998
14 years 11 months ago
Trainable, Scalable Summarization Using Robust NLP and Machine Learning
We describe a trainable and scalable summarization system which utilizes features derived from information retrieval, information extraction, and NLP techniques and on-line resour...
Chinatsu Aone, Mary Ellen Okurowski, James Gorlins...