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TRECVID
2007
15 years 2 months ago
MSRA-USTC-SJTU AT TRECVID 2007: HIGH-LEVEL FEATURE EXTRACTION AND SEARCH
This paper describes the MSRA-USTC-SJTU experiments for TRECVID 2007. We performed the experiments in high-level feature extraction and automatic search tasks. For high-level feat...
Tao Mei, Xian-Sheng Hua, Wei Lai, Linjun Yang, Zhe...
87
Voted
CIKM
2009
Springer
15 years 7 months ago
Combining labeled and unlabeled data with word-class distribution learning
We describe a novel simple and highly scalable semi-supervised method called Word-Class Distribution Learning (WCDL), and apply it the task of information extraction (IE) by utili...
Yanjun Qi, Ronan Collobert, Pavel Kuksa, Koray Kav...
102
Voted
NAACL
2010
14 years 11 months ago
Minimally-Supervised Extraction of Entities from Text Advertisements
Extraction of entities from ad creatives is an important problem that can benefit many computational advertising tasks. Supervised and semi-supervised solutions rely on labeled da...
Sameer Singh, Dustin Hillard, Chris Leggetter
KDD
2008
ACM
172views Data Mining» more  KDD 2008»
16 years 1 months ago
Structured metric learning for high dimensional problems
The success of popular algorithms such as k-means clustering or nearest neighbor searches depend on the assumption that the underlying distance functions reflect domain-specific n...
Jason V. Davis, Inderjit S. Dhillon
117
Voted
CIKM
2009
Springer
15 years 7 months ago
Semi-supervised learning of semantic classes for query understanding: from the web and for the web
Understanding intents from search queries can improve a user’s search experience and boost a site’s advertising profits. Query tagging via statistical sequential labeling mode...
Ye-Yi Wang, Raphael Hoffmann, Xiao Li, Jakub Szyma...