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» Improving Word Sense Disambiguation Using Topic Features
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EMNLP
2008
13 years 9 months ago
Graph-based Analysis of Semantic Drift in Espresso-like Bootstrapping Algorithms
Bootstrapping has a tendency, called semantic drift, to select instances unrelated to the seed instances as the iteration proceeds. We demonstrate the semantic drift of bootstrapp...
Mamoru Komachi, Taku Kudo, Masashi Shimbo, Yuji Ma...
ECML
2003
Springer
14 years 22 days ago
Combined Optimization of Feature Selection and Algorithm Parameters in Machine Learning of Language
Comparative machine learning experiments have become an important methodology in empirical approaches to natural language processing (i) to investigate which machine learning algor...
Walter Daelemans, Véronique Hoste, Fien De ...
ICPR
2010
IEEE
13 years 5 months ago
Scene Classification Using Spatial Pyramid of Latent Topics
We propose a scene classification method, which combines two popular methods in the literature: Spatial Pyramid Matching (SPM) and probabilistic Latent Semantic Analysis (pLSA) mod...
Emrah Ergul, Nafiz Arica
TREC
2003
13 years 8 months ago
UIC at TREC-2003: Robust Track
In TREC 2003, the Database and Information System Lab (DBIS) at University of Illinois at Chicago (UIC) participate in the robust track, which is a traditional ad hoc retrieval ta...
Shuang Liu, Clement T. Yu
COLING
2010
13 years 2 months ago
Towards an optimal weighting of context words based on distance
Word Sense Disambiguation (WSD) often relies on a context model or vector constructed from the words that co-occur with the target word within the same text windows. In most cases...
Bernard Brosseau-Villeneuve, Jian-Yun Nie, Noriko ...