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Displaying 1-6 out of 6 total
Chemical Name Extraction Based on Automatic Training Data Generation and Rich Feature Set
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics
By Su Yan,W. Scott Spangler,Ying Chen
Issue Date:September 2013
pp. 1218-1233
The automation of extracting chemical names from text has significant value to biomedical and life science research. A major barrier in this task is the difficulty of getting a sizable and good quality data to train a reliable entity extraction model. Anot...
 
Prospective Client Driven Technology Recommendation
Found in: 2012 Annual SRII Global Conference (SRII)
By Qi He,W. Scott Spangler,Bin He,Ying Chen,Linda Kato
Issue Date:July 2012
pp. 110-119
Helping locate the patents of the right technologies for licensing to prospective clients is more than one billion USD business annually to IBM. However, searching for right technologies from multiple massive data sources for a value presentation to custom...
 
Chemical Name Extraction Based on Automatic Training Data Generation and Rich Feature Set
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
By Su Yan, W. Scott Spangler, Ying Chen
Issue Date:September 2013
pp. 1218-1233
The automation of extracting chemical names from text has significant value to biomedical and life science research. A major barrier in this task is the difficulty of getting a sizable and good quality data to train a reliable entity extraction model. Anot...
     
Learning to extract chemical names based on random text generation and incomplete dictionary
Found in: Proceedings of the 11th International Workshop on Data Mining in Bioinformatics (BIOKDD '12)
By Su Yan, W. Scott Spangler, Ying Chen
Issue Date:August 2012
pp. 21-25
Automatically extracting chemical names from text has significant value to biomedical and life science research. A major barrier in this task is the difficulty of getting a sizable good quality training set to train a reliable entity extraction model. Leve...
     
COA: finding novel patents through text analysis
Found in: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '09)
By Alfredo Alba, Mohammad Al Hasan, Thomas Griffin, W. Scott Spangler
Issue Date:June 2009
pp. 1-24
In recent years, the number of patents filed by the business enterprises in the technology industry are growing rapidly, thus providing unprecedented opportunities for knowledge discovery in patent data. One important task in this regard is to employ data ...
     
Clustering hypertext with applications to web searching
Found in: Proceedings of the eleventh ACM on Hypertext and hypermedia (HYPERTEXT '00)
By Dharmendra S. Modha, W. Scott Spangler
Issue Date:May 2000
pp. 143-152
This paper discusses a semantic database approach to museum hypermedia systems based upon binary relations, with a restricted set of abstraction relationships. We describe examples of schema, queries and naviagaion aids for a prototype system designed as a...
     
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