A flexible learning system for wrapping tables and lists in HTML documents
Proceedings of the 11th international conference on World Wide Web
Table extraction using conditional random fields
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Using the structure of Web sites for automatic segmentation of tables
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
A survey of table recognition: Models, observations, transformations, and inferences
International Journal on Document Analysis and Recognition
Mutation Mining--A Prospector's Tale
Information Systems Frontiers
Question answering performance on table data
dg.o '04 Proceedings of the 2004 annual national conference on Digital government research
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Bioinformatics
Association rules to identify receptor and ligand structures through named entities recognition
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part III
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We investigate the challenge of extracting information about genetic mutations from tables, an important source of information in scientific papers. We use various machine learning algorithms and feature sets, and evaluate performance in extracting fields associated with an existing handcreated database of mutations. We then show how classifying tabular information can be leveraged for the task of named entity detection for mutations.