WordNet: a lexical database for English
Communications of the ACM
A machine learning based approach for table detection on the web
Proceedings of the 11th international conference on World Wide Web
QuASM: a system for question answering using semi-structured data
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
A framework for web table mining
Proceedings of the 4th international workshop on Web information and data management
Mining tables from large scale HTML texts
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
A survey of table recognition: Models, observations, transformations, and inferences
International Journal on Document Analysis and Recognition
Automating the extraction of data from HTML tables with unknown structure
Data & Knowledge Engineering - Special issue: ER 2002
Learning table extraction from examples
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
QMatch - Using paths to match XML schemas
Data & Knowledge Engineering
Extracting logical structures from HTML tables
Computer Standards & Interfaces
Detecting tables in Web documents
Engineering Applications of Artificial Intelligence
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Table has been recognized as a simply and widely used data representation scheme. Each table alone typically contains rich and useful information which is valuable for many applications such as information retrieval, question-answering and etc. While all table formats can simply be parsed by human, this parsing is difficult for computer, prohibiting such applications to be done in an automatic manner. In this paper, we thus propose the comprehensive and novel table interpretation technique, namely tInterpreter. Essentially, it transforms a table into its corresponding horizontal 1-dimensional tables. To achieve this, the underlying work is based on (i) the similarity of two given cells with respect to the data type and the semantic correspondence concerns; (ii) the discovery for the boundary of a primitive table residing in a composite table; (iii) the identification of the attribute-value relationship and the value association of cells; and (iv) the integration of two pieces of similar or dissimilar information. The experimental result showed that the overall effectiveness of tInterpreter was higher than Chen, Tengli and Kim.