An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A machine learning based approach for table detection on the web
Proceedings of the 11th international conference on World Wide Web
Effective Retrieval of Information in Tables on the Internet
IEA/AIE '02 Proceedings of the 15th international conference on Industrial and engineering applications of artificial intelligence and expert systems: developments in applied artificial intelligence
Table extraction using conditional random fields
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Mining tables from large scale HTML texts
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Learning to recognize tables in free text
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Web data extraction based on partial tree alignment
WWW '05 Proceedings of the 14th international conference on World Wide Web
A Scalable Hybrid Approach for Extracting Head Components from Web Tables
IEEE Transactions on Knowledge and Data Engineering
A composite kernel to extract relations between entities with both flat and structured features
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Learning table extraction from examples
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Fast and effective kernels for relational learning from texts
Proceedings of the 24th international conference on Machine learning
Discriminating Meaningful Web Tables from Decorative Tables Using a Composite Kernel
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Program plagiarism detection using parse tree Kernels
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
A fine-grained taxonomy of tables on the web
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
FACTO: a fact lookup engine based on web tables
Proceedings of the 20th international conference on World wide web
| Hi-index | 0.00 |
A table is a well-organized and summarized knowledge expression for a domain. Therefore, it is of great importance to extract information from tables. However, many tables in Web pages are used not to transfer information but to decorate pages. One of the most critical tasks in Web table mining is thus to discriminate meaningful tables from decorative ones. The main obstacle of this task comes from the difficulty of generating relevant features for discrimination. This paper proposes a novel discrimination method using a composite kernel which combines parse tree kernels and a linear kernel. Because a Web table is represented as a parse tree by an HTML parser, it is natural to represent the structural information of a table as a parse tree. In this paper, two types of parse trees are used to represent structural information within and around a table. These two trees define the structure kernel that handles the structural information of tables. The contents of a Web table are manipulated by a linear kernel with content features. Support vector machines with the composite kernel distinguish meaningful tables from decorative ones with high accuracy. A series of experiments show that the proposed method achieves state-of-the-art performance.