Indutive Learning Approaches in Information Extraction Analysis, Formalization, 
Comparison, Evaluation

Kara, Abdul Qadar

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Indutive Learning Approaches in Information Extraction Analysis, Formalization, Comparison, Evaluation

Kara, A. Q. (2004). Indutive Learning Approaches in Information Extraction Analysis, Formalization, Comparison, Evaluation. Master Thesis, Universität des Saarlandes, Saarbrücken.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0027-F48E-9 Versions-Permalink: https://hdl.handle.net/11858/00-001M-0000-0027-F48F-7

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Kara, Abdul Qadar^{1, 2}, Autor

Affiliations:
1Programming Logics, MPI for Informatics, Max Planck Society, ou_40045
2International Max Planck Research School, MPI for Informatics, Max Planck Society, Campus E1 4, 66123 Saarbrücken, DE, ou_1116551

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Zusammenfassung: Although Information Extraction field has been in the market since almost last two decades, it is still considered to be in its initial stage. There are at present many algorithms that are used for Information Extraction task, and they also have a good success rate, but there are no benchemarks or standard data on which they can be compared among themselves. Most of the algorithms work well in semi-structured data but they seem to fail when dealing with free text. Other algorithms fail when different types of data is required to extract from the same doument. One way, is to some how try to compare them all and then try to improve and create algorithms that are domain independent and work both efficiently and effectively. For this, we introduce an idea of formalizing Information Extractio algorithms and then to find out where can we improve them or what parts are still needed to improve the over all performance. We have in the end, described what we got by formalizing the algorithms and what can we achieve after formalinzing further algorithms.