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• W2102941498 abstract "In this thesis we present and evaluate two pattern matching based methods for answer extraction in textual question answering systems. A textual question answering system is a system that seeks answers to natural language questions from unstructured text. Textual question answering systems are an important research problem because as the amount of natural language text in digital format grows all the time, the need for novel methods for pinpointing important knowledge from the vast textual databases becomes more and more urgent. In addition to this, textual question answering systems form a well-defined framework with lots of existing evaluation data in which new methods can be developed and evaluated. The separate subproblem of developing answer extraction methods for extracting answers from unstructured text is an interesting problem not only by itself but also because it is quite similar to the problems of information extraction from text and of semantic annotation of text. Thus, answer extraction methods may be generalized for these problems also. In this thesis, we concentrate on developing methods for the automatic creation of answer extraction patterns. A new type of extraction patterns is developed as well. The pattern matching based approach chosen is interesting because of its language and application independence. The answer extraction methods are developed in the framework of our own question answering system. Publicly available datasets in English are used as training and evaluation data for the methods. The techniques developed" @default.
• W2102941498 created "2016-06-24" @default.
• W2102941498 creator A5084319902 @default.
• W2102941498 date "2007-06-12" @default.
• W2102941498 modified "2023-09-25" @default.
• W2102941498 title "Methods for Answer Extraction in Textual Question Answering" @default.
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