Abstract
Knowledge-based information extraction is known to have flexibility in recognizing various kinds of target information by exploiting the domain knowledge to automatically generate information-extraction rules. However, most of previous knowledge-based information-extraction systems are only applicable to labeled documents, and as a result, ontology terms must appear in the document in order to guide the system to determine the existence of the target information.
To make a knowledge-based information-extraction system to be more general enough to handle both labeled and unlabeled documents, this paper proposes an enhanced scheme of knowledge-based wrapper generation by using token-based morphological patterns. Each document is represented as a sequence of tokens, rather than a sequence of logical lines, in order to capture the meaning of data fragments more correctly and recognize the target information contextually. The newly implemented system XTROS + is presented and its performance is demonstrated.
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Yang, J., Choi, J. (2003). Agents for Intelligent Information Extraction by Using Domain Knowledge and Token-Based Morphological Patterns. In: Lee, J., Barley, M. (eds) Intelligent Agents and Multi-Agent Systems. PRIMA 2003. Lecture Notes in Computer Science(), vol 2891. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39896-7_7
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DOI: https://doi.org/10.1007/978-3-540-39896-7_7
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