Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
Information extraction with automatic knowledge expansion
Information Processing and Management: an International Journal
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Information Extraction originally tries to statically acquire information from various formatted documents in accordance with user-defined schema. Further, it can expand its application areas into the processing of purposeful user's utterance in natural language including various linguistic phenomena such as syntactic transformation and colloquial expression with frequently omitted words/phrases. We basically adopt verified lexico-semantic framework to obtain meaningful information from user's utterance and divide extraction phase into the two: the first is to extract and revise arguments and the other is to extract a predicate, which is an utterance meaning type of the input sentence.