Knowledge Acquisition of Predicate Argument Structures from Technical Texts Using Machine Learning: The System ASIUM

In this paper, we describe the Machine Learning system, ASIUM, which learns Subcaterorization Frames of verbs and ontologies from the syntactic parsing of technical texts in natural language. The restrictions of selection in the subcategorization frames are filled by the ontology's concepts. Applications requiring such knowledge are crucial and numerous. The most direct applications are semantic control of texts and syntactic parsing disambiguation. This knowledge acquisition task cannot be fully automatically performed. Instead,we propose a cooperative ML method which provides the user with a global view of the acquisition task and also with acquisition tools like automatic concepts splitting, example generation, and an ontology view with attachments to the verbs. Validation steps using these features are intertwined with learning steps so that the user validates the concepts as they are learned. Experiments performed on two different corpora (cooking domain and patents) give very promising results.