Using a symbolic machine learning tool to refine lexico-syntactic patterns

Abstract

Acquisition of patterns for information extraction systems is a common task in Natural Language Processing, mostly based on manual analysis of text corpora. We have developed a system called PROMÉTHÉE, which incrementally extracts lexico-syntactic patterns for a specific conceptual relation from a technical corpus. However, these patterns are often too general and need to be manually validated. In this paper, we demonstrate how PROMÉTHÉE has been interfaced with the machine learning system EAGLE in order to automatically refine the patterns it produces. The empirical results obtained with this technique show that the refined patterns allows to decrease the need for the human validation.