A Rote Extractor with Edit Distance-Based Generalisation and Multi-Corpora Precision Calculation

Enrique Alfonseca, Pablo Castells, Manabu Okumura, Maria Ruiz-Casado

Abstract: In this paper, we describe a rote extractor that learns patterns for finding semantic relationships in unrestricted text, with new procedures for pattern generalization and scoring. These include the use of part-of-speech tags to guide the generalization, Named Entity categories inside the patterns, an edit-distance-based pattern generalization algorithm, and a pattern accuracy calculation procedure based on evaluating the patterns on several test corpora. In an evaluation with 14 entities, the system attains a precision higher than 50% for half of the relationships considered.