Concordance Comparison as a Means of Assembling Local Grammars

TL;DR

This paper presents a method using concordance comparison of local grammars to improve person name recognition in Portuguese texts, achieving a 6-point F-Measure gain over the state-of-the-art.

Contribution

It introduces a novel approach to assemble local grammars by analyzing concordance differences, enhancing extraction performance.

Findings

Achieved an F-Measure of 76.86 on the HAREM dataset.

Identified relationships of inclusion, intersection, and disjunction among grammars.

Improved Portuguese person name recognition by 6 points.

Abstract

Named Entity Recognition for person names is an important but non-trivial task in information extraction. This article uses a tool that compares the concordances obtained from two local grammars (LG) and highlights the differences. We used the results as an aid to select the best of a set of LGs. By analyzing the comparisons, we observed relationships of inclusion, intersection and disjunction within each pair of LGs, which helped us to assemble those that yielded the best results. This approach was used in a case study on extraction of person names from texts written in Portuguese. We applied the enhanced grammar to the Gold Collection of the Second HAREM. The F-Measure obtained was 76.86, representing a gain of 6 points in relation to the state-of-the-art for Portuguese.