Integer-Programming Ensemble of Temporal-Relations Classifiers

TL;DR

This paper introduces an integer programming ensemble method that combines multiple classifiers to improve the extraction of temporal relations in text, demonstrating superior performance on benchmark challenges.

Contribution

The paper presents a novel ensemble approach using integer programming to enhance temporal relation classification in NLP tasks.

Findings

Ensemble method outperforms individual classifiers.

Improved results on SemEval-2013 TempEval-3.

Enhanced performance on SemEval-2016 Clinical TempEval.

Abstract

The extraction and understanding of temporal events and their relations are major challenges in natural language processing. Processing text on a sentence-by-sentence or expression-by-expression basis often fails, in part due to the challenge of capturing the global consistency of the text. We present an ensemble method, which reconciles the outputs of multiple classifiers of temporal expressions across the text using integer programming. Computational experiments show that the ensemble improves upon the best individual results from two recent challenges, SemEval-2013 TempEval-3 (Temporal Annotation) and SemEval-2016 Task 12 (Clinical TempEval).