Extracting Temporal and Causal Relations between Events

TL;DR

This paper introduces CATENA, an integrated system for extracting temporal and causal relations from text, enhancing NLP tasks like timeline summarization and question answering across multiple languages.

Contribution

The paper presents a novel framework combining temporal and causal relation extraction components into a unified system, with adaptations for Italian and Indonesian.

Findings

Effective extraction of temporal and causal relations demonstrated

Improved relation extraction with word embeddings and data expansion

Multilingual adaptation for non-English languages achieved

Abstract

Structured information resulting from temporal information processing is crucial for a variety of natural language processing tasks, for instance to generate timeline summarization of events from news documents, or to answer temporal/causal-related questions about some events. In this thesis we present a framework for an integrated temporal and causal relation extraction system. We first develop a robust extraction component for each type of relations, i.e. temporal order and causality. We then combine the two extraction components into an integrated relation extraction system, CATENA---CAusal and Temporal relation Extraction from NAtural language texts---, by utilizing the presumption about event precedence in causality, that causing events must happened BEFORE resulting events. Several resources and techniques to improve our relation extraction systems are also discussed, including word embeddings and training data expansion. Finally, we report our adaptation efforts of temporal information processing for languages other than English, namely Italian and Indonesian.