A Knowledge Acquisition Method for Event Extraction and Coding Based on Deep Patterns

Abstract

A major problem in the field of peace and conflict studies is to extract events from a variety of news sources. The events need to be coded with an event type and annotated with entities from a domain specific ontology for future retrieval and analysis. The problem is dynamic in nature, characterised by new or changing groups and targets, and the emergence of new types of events. A number of automated event extraction systems exist that detect thousands of events on a daily basis. The resulting datasets, however, lack sufficient coverage of specific domains and suffer from too many duplicated and irrelevant events. Therefore expert event coding and validation is required to ensure sufficient quality and coverage of a conflict. We propose a new framework for semi-automatic rule-based event extraction and coding based on the use of deep syntactic-semantic patterns created from normal user input to an event annotation system. The method is implemented in a prototype Event Coding Assistant that processes news articles to suggest relevant events to a user who can correct or accept the suggestions. Over time as a knowledge base of patterns is built, event extraction accuracy improves and, as shown by analysis of system logs, the workload of the user is decreased.