Recognition of Named-Event Passages in News Articles
This work addresses a domain-specific problem in natural language processing for news analysis, but it appears incremental as it builds on existing Named Entity concepts.
The paper tackles the problem of identifying passages in news articles that describe Named Events, such as battles and earthquakes, by extending the Named Entity concept. It reports preliminary evaluation results and introduces a method for collecting Gold Standard data using Amazon Mechanical Turk.
We extend the concept of Named Entities to Named Events - commonly occurring events such as battles and earthquakes. We propose a method for finding specific passages in news articles that contain information about such events and report our preliminary evaluation results. Collecting "Gold Standard" data presents many problems, both practical and conceptual. We present a method for obtaining such data using the Amazon Mechanical Turk service.