Abstract
We propose a novel way of utilizing and accessing information stored in news archives as well as a new style of investigating the history. Our idea is to automatically generate similar entity pairs given two sets of entities, one from the past and one representing the present. This allows performing entity-oriented mapping between different times. We introduce an effective method to solve the aforementioned task based on a concise integer linear programming framework. In particular, our model first conducts typicality analysis to estimate entity representativeness. It next constructs orthogonal transformation between the two entity collections. The result is a set of typical across-time comparables. We demonstrate the effectiveness of our approach on the New York Times dataset through both qualitative and quantitative tests.
Highlights
Named entities are first class citizens in news and typically attract a lot of focus in any news article analysis
We first note that our joint integer linear programming framework (J-integer linear programming (ILP)) model achieves better results than the baselines based on both Correctness and Comprehensibility criteria
J-ILP outperforms baselines by 20.2% and 28.0% in terms of Correctness and Comprehensibility for comparison C1, and by 18.6% and 23.0% for C2, respectively. This observation proves that J-ILP has relatively good performance in detecting
Summary
Named entities are first class citizens in news and typically attract a lot of focus in any news article analysis. Automatic approaches toward effective named entity detection, disambiguation, linking and understanding have gathered much attention and have been studied since long ago. This is true for the case of archival news article collections, albeit ancient documents typically add additional challenges for those tasks. One of common objectives of studying the history is to compare it with the present for drawing informative, novel or interesting conclusions. In such acrosstime scenarios an entity-to-entity comparison is an intuitive
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
