Mapping Entity Sets in News Archives Across Time

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...

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Bibliographic Details
Published in:Data Science and Engineering Vol. 4; no. 3; pp. 208 - 222
Main Authors: Duan, Yijun, Jatowt, Adam, Bhowmick, Sourav S., Yoshikawa, Masatoshi
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2019
Springer
Springer Nature B.V
SpringerOpen
Subjects:
Algorithm Analysis and Problem Complexity
Archives & records
Artificial Intelligence
Chemistry and Earth Sciences
Comparable entity mining
Computer Science
Data Mining and Knowledge Discovery
Database Management
Integer linear programming
Integer programming
Linear programming
Mapping
Physics
Statistics for Engineering
Systems and Data Security
Temporal embeddings alignment
Typicality analysis
Integer linear programming
Typicality analysis
Comparable entity mining
Temporal embeddings alignment
ISSN:2364-1185, 2364-1541
Online Access:Get full text
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Summary: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.
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ISSN:2364-1185
2364-1541
DOI:10.1007/s41019-019-00102-3