The Journal of Machine Learning Research
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
A method for relating multiple newspaper articles by using graphs, and its application to Webcasting
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
A study of topic similarity measures
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Tracking news stories across different sources
Proceedings of the 13th annual ACM international conference on Multimedia
Toward a Common Event Model for Multimedia Applications
IEEE MultiMedia
Storyline-based summarization for news topic retrospection
Decision Support Systems
Mining the change of event trends for decision support in environmental scanning
Expert Systems with Applications: An International Journal
Acquiring competitive intelligence from social media
Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data
| Hi-index | 0.00 |
Given the number of online sources for news, the volumes of news generated are so daunting that gaining insight from these collections become impossible without some aid to link them. Semantic linking of news articles facilitates grouping of similar or relevant news stories together for ease of human consumption. For example, a political analyst may like to have a single view of all news articles that report visits of State heads of different countries to a single country to make an in-depth analytical report on the possible impacts of all associated events. It is likely that no news source links all the relevant news together. In this paper, we discuss a multi-resolution, multi-perspective news analysis system that can link news articles collected from diverse sources over a period of time. The distinctive feature of the proposed news linking system is its capability to simultaneously link news articles and stories at multiple levels of granularity. At the lowest level several articles reporting the same event are linked together. Higher level groupings are more contextual and semantic. We have deployed a range of algorithms that use statistical text processing and Natural Language Processing techniques. The system is incremental in nature and depicts how stories have evolved over time along with main actors and activities. It also illustrates how a single story diverges into multiple themes or multiple stories converge due to conceptual similarity. Accuracy of linking thematically and conceptually linked news articles are also presented.