Abstract
In recent years, Topic Detection and Tracking (TDT) has served as a core technology for searching, organizing and structuring news oriented textual materials from a variety of internet news and social media. The biggest challenges of TDT are the sparsity and complexity of data, organization of topic granularity, unexpectedness of emergency topics, and the unpredictability of topics evolution. This paper proposes a new TDT method based on event ontology for hierarchical topic detection and tracking topic evolution, named TDTEO. As domain-oriented event knowledge base, the event ontology provides event classes hierarchy based on domain common sense, as well a set of scenario models that describe the occurrence and evolution of different types of emergency events. The proposed method solves the problem that new emerging events are easy to be missed in the process of topic detection, and solves the problem that the topic model is easy to result in semantics drift due to the dynamic evolution of topic, and it can effectively improve the accuracy of topic detection and tracking. Experiments show our models achieve satisfactory performance of topic detection with a maximum macro-F1 value of 85.25%, and the (C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Det</sub> )Norm of topic tracking in the datasets is as low as 0.1028. Experimental results show that hierachical topic model can effectively detect the topics and tracking model based on event scenario can reflect the trend of topic evolution.
Highlights
The Topic Detection and Tracking program originated from a pilot study of technologies for automatically organizing news texts sponsored by DARPA in 1996 [1]
Aiming at the shortcomings of existing online topic detection and tracking methods, this paper proposes a topic detection and tracking method based on event ontology, named TDTEO (Topic Detection and Tracking based on Event Ontology)
In this paper, we propose a new TDT method based on event ontology for hierarchical topic detection and tracking topic evolution
Summary
The Topic Detection and Tracking program originated from a pilot study of technologies for automatically organizing news texts sponsored by DARPA in 1996 [1]. Adaptive topic tracking usually automatically learns the evolution trend of the topic and the drift trigger point based on system feedback. We take topic detection and tracking in the domain of Science and Technology as an example to study TDT based on event ontology. Update the topic model by using feature vectors (such as specific event participants, entities, places, etc.) created from the detected event instances to enhance the adaptability of the topic model. Find the corresponding event class and its scenario model in event ontology, and query its subsequent event classes (there exist sequence or causal relationship with it) and obtain the feature data from subsequent event classes to update the topic model.
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