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Topic Tracking of Accidental News Based on SVM

Author: WangQiang
Tutor: ZhangYongKui
School: Shanxi University
Course: Applied Computer Technology
Keywords: Accidental events Topic shift text classification Similarity calculation Topic tracking
CLC: TP391.1
Type: Master's thesis
Year: 2009
Downloads: 141
Quote: 1
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Abstract


Mobile wireless Internet makes it a very rich era of information. However, network expansion and messy drama without chapters, makes the discovery of valuable information and management become difficult. Because of Accidental events randomness and uncertainty, decision-makers may not available to comprehensive. In the information feedback and processing, information accuracy and effectiveness can not guarantee, resulting in distortion of information. How we can access to comprehensive and accurate reports of Accidental events and the evolution of that need to be addressed now.Topic detection can identify new topics in a stream of news stories and organize the news stories by topic. Topic tracking can track the given topics and obtain the relevant news stories in the news stream.so applying the topic detection, tracking techniques into the model will manage the information effectively. We track the sequential story of accidental event based on the certain topics people interested in ,which let people know the latest evolution of the event.We build a muti-vector space model for the Accidental events. By analysis text classification algorithm, we apply SVM classification algorithm into topic tracking. To find and track topic shift in topic tracking task, this paper proposes the improved topic tracking system, which detects the novelty information in topic tracking feedback and modifies topic model based on VSM, in order to track the topic shift effectively.The main work in this article:(1) By analyzing the processed corpus, we divided the text of the incident information into two types, objective information, and subjective information. And the use of the term will be characterized as a candidate feature words is divided into five categories (name, time, and place names, organization names, content) and the formation of the five sub-vector, with five sub-vector space model to table the document information, the location information word is special consideration when Weight calculation .(2) Link detection, based on the combination of multi-vector model and the SVM classification algorithm, which achieved good results.(3) To resolve the topic shift in topic tracking task, we build a topic tracking system based on improved core and innovative models.(4)We designed an experimental system to achieve topic link detection and topic tracking, It can track the sequential story of accidental news effectively. Finally, we use 10 topics from accidental news corpus, about 260 stories .The result shows that the method can improve the efficiency of tracking accidental events in a certain way.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Text Processing
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