[1]宋丹 王卫东 陈英.基于改进向量空间模型的话题识别与跟踪[J].计算机技术与发展,2006,(09):62-64.
　SONG Dan,WANG Wei-dong,CHEN Ying.Topic Detection and Tracking with a Developed Vector Space Model[J].,2006,(09):62-64.
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基于改进向量空间模型的话题识别与跟踪()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2006年09期
页码:
62-64
栏目:
智能、算法、系统工程
出版日期:
1900-01-01

文章信息/Info

Title:
Topic Detection and Tracking with a Developed Vector Space Model
文章编号:
1673-629X（2006）09-0062-03
作者:
宋丹1 王卫东2 陈英2
[1]大连理工大学计算机科学与工程系[2]东北电力大学计算机系
Author(s):
SONG Dan;  WANG Wei-dong;  CHEN Ying
[1]Department of Computer Science and Engineering, Dalian University of Technology[2]Department of Computer Science, Northeast Dianli University
关键词:
话题识别与跟踪; 向量空间模型; 时间表达
Keywords:
topic detection and tracking;  vector space model;  temporal expressions
分类号:
TP18
文献标志码:
A
摘要:
话题识别与跟踪旨在发展一系列基于事件的信息组织技术，通过监测以实现对新闻媒体信息流中新话题的自动识别和已知话题的动态跟踪。文中提供一种利用改进的向量空间模型进行识别和跟踪的方法。没有使用传统向量空间模型中单个向量，而是按照语义将特征词划分为4个组（人物、时间、地点、内容）并形成4个向量空间。每个空间进行独立的权重计算和相似度计算。实验证明这些方法是有效的
Abstract:
Topic detection and tracking is an event - based information organization task where online new streams are monitored in order to spot new unreported events and link documents with previously detected events. So present an approach that formalizes temporal expressions and augments spatial terms with ontological information and uses this data in the dictation. In addition, instead using a single term vector as document representation, split the terms into four semantic classes and process, including character, time, space and content, and weigh the classes separately. The approach is motivated by experiment

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备注/Memo

备注/Memo:
宋丹（1980-），女，辽宁锦州人，硕士研究生．研究方向为话题识别与跟踪
更新日期/Last Update: 1900-01-01