|Table of Contents|

Approach for topical sentence of news events extraction based on graph

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2016年04期
Page:
438-
Research Field:
Publishing date:

Info

Title:
Approach for topical sentence of news events extraction based on graph
Author(s):
Wang YongkaiMao CunliYu ZhengtaoGuo JianyiHong XudongLuo Lin
School of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China
Keywords:
news events event topic sentences trigger word named entity event relation undirected graph ranking extraction
PACS:
TP311
DOI:
10.14177/j.cnki.32-1397n.2016.40.04.010
Abstract:
News events topical sentence recognition task is a text-based semantic analysis of natural language processing technology.In order to accurately calculate the news text sentences that are most relevant to the topic,this paper proposes a novel approach for topical sentence of news events extraction based on a undirected graph.This paper describes the characteristics of an event trigger Word and sentence extraction named entity and constructs the candidated event extraction templates.This paper,calculates the relationship between candidated event sentences and constructs undirected graphs of event relation ship.Finally,based on the TextRank algorithm,the weight of any vertex in the graph is represented by the weighted sum of the vertex weights,and according to the sorted weights,the event topical sentences are extracted.Experimental results show that the proposed approach is better than TFIDF and event extraction method based on title and that F values are respectively 6.26% and 2%.

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Last Update: 2016-06-30