A Novel Burst-based Text Representation Model for Scalable Event Detection
Zhao, Xin and Chen, Rishan and Fan, Kai and Yan, Hongfei and Li, Xiaoming

Article Structure

Abstract

Mining retrospective events from text streams has been an important research topic.

Introduction

Mining retrospective events (Yang et al., 1998; Fung et al., 2007; Allan et al., 2000) has been quite an important research topic in text mining.

Burst-based Text Representation

In this section, we describe the proposed burst-based text representation model, denoted as BurstVSM.

The news articles in one day is treated as a batch.

et al., 2004) ,we parameterize p0 and p1 with the time index for each batch, formally, we have p0(t) and p1(t) for the with batch.

Evaluation

4.1 Experiment Setup

Topics

news articles

Appears in 3 sentences as: news articles (3)
In A Novel Burst-based Text Representation Model for Scalable Event Detection
  1. One standard way for that is to cluster news articles as events by following a two-step approach (Yang et al., 1998): 1) represent document as vectors and calculate similarities between documents; 2) run the clustering algorithm to obtain document clusters as events.1 Underlying text representation often plays a critical role in this approach, especially for long text streams.
    Page 1, “Introduction”
  2. D1 and D2 are news articles about U.S. presidential election respectively in years 2004 and 2008.
    Page 1, “Introduction”
  3. Since our major focus is to detect events from news articles , we only keep the web pages with keyword “news” in URL field.
    Page 3, “Evaluation”

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