Abstract. In this paper, we propose an event words based method for story link detection. Different from previous studies, we use time and places to label nouns and named entities, the featured nouns/named entities are called event words. In our approach, a document is repre-sented by five dimensions including nouns/named entities, time featured nouns/named entities, place featured nouns/named entities, time&place featured nouns/named entities and publication date. Experimental re-sults show that, our method gain a significant improvement over baseline and event words plays a vital role in this improvement. Especially when using publication date, we can reach the highest 92 % on precision
We investigate the novel problem of event recognition from news webpages. "Events" are bas...
Interpreting news requires identifying its con-stituent events. Events are complex linguis-tically a...
Information linkage is becoming more and more important in this digital age. In this paper, we propo...
Several information organization, access, and filtering systems can benefit from different kind of d...
In this paper, we present a scheme for identifying instances of events and extracting information ab...
This paper deals with topic detection. Specifically link detection - finding similarities amongst a ...
First Story Detection (FSD) aims to identify the first story for an emerging event previously unrepo...
Story link detection has been regarded as a core technology for other Topic Detection and Tracking t...
In this work, we discuss and evaluate solutions to text classification problems associated with the ...
Event detection is an interesting task for many applications, for instance: surveillance, scientific...
With the overwhelming volume of online news available today, there is an increasing need for automat...
The correlation between events within the same document plays a crucial role in event detection, mos...
We present the STORIES methods and tool for (a) learning an abstracted story representation from a c...
This paper describes the use of connections between named entities for summarization of broadcast ne...
Story Link Detection (SLD) is known as a sub-task of Topic Detection and Tracking (TDT). SLD aims to...
We investigate the novel problem of event recognition from news webpages. "Events" are bas...
Interpreting news requires identifying its con-stituent events. Events are complex linguis-tically a...
Information linkage is becoming more and more important in this digital age. In this paper, we propo...
Several information organization, access, and filtering systems can benefit from different kind of d...
In this paper, we present a scheme for identifying instances of events and extracting information ab...
This paper deals with topic detection. Specifically link detection - finding similarities amongst a ...
First Story Detection (FSD) aims to identify the first story for an emerging event previously unrepo...
Story link detection has been regarded as a core technology for other Topic Detection and Tracking t...
In this work, we discuss and evaluate solutions to text classification problems associated with the ...
Event detection is an interesting task for many applications, for instance: surveillance, scientific...
With the overwhelming volume of online news available today, there is an increasing need for automat...
The correlation between events within the same document plays a crucial role in event detection, mos...
We present the STORIES methods and tool for (a) learning an abstracted story representation from a c...
This paper describes the use of connections between named entities for summarization of broadcast ne...
Story Link Detection (SLD) is known as a sub-task of Topic Detection and Tracking (TDT). SLD aims to...
We investigate the novel problem of event recognition from news webpages. "Events" are bas...
Interpreting news requires identifying its con-stituent events. Events are complex linguis-tically a...
Information linkage is becoming more and more important in this digital age. In this paper, we propo...