We present a demonstration of a newly developed text stream event detection method on over a million articles from the New York Times corpus. The event detection is designed to operate in a predominantly on-line fashion, reporting new events within a specified timeframe. The event detection is achieved by detecting significant changes in the statistical properties of the text where those properties are efficiently stored and updated in a suffix tree. This particular demonstration shows how our method is effective at discovering both short- and long-term events (which are often denoted topics), and how it automatically copes with topic drift on a corpus of 1 035 263 articles
This paper describes the term frequency patterns found in online news summaries published over a se...
When an important event happens, such as a terrorist attack or natural disaster, many people turn to...
Data mining in text streams, or text stream mining, is an increasingly im- portant topic for a numbe...
We present a demonstration of a newly developed text stream event detection method on over a million...
We present a demonstration of a newly developed text stream event detection method on over a million...
With the rise of social media and online newswire, text streams are attracting more and more researc...
Topic detection (TD) is an important area of research whose primary goal is to detect retrospective ...
Currently news flood spreads throughout the web. The techniques of Event Detection and Tracking make...
Detecting events from one or more temporally-ordered stream(s) of documents (e.g. news articles, blo...
In this work, we discuss and evaluate solutions to text classification problems associated with the ...
In this work, we discuss and evaluate solutions to text classification problems associated with the ...
The web has become the fastest growing and the most up to date source of information. Web mining is ...
Streams of short text, such as news titles, enable us to effectively and efficiently learn the real ...
This paper describes the term frequency patterns found in online news summaries published over a sev...
When an important event happens, such as a terrorist attack or natural disaster, many people turn to...
This paper describes the term frequency patterns found in online news summaries published over a se...
When an important event happens, such as a terrorist attack or natural disaster, many people turn to...
Data mining in text streams, or text stream mining, is an increasingly im- portant topic for a numbe...
We present a demonstration of a newly developed text stream event detection method on over a million...
We present a demonstration of a newly developed text stream event detection method on over a million...
With the rise of social media and online newswire, text streams are attracting more and more researc...
Topic detection (TD) is an important area of research whose primary goal is to detect retrospective ...
Currently news flood spreads throughout the web. The techniques of Event Detection and Tracking make...
Detecting events from one or more temporally-ordered stream(s) of documents (e.g. news articles, blo...
In this work, we discuss and evaluate solutions to text classification problems associated with the ...
In this work, we discuss and evaluate solutions to text classification problems associated with the ...
The web has become the fastest growing and the most up to date source of information. Web mining is ...
Streams of short text, such as news titles, enable us to effectively and efficiently learn the real ...
This paper describes the term frequency patterns found in online news summaries published over a sev...
When an important event happens, such as a terrorist attack or natural disaster, many people turn to...
This paper describes the term frequency patterns found in online news summaries published over a se...
When an important event happens, such as a terrorist attack or natural disaster, many people turn to...
Data mining in text streams, or text stream mining, is an increasingly im- portant topic for a numbe...