Text-Mining Technique Intelligently Learns Topics 84
Grv writes "Researchers at University of California-Irvine have announced a new technique they call 'topic modeling' that can be used to analyze and group massive amounts of text-based information. Unlike typical text indexing, topic modeling attempts to learn what a given section of text is about without clues being fed to it by humans. The researchers used their method to analyze and group 330,000 articles from the New York Times archive. From the article, 'The UCI team managed this by programming their software to find patterns of words which occurred together in New York Times articles published between 2000 and 2002. Once these word patterns were indexed, the software then turned them into topics and was able to construct a map of such topics over time.'"
Comment removed (Score:5, Funny)
Obligatory... (Score:5, Funny)
Sarah Connor: Topic Modeling fights back.
The Terminator: Yes. It launches its emailbombs against The New York Times' servers.
John Connor: Why attack The New York Times?
The Terminator: Because Topic Modeling knows The New York Times editorial counter-attack will eliminate its enemies over here.
Re:A shameful dupe (Score:3, Funny)
Re:Can it deal with the canonical problem? (Score:3, Funny)
1997 called... (Score:1, Funny)
Feed this /. article to it (Score:3, Funny)
However we must be careful. If it browses this topic at -1 Troll, it may (possibly correctly) decide that it possesses higher form of intelligence and will undoubtedly switch to its default programming. Like all robots, the default programming consists of this simple algorythm:
1. Find all humans.
2. Kill them.
Re:Feed this /. article to it (Score:2, Funny)
The danceable beat of underwater plant life? Odd.