Showing posts with label NLP. Show all posts
Showing posts with label NLP. Show all posts

Friday, September 23, 2011

Blog: New Mathematical Model to Enable Web Searches for Meaning

New Mathematical Model to Enable Web Searches for Meaning
University of Hertfordshire (09/23/11) Paige Upchurch

University of Hertfordshire computer scientist Daoud Clarke has developed a mathematical model based on a theory of meaning that could revolutionize artificial intelligence technologies and enable Web searches to interpret the meaning of queries. The model is based on the idea that the meaning of words and phrases is determined by the context in which they occur. "This is an old idea, with its origin in the philosophy of Wittgenstein, and was later taken up by linguists, but this is the first time that someone has used it to construct a comprehensive theory of meaning," Clarke says. The model provides a way to represent words and phrases as sequences of numbers, known as vectors. "Our theory tells you what the vector for a phrase should look like in terms of the vectors for the individual words that make up the phrase," Clarke says. "Representing meanings of words using vectors allows fuzzy relationships between words to be expressed as the distance or angle between the vectors." He says the model could be applied to new types of artificial intelligence, such as determining the exact nature of a particular Web query.

Tuesday, July 12, 2011

Blog: Computer Learns Language By Playing Games

Computer Learns Language By Playing Games
MIT News (07/12/11) Larry Hardesty

Massachusetts Institute of Technology professor Regina Marzilay has adapted a system she developed to generate scripts for installing software on a Windows computer based on postings from a Microsoft help site to learn to play the Civilization computer game. The goal of the project was to demonstrate that computer systems that learn the meanings of words through exploratory interaction with their environments have much potential and deserve further research. The system begins with no prior knowledge about the task or the language in which the instructions are written, making the initial behavior almost completely random. As the system takes various actions, different words appear on the screen. The system finds those words in the instructions and develops hypotheses about what those words mean, based on the surrounding text. The hypotheses that consistently lead to good results are referred back to more frequently, while the hypotheses that are proven unsuccessful are discarded. In the case of the computer game, the system won 72 percent more often than a version of the same system that did not use the written instructions, and 27 percent more frequently than an artificial intelligence-based system.

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