On Nov 29, 2007 5:31 AM, Reinier Lamers <[EMAIL PROTECTED]> wrote: > Especially in the fuzzy cases like this one, NLP often turns to machine > learning models. One could try to train a hidden Markov model or support > vector machines to label parts of the string as "name", "street", > "number", "city", etc. These techniques work very well for part of > speech tagging in natural language, and this seems similar. However, you > need a manually annotated set of examples to train the models. If you > really have a big load of data and it seems like a good solution, you > could use an off-the-shelf part-of-speech tagger like SVMTool > (http://www.lsi.upc.edu/~nlp/SVMTool/<http://www.lsi.upc.edu/%7Enlp/SVMTool/>) > to do it. > > Reinier
Hi Reinier, Thanks for the link to SVMTool. I don't have the basis to understand most of the NLP articles I found and get stuck on the first NLP's slang words. For me using an existing tool will be easier than build a new one. I'm currently looking at the tool's documentation and it looks quite promising. It seems to be very generic and highly reusable. Cheers, Olivier.
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