Maximum Entropy Modeling in Semantic Tagging
نویسندگان
چکیده
The Maximum Entropy maxent principle has been successfully applied in classi cation and tagging tasks Compared with other statistical learning method it allows convenient integration of di erent knowledge sources However it is restricted by the size of the training corpus in that not all knowledge can be incorporated For instance events observed with low counts can be either a particular pattern or just a uke In our work human annotated data is limited but a huge amount of raw data is available and free Moreover extra information can be obtained from WordNet and dictionaries Our proposal will exploit these extra knowledge sources and raw auxiliary data in building reliable maxent models
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