Combining PPM Models Using A Text Mining Approach
DCC '01 Proceedings of the Data Compression Conference
Text categorization for streams
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Identification of gene function using prediction by partial matching (PPM) language models
Proceedings of the 17th ACM conference on Information and knowledge management
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An Application Program Interface (API) for modelling sequential text is described. The API is intended to shield the user from details of the modelling and probability estimation process. This should enable different implementations of models to be replaced transparently in application programs. The API is probabilistic: that is, it supplies the probability of the next symbol in the sequence. It is general enough to deal accurately with models that include escapes for probabilities. The concepts abstracted by the API are explained together with details of the API calls. The motivation for this API is work on the use of textual models for applications in addition to strict data compression, e.g. determination of the source of text, spelling correction or segmentation of text by inserting spaces. Users of the models do not want to be concerned about the details either of the implementation of the models or how they were trained and the sources of the training text. The problem considered in this paper is how to permit code for different models and actual trained models themselves to be interchanged easily between users. The fundamental idea is that it should be possible to write application programs independent of the details of particular modelling code, that it should be possible to implement different modelling code independent of the various applications, and that it should be possible to easily exchange different pre-trained models between users. It is hoped that this independence will foster the exchange and use of high performance modelling code; the construction of sophisticated adaptive systems based on the best available models; and the proliferation and provision of high quality models of standard text types such as English or other natural languages; and easy comparison of different modelling techniques.