What is the NLTK equivalent of UIMA CAS (General Annotation Framework)?
In UIMA, CAS (General Annotation Framework) plays an important role in structuring an NLP application . It allows you to pass metadata added by one component to the next component. For example, sentence boundaries from a sentence tokenizer can be added to the CAS and used by a subsequent word tokenizer.
What is the equivalent data structure in NLTK ?
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In short, there is no equivalent concept for CAS (Common Analysis System) in NLTK. The latter uses a much simpler means of text presentation than UIMA. In NLTK, texts are just lists of words , whereas in UIMA you have very complex (and heavy) data structures defined as part of the CAS for the purpose of describing input and flow through the UIMA system.
Having said that, I believe the two of them serve different purposes anyway. If I were to name the Java equivalent for NLTK, I would choose the OpenNLP toolkit over UIMA. The former offers a number of algorithms for machine learning-based NLP (as does NLTK, among others), while the latter is a component-based framework not only for NLP, but unstructured data in general. That is, it defines a general model for building applications that work with unstructured data.
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