Natural Language Processing with Python

NLTK Roadmap

The Natural Language Toolkit is a work in progress, and is being continually expanded as people contribute code. Some areas of NLP and linguistics are not (yet) well supported in NLTK, and contributions in these areas are especially welcome. Check http://www.nltk.org/ for news about developments after the publication date of this book. Contributions in the following areas are particularly encouraged:

Phonology and morphology

Computational approaches to the study of sound patterns and word structures typically use a finite-state toolkit. Phenomena such as suppletion and non-concatenative morphology are difficult to address using the string-processing methods we have been studying. The technical challenge is not only to link NLTK to a high-performance finite-state toolkit, but to avoid duplication of lexical data and to link the morphosyntactic features needed by morph analyzers and syntactic parsers.

High-performance components

Some NLP tasks are too computationally intensive for pure Python implementations to be feasible. However, in some cases the expense arises only when training models, not when using them to label inputs. NLTK’s package system provides a convenient way to distribute trained models, even models trained using corpora that cannot be freely distributed. Alternatives are to develop Python interfaces to high-performance machine learning tools, or to expand the reach of Python by using parallel programming techniques such as MapReduce.

Lexical semantics

This ...