 | Natural Language ToolkitNatural Language Toolkit is a suite of Python libraries and programs for symbolic and statistical natural language processing. |
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Natural Language Toolkit Description
Natural Language Toolkit is a suite of Python libraries and programs for symbolic and statistical natural language processing. Natural Language Toolkit is a suite of Python libraries and programs for symbolic and statistical natural language processing. NLTK includes graphical demonstrations and sample data.It is accompanied by extensive documentation, including tutorials that explain the underlying concepts behind the language processing tasks supported by the toolkit.Documentation:A substantial amount of documentation about how to use NLTK is available from the nltk home page: < >In particular, the NLTK home page contains three types of documentation:· Tutorials teach students how to use the toolkit, in the context of performing specific tasks. They are appropriate for anyone who wishes to learn how to use the toolkit. < /tutorial/ > · The toolkit's reference documentation describes every module, interface, class, method, function, and variable in the toolkit. This documentation should be useful to both users and developers. < /ref/nltk.html >· A number of technical reports are available. These reports explain and justify the toolkit's design and implementation. They are used by the developers of the toolkit to guide and document the toolkit's construction. Students can consult these reports if they would like further information about how the toolkit is designed and why it is designed that way. < /tech/ >What's New in This Release:NLTK:- Expanded semantics package for first order logic, linear logic, glue semantics, DRT, LFG (Dan Garrette)- new WordSense class in wordnet.synset supporting access to synsets from sense keys and accessing sense counts (Joel Nothman)- interface to Mallet's linear chain CRF implementation (nltk.tag.crf)- misc bugfixes incl Punkt, synsets, maxent- improved support for chunkers incl flexible chunk corpus reader, new rule type: ChunkRuleWithContext- new GUI for pos-tagged concordancing nltk.draw.pos_concordance- new GUI for developing regexp chunkers nltk.draw.rechunkparser - added bio_sents() and bio_words() methods to ConllChunkCorpusReader in conll.py to allow reading (word, tag, chunk_typ) tuples off of CoNLL-2000 corpus. Also modified ConllChunkCorpusView to support these changes.- feature structures support values with custom unification methods- new flag on tagged corpus readers to use simplified tagsets- new package for ngram language modeling with Katz backoff nltk.model- added classes for single-parented and multi-parented trees that automatically maintain parent pointers (nltk.tree.ParentedTree and nltk.tree.MultiParentedTree)- new WordNet browser GUI (Jussi Salmela, Paul Bone)- improved support for lazy sequences- added generate() method to probability distributions- more flexible parser for converting bracketed strings to trees- made fixes to docstrings to improve API documentationContrib (work in progress)- new NLG package, FUF/SURGE (Petro Verkhogliad) - new dependency parser package (Jason Narad)- new Coreference package, incl support for ACE-2, MUC-6 and MUC-7 corpora (Joseph Frazee)- CCG Parser (Graeme Gange)- first order resolution theorem prover (Dan Garrette)Data:- Nnw NPS Chat Corpus and corpus reader (nltk.corpus.nps_chat)- ConllCorpusReader can now be used to read CoNLL 2004 and 2005 corpora.- Implemented HMM-based Treebank POS tagger and phrase chunker for nltk_contrib.coref in api.py. Pickled versions of these objects are checked in in data/taggers and data/chunkers.Book:- misc corrections in response to feedback from readers What's New in This Release: · This version finalizes NLTK's API ahead of the 2.0 release and the publication of the NLTK book. There have been dozens of minor enhancements and bugfixes. Many names of the form nltk.foo.Bar are now available as nltk.Bar. There is expanded functionality in the decision tree, collocations, and Toolbox modules. A new translation toy nltk.misc.babelfish has been added. A new module nltk.help gives access to tagset documentation. Fixed imports so NLTK will build and install without Tkinter (for running on servers). New data includes a maximum entropy chunker model and updated grammars. NLTK Contrib includes updates to the coreference package (Joseph Frazee) and the ISRI Arabic stemmer (Hosam Algasaier). The book has undergone substantial editorial corrections ahead of final publication.
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