nltk.classify.senna module

A general interface to the SENNA pipeline that supports any of the operations specified in SUPPORTED_OPERATIONS.

Applying multiple operations at once has the speed advantage. For example, Senna will automatically determine POS tags if you are extracting named entities. Applying both of the operations will cost only the time of extracting the named entities.

The SENNA pipeline has a fixed maximum size of the sentences that it can read. By default it is 1024 token/sentence. If you have larger sentences, changing the MAX_SENTENCE_SIZE value in SENNA_main.c should be considered and your system specific binary should be rebuilt. Otherwise this could introduce misalignment errors.

The input is:

  • path to the directory that contains SENNA executables. If the path is incorrect, Senna will automatically search for executable file specified in SENNA environment variable

  • List of the operations needed to be performed.

  • (optionally) the encoding of the input data (default:utf-8)

Note: Unit tests for this module can be found in test/unit/test_senna.py

>>> from nltk.classify import Senna
>>> pipeline = Senna('/usr/share/senna-v3.0', ['pos', 'chk', 'ner'])
>>> sent = 'Dusseldorf is an international business center'.split()
>>> [(token['word'], token['chk'], token['ner'], token['pos']) for token in pipeline.tag(sent)] 
[('Dusseldorf', 'B-NP', 'B-LOC', 'NNP'), ('is', 'B-VP', 'O', 'VBZ'), ('an', 'B-NP', 'O', 'DT'),
('international', 'I-NP', 'O', 'JJ'), ('business', 'I-NP', 'O', 'NN'), ('center', 'I-NP', 'O', 'NN')]
class nltk.classify.senna.Senna[source]

Bases: nltk.tag.api.TaggerI

SUPPORTED_OPERATIONS = ['pos', 'chk', 'ner']
__init__(senna_path, operations, encoding='utf-8')[source]
executable(base_path)[source]

The function that determines the system specific binary that should be used in the pipeline. In case, the system is not known the default senna binary will be used.

tag(tokens)[source]

Applies the specified operation(s) on a list of tokens.

tag_sents(sentences)[source]

Applies the tag method over a list of sentences. This method will return a list of dictionaries. Every dictionary will contain a word with its calculated annotations/tags.