Source code for nltk.classify.senna

# Natural Language Toolkit: Senna Interface
# Copyright (C) 2001-2022 NLTK Project
# Author: Rami Al-Rfou' <>
# URL: <>
# For license information, see LICENSE.TXT

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/

>>> 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)] # doctest: +SKIP
[('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')]

from os import environ, path, sep
from platform import architecture, system
from subprocess import PIPE, Popen

from nltk.tag.api import TaggerI

[docs]class Senna(TaggerI): SUPPORTED_OPERATIONS = ["pos", "chk", "ner"]
[docs] def __init__(self, senna_path, operations, encoding="utf-8"): self._encoding = encoding self._path = path.normpath(senna_path) + sep # Verifies the existence of the executable on the self._path first # senna_binary_file_1 = self.executable(self._path) exe_file_1 = self.executable(self._path) if not path.isfile(exe_file_1): # Check for the system environment if "SENNA" in environ: # self._path = path.join(environ['SENNA'],'') self._path = path.normpath(environ["SENNA"]) + sep exe_file_2 = self.executable(self._path) if not path.isfile(exe_file_2): raise OSError( "Senna executable expected at %s or %s but not found" % (exe_file_1, exe_file_2) ) self.operations = operations
[docs] def executable(self, base_path): """ 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. """ os_name = system() if os_name == "Linux": bits = architecture()[0] if bits == "64bit": return path.join(base_path, "senna-linux64") return path.join(base_path, "senna-linux32") if os_name == "Windows": return path.join(base_path, "senna-win32.exe") if os_name == "Darwin": return path.join(base_path, "senna-osx") return path.join(base_path, "senna")
def _map(self): """ A method that calculates the order of the columns that SENNA pipeline will output the tags into. This depends on the operations being ordered. """ _map = {} i = 1 for operation in Senna.SUPPORTED_OPERATIONS: if operation in self.operations: _map[operation] = i i += 1 return _map
[docs] def tag(self, tokens): """ Applies the specified operation(s) on a list of tokens. """ return self.tag_sents([tokens])[0]
[docs] def tag_sents(self, sentences): """ 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. """ encoding = self._encoding if not path.isfile(self.executable(self._path)): raise OSError( "Senna executable expected at %s but not found" % self.executable(self._path) ) # Build the senna command to run the tagger _senna_cmd = [ self.executable(self._path), "-path", self._path, "-usrtokens", "-iobtags", ] _senna_cmd.extend(["-" + op for op in self.operations]) # Serialize the actual sentences to a temporary string _input = "\n".join(" ".join(x) for x in sentences) + "\n" if isinstance(_input, str) and encoding: _input = _input.encode(encoding) # Run the tagger and get the output p = Popen(_senna_cmd, stdin=PIPE, stdout=PIPE, stderr=PIPE) (stdout, stderr) = p.communicate(input=_input) senna_output = stdout # Check the return code. if p.returncode != 0: raise RuntimeError("Senna command failed! Details: %s" % stderr) if encoding: senna_output = stdout.decode(encoding) # Output the tagged sentences map_ = self._map() tagged_sentences = [[]] sentence_index = 0 token_index = 0 for tagged_word in senna_output.strip().split("\n"): if not tagged_word: tagged_sentences.append([]) sentence_index += 1 token_index = 0 continue tags = tagged_word.split("\t") result = {} for tag in map_: result[tag] = tags[map_[tag]].strip() try: result["word"] = sentences[sentence_index][token_index] except IndexError as e: raise IndexError( "Misalignment error occurred at sentence number %d. Possible reason" " is that the sentence size exceeded the maximum size. Check the " "documentation of Senna class for more information." % sentence_index ) from e tagged_sentences[-1].append(result) token_index += 1 return tagged_sentences