Source code for nltk.tag.mapping

# Natural Language Toolkit: Tagset Mapping
#
# Copyright (C) 2001-2022 NLTK Project
# Author: Nathan Schneider <nathan@cmu.edu>
#         Steven Bird <stevenbird1@gmail.com>
# URL: <https://www.nltk.org/>
# For license information, see LICENSE.TXT

"""
Interface for converting POS tags from various treebanks
to the universal tagset of Petrov, Das, & McDonald.

The tagset consists of the following 12 coarse tags:

VERB - verbs (all tenses and modes)
NOUN - nouns (common and proper)
PRON - pronouns
ADJ - adjectives
ADV - adverbs
ADP - adpositions (prepositions and postpositions)
CONJ - conjunctions
DET - determiners
NUM - cardinal numbers
PRT - particles or other function words
X - other: foreign words, typos, abbreviations
. - punctuation

@see: https://arxiv.org/abs/1104.2086 and https://code.google.com/p/universal-pos-tags/

"""

from collections import defaultdict
from os.path import join

from nltk.data import load

_UNIVERSAL_DATA = "taggers/universal_tagset"
_UNIVERSAL_TAGS = (
    "VERB",
    "NOUN",
    "PRON",
    "ADJ",
    "ADV",
    "ADP",
    "CONJ",
    "DET",
    "NUM",
    "PRT",
    "X",
    ".",
)

# _MAPPINGS = defaultdict(lambda: defaultdict(dict))
# the mapping between tagset T1 and T2 returns UNK if applied to an unrecognized tag
_MAPPINGS = defaultdict(lambda: defaultdict(lambda: defaultdict(lambda: "UNK")))


def _load_universal_map(fileid):
    contents = load(join(_UNIVERSAL_DATA, fileid + ".map"), format="text")

    # When mapping to the Universal Tagset,
    # map unknown inputs to 'X' not 'UNK'
    _MAPPINGS[fileid]["universal"].default_factory = lambda: "X"

    for line in contents.splitlines():
        line = line.strip()
        if line == "":
            continue
        fine, coarse = line.split("\t")

        assert coarse in _UNIVERSAL_TAGS, f"Unexpected coarse tag: {coarse}"
        assert (
            fine not in _MAPPINGS[fileid]["universal"]
        ), f"Multiple entries for original tag: {fine}"

        _MAPPINGS[fileid]["universal"][fine] = coarse


[docs]def tagset_mapping(source, target): """ Retrieve the mapping dictionary between tagsets. >>> tagset_mapping('ru-rnc', 'universal') == {'!': '.', 'A': 'ADJ', 'C': 'CONJ', 'AD': 'ADV',\ 'NN': 'NOUN', 'VG': 'VERB', 'COMP': 'CONJ', 'NC': 'NUM', 'VP': 'VERB', 'P': 'ADP',\ 'IJ': 'X', 'V': 'VERB', 'Z': 'X', 'VI': 'VERB', 'YES_NO_SENT': 'X', 'PTCL': 'PRT'} True """ if source not in _MAPPINGS or target not in _MAPPINGS[source]: if target == "universal": _load_universal_map(source) # Added the new Russian National Corpus mappings because the # Russian model for nltk.pos_tag() uses it. _MAPPINGS["ru-rnc-new"]["universal"] = { "A": "ADJ", "A-PRO": "PRON", "ADV": "ADV", "ADV-PRO": "PRON", "ANUM": "ADJ", "CONJ": "CONJ", "INTJ": "X", "NONLEX": ".", "NUM": "NUM", "PARENTH": "PRT", "PART": "PRT", "PR": "ADP", "PRAEDIC": "PRT", "PRAEDIC-PRO": "PRON", "S": "NOUN", "S-PRO": "PRON", "V": "VERB", } return _MAPPINGS[source][target]
[docs]def map_tag(source, target, source_tag): """ Maps the tag from the source tagset to the target tagset. >>> map_tag('en-ptb', 'universal', 'VBZ') 'VERB' >>> map_tag('en-ptb', 'universal', 'VBP') 'VERB' >>> map_tag('en-ptb', 'universal', '``') '.' """ # we need a systematic approach to naming if target == "universal": if source == "wsj": source = "en-ptb" if source == "brown": source = "en-brown" return tagset_mapping(source, target)[source_tag]