Source code for cltk.tag.ner

"""Named entity recognition (NER)."""

import importlib.machinery
import os

from nltk.tokenize.punkt import PunktLanguageVars

from import FetchCorpus
from cltk.tokenizers.word import PunktWordTokenizer
from cltk.utils import CLTK_DATA_DIR

__author__ = ["Natasha Voake <>"]
__license__ = "MIT License. See LICENSE."

    "grc": os.path.join(
        CLTK_DATA_DIR, "grc/model/grc_models_cltk/ner/proper_names.txt"
    "lat": os.path.join(
        CLTK_DATA_DIR, "lat/model/lat_models_cltk/ner/proper_names.txt"

[docs]class NamedEntityReplacer(object): def __init__(self): self.entities = self._load_necessary_data() def _load_necessary_data(self): rel_path = os.path.join( CLTK_DATA_DIR, "fro", "text", "fro_models_cltk", "" ) path = os.path.expanduser(rel_path) #'Loading entries. This may take a minute.') loader = importlib.machinery.SourceFileLoader("entities", path) module = loader.load_module() entities = module.entities return entities """tags named entities in a string and outputs a list of tuples in the following format: (name, "entity", kind_of_entity)"""
[docs] def tag_ner_fr(self, input_text, output_type=list): entities = self.entities for entity in entities: (name, kind) = entity word_tokenizer = PunktWordTokenizer() tokenized_text = word_tokenizer.tokenize(input_text) ner_tuple_list = [] match = False for word in tokenized_text: for name, kind in entities: if word == name: named_things = [(name, "entity", kind)] ner_tuple_list.append(named_things) match = True break else: ner_tuple_list.append((word,)) return ner_tuple_list
[docs]def _check_latest_data(lang): """Check for presence of proper names dir, clone if not.""" assert lang in NER_DICT.keys(), "Invalid language. Choose from: {}".format( ", ".join(NER_DICT.keys()) ) ner_file_path = os.path.expanduser(NER_DICT[lang]) if not os.path.isfile(ner_file_path): corpus_importer = FetchCorpus(lang) corpus_importer.import_corpus("{}_models_cltk".format(lang))
[docs]def tag_ner(lang, input_text, output_type=list): """Run NER for chosen language.""" _check_latest_data(lang) assert lang in NER_DICT.keys(), "Invalid language. Choose from: {}".format( ", ".join(NER_DICT.keys()) ) types = [str, list] assert type(input_text) in types, "Input must be: {}.".format(", ".join(types)) assert output_type in types, "Output must be a {}.".format(", ".join(types)) if type(input_text) == str: punkt = PunktLanguageVars() tokens = punkt.word_tokenize(input_text) new_tokens = [] for word in tokens: if word.endswith("."): new_tokens.append(word[:-1]) new_tokens.append(".") else: new_tokens.append(word) input_text = new_tokens ner_file_path = os.path.expanduser(NER_DICT[lang]) with open(ner_file_path) as file_open: ner_str = ner_list = ner_str.split("\n") ner_tuple_list = [] for count, word_token in enumerate(input_text): match = False for ner_word in ner_list: # the replacer slows things down, but is necessary if word_token == ner_word: ner_tuple = (word_token, "Entity") ner_tuple_list.append(ner_tuple) match = True break if not match: ner_tuple_list.append((word_token,)) if output_type is str: string = "" for tup in ner_tuple_list: start_space = " " final_space = "" # this is some mediocre string reconstitution # maybe not worth the effort if tup[0] in [",", ".", ";", ":", "?", "!"]: start_space = "" if len(tup) == 2: string += start_space + tup[0] + "/" + tup[1] + final_space else: string += start_space + tup[0] + final_space return string return ner_tuple_list