python – 使用NLTK简化法语POS标签集
作者：互联网
如何简化斯坦福法国POS标签器返回的部分语音标签？将英文句子读入NLTK相当容易,找到每个单词的词性,然后使用map_tag()来简化标签集：
#!/usr/bin/python
# -*- coding: utf-8 -*-
import os
from nltk.tag.stanford import POSTagger
from nltk.tokenize import word_tokenize
from nltk.tag import map_tag
#set java_home path from within script. Run os.getenv("JAVA_HOME") to test java_home
os.environ["JAVA_HOME"] = "C:\\Program Files\\Java\\jdk1.7.0_25\\bin"
english = u"the whole earth swarms with living beings, every plant, every grain and leaf, supports the life of thousands."
path_to_english_model = "C:\\Text\\Professional\\Digital Humanities\\Packages and Tools\\Stanford Packages\\stanford-postagger-full-2014-08-27\\stanford-postagger-full-2014-08-27\\models\\english-bidirectional-distsim.tagger"
path_to_jar = "C:\\Text\\Professional\\Digital Humanities\\Packages and Tools\\Stanford Packages\\stanford-postagger-full-2014-08-27\\stanford-postagger-full-2014-08-27\\stanford-postagger.jar"
#define english and french taggers
english_tagger = POSTagger(path_to_english_model, path_to_jar, encoding="utf-8")
#each tuple in list_of_english_pos_tuples = (word, pos)
list_of_english_pos_tuples = english_tagger.tag(word_tokenize(english))
simplified_pos_tags_english = [(word, map_tag('en-ptb', 'universal', tag)) for word, tag in list_of_english_pos_tuples]
print simplified_pos_tags_english
#output = [(u'the', u'DET'), (u'whole', u'ADJ'), (u'earth', u'NOUN'), (u'swarms', u'NOUN'), (u'with', u'ADP'), (u'living', u'NOUN'), (u'beings', u'NOUN'), (u',', u'.'), (u'every', u'DET'), (u'plant', u'NOUN'), (u',', u'.'), (u'every', u'DET'), (u'grain', u'NOUN'), (u'and', u'CONJ'), (u'leaf', u'NOUN'), (u',', u'.'), (u'supports', u'VERB'), (u'the', u'DET'), (u'life', u'NOUN'), (u'of', u'ADP'), (u'thousands', u'NOUN'), (u'.', u'.')]
但是我不确定如何将以下代码返回的法语标签映射到通用标记集：
#!/usr/bin/python
# -*- coding: utf-8 -*-
import os
from nltk.tag.stanford import POSTagger
from nltk.tokenize import word_tokenize
from nltk.tag import map_tag
#set java_home path from within script. Run os.getenv("JAVA_HOME") to test java_home
os.environ["JAVA_HOME"] = "C:\\Program Files\\Java\\jdk1.7.0_25\\bin"
french = u"Chaque plante, chaque graine, chaque particule de matière organique contient des milliers d'atomes animés."
path_to_french_model = "C:\\Text\\Professional\\Digital Humanities\\Packages and Tools\\Stanford Packages\\stanford-postagger-full-2014-08-27\\stanford-postagger-full-2014-08-27\\models\\french.tagger"
path_to_jar = "C:\\Text\\Professional\\Digital Humanities\\Packages and Tools\\Stanford Packages\\stanford-postagger-full-2014-08-27\\stanford-postagger-full-2014-08-27\\stanford-postagger.jar"
french_tagger = POSTagger(path_to_french_model, path_to_jar, encoding="utf-8")
list_of_french_pos_tuples = french_tagger.tag(word_tokenize(french))
#up to this point all is well, but I'm not sure how to successfully create a simplified pos tagset with the French tuples
simplified_pos_tags_french = [(word, map_tag('SOME_ARGUMENT', 'universal', tag)) for word, tag in list_of_french_pos_tuples]
print simplified_pos_tags_french
有谁知道如何简化斯坦福POS标记器中法语模型使用的默认标签集？对于其他人可以就此问题提出的任何见解,我将不胜感激.
解决方法:
我最终只是手动将斯坦福的POS标签映射到通用标签集.对于它的价值,上面的代码片段是一个略大的工作流程的一部分,旨在测量法语和英语句子之间的句法相似性.这是完整的代码,以防它帮助其他人：
#!/usr/bin/python
# -*- coding: utf-8 -*-
'''NLTK 3.0 offers map_tag, which maps the Penn Treebank Tag Set to the Universal Tagset, a course tag set with the following 12 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
We'll map Stanford's tag set to this tag set then compare the similarity between subregions of French and English sentences.'''
from __future__ import division
import os, math
from nltk.tag.stanford import POSTagger
from nltk.tokenize import word_tokenize
from nltk.tag import map_tag
from collections import Counter
#########################
# Create Tagset Mapping #
#########################
def create_french_to_universal_dict():
'''this function creates the dict we'll call below when we map french pos tags to the universal tag set'''
french_to_universal = {}
french_to_universal[u"ADJ"] = u"ADJ"
french_to_universal[u"ADJWH"] = u"ADJ"
french_to_universal[u"ADV"] = u"ADV"
french_to_universal[u"ADVWH"] = u"ADV"
french_to_universal[u"CC"] = u"CONJ"
french_to_universal[u"CLO"] = u"PRON"
french_to_universal[u"CLR"] = u"PRON"
french_to_universal[u"CLS"] = u"PRON"
french_to_universal[u"CS"] = u"CONJ"
french_to_universal[u"DET"] = u"DET"
french_to_universal[u"DETWH"] = u"DET"
french_to_universal[u"ET"] = u"X"
french_to_universal[u"NC"] = u"NOUN"
french_to_universal[u"NPP"] = u"NOUN"
french_to_universal[u"P"] = u"ADP"
french_to_universal[u"PUNC"] = u"."
french_to_universal[u"PRO"] = u"PRON"
french_to_universal[u"PROREL"] = u"PRON"
french_to_universal[u"PROWH"] = u"PRON"
french_to_universal[u"V"] = u"VERB"
french_to_universal[u"VIMP"] = u"VERB"
french_to_universal[u"VINF"] = u"VERB"
french_to_universal[u"VPP"] = u"VERB"
french_to_universal[u"VPR"] = u"VERB"
french_to_universal[u"VS"] = u"VERB"
#nb, I is not part of the universal tagset--interjections get mapped to X
french_to_universal[u"I"] = u"X"
return french_to_universal
french_to_universal_dict = create_french_to_universal_dict()
def map_french_tag_to_universal(list_of_french_tag_tuples):
'''this function reads in a list of tuples (word, pos) and returns the same list with pos mapped to universal tagset'''
return [ (tup[0], french_to_universal_dict[ tup[1] ]) for tup in list_of_french_tag_tuples ]
###############################
# Define Similarity Functions #
###############################
def counter_cosine_similarity(c1, c2):
'''this function reads in two counters and returns their cosine similarity'''
terms = set(c1).union(c2)
dotprod = sum(c1.get(k, 0) * c2.get(k, 0) for k in terms)
magA = math.sqrt(sum(c1.get(k, 0)**2 for k in terms))
magB = math.sqrt(sum(c2.get(k, 0)**2 for k in terms))
return dotprod / (magA * magB)
def longest_common_subsequence_length(a, b):
'''this function reads in two lists and returns the length of their longest common subsequence'''
table = [[0] * (len(b) + 1) for _ in xrange(len(a) + 1)]
for i, ca in enumerate(a, 1):
for j, cb in enumerate(b, 1):
table[i][j] = (
table[i - 1][j - 1] + 1 if ca == cb else
max(table[i][j - 1], table[i - 1][j]))
return table[-1][-1]
def longest_contiguous_subsequence_length(a, b):
'''this function reads in two lists and returns the length of their longest contiguous subsequence'''
table = [[0] * (len(b) + 1) for _ in xrange(len(a) + 1)]
l = 0
for i, ca in enumerate(a, 1):
for j, cb in enumerate(b, 1):
if ca == cb:
table[i][j] = table[i - 1][j - 1] + 1
if table[i][j] > l:
l = table[i][j]
return l
def calculate_syntactic_similarity(french_pos_tuples, english_pos_tuples):
'''this function reads in two lists of (word, pos) tuples and returns their cosine similarity, logest_common_subsequence, and longest_common_contiguous_sequence'''
french_pos_list = [tup[1] for tup in french_pos_tuples]
english_pos_list = [tup[1] for tup in english_pos_tuples]
french_pos_counter = Counter(french_pos_list)
english_pos_counter = Counter(english_pos_list)
cosine_similarity = counter_cosine_similarity(french_pos_counter, english_pos_counter)
lc_subsequence = longest_common_subsequence_length(french_pos_counter, english_pos_counter) / max(len(french_pos_list), len(english_pos_list))
lc_contiguous_subsequence = longest_contiguous_subsequence_length(french_pos_counter, english_pos_counter) / max(len(french_pos_list), len(english_pos_list))
return cosine_similarity, lc_subsequence, lc_contiguous_subsequence
###########################
# Parse POS with Stanford #
###########################
#set java_home path from within script. Run os.getenv("JAVA_HOME") to test java_home
os.environ["JAVA_HOME"] = "C:\\Program Files\\Java\\jdk1.7.0_25\\bin"
english = u"the whole earth swarms with living beings, every plant, every grain and leaf, supports the life of thousands."
french = u"Chaque plante, chaque graine, chaque particule de matière organique contient des milliers d'atomes animés."
#specify paths
path_to_english_model = "C:\\Text\\Professional\\Digital Humanities\\Packages and Tools\\Stanford Packages\\stanford-postagger-full-2014-08-27\\stanford-postagger-full-2014-08-27\\models\\english-bidirectional-distsim.tagger"
path_to_french_model = "C:\\Text\\Professional\\Digital Humanities\\Packages and Tools\\Stanford Packages\\stanford-postagger-full-2014-08-27\\stanford-postagger-full-2014-08-27\\models\\french.tagger"
path_to_jar = "C:\\Text\\Professional\\Digital Humanities\\Packages and Tools\\Stanford Packages\\stanford-postagger-full-2014-08-27\\stanford-postagger-full-2014-08-27\\stanford-postagger.jar"
#define english and french taggers
english_tagger = POSTagger(path_to_english_model, path_to_jar, encoding="utf-8")
french_tagger = POSTagger(path_to_french_model, path_to_jar, encoding="utf-8")
#each tuple in list_of_english_pos_tuples = (word, pos)
list_of_english_pos_tuples = english_tagger.tag(word_tokenize(english))
list_of_french_pos_tuples = french_tagger.tag(word_tokenize(french))
#simplify each tagset
simplified_pos_tags_english = [(word, map_tag('en-ptb', 'universal', tag)) for word, tag in list_of_english_pos_tuples]
simplified_pos_tags_french = map_french_tag_to_universal( list_of_french_pos_tuples )
print calculate_syntactic_similarity(simplified_pos_tags_french, simplified_pos_tags_english)
标签：stanford-nlp,python,syntax,nlp,nltk 来源： https://codeday.me/bug/20191006/1858350.html