【NLP的python库(03/4) 】： 全面概述

一、说明 

        Python 对自然语言处理库有丰富的支持。从文本处理、标记化文本并确定其引理开始，到句法分析、解析文本并分配句法角色，再到语义处理，例如识别命名实体、情感分析和文档分类，一切都由至少一个库提供。那么，你从哪里开始呢？

        本文的目标是为每个核心 NLP 任务提供相关 Python 库的概述。这些库通过简要说明进行了解释，并给出了 NLP 任务的具体代码片段。继续我对 NLP 博客文章的介绍，本文仅显示用于文本处理、句法和语义分析以及文档语义等核心 NLP 任务的库。此外，在 NLP 实用程序类别中，还提供了用于语料库管理和数据集的库。

        涵盖以下库：

• NLTK
• TextBlob
• Spacy
• SciKit Learn
• Gensim 

二、核心自然语言处理任务

2.1 文本处理

任务：标记化、词形还原、词干提取、部分标记

NLTK 库为文本处理提供了一个完整的工具包，包括标记化、词干提取和词形还原。

from nltk.tokenize import sent_tokenize, word_tokenize

paragraph = '''Artificial intelligence was founded as an academic discipline in 1956, and in the years since it has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an "AI winter"), followed by new approaches, success, and renewed funding. AI research has tried and discarded many different approaches, including simulating the brain, modeling human problem solving, formal logic, large databases of knowledge, and imitating animal behavior. In the first decades of the 21st century, highly mathematical and statistical machine learning has dominated the field, and this technique has proved highly successful, helping to solve many challenging problems throughout industry and academia.'''

sentences = []
for sent in sent_tokenize(paragraph):
  sentences.append(word_tokenize(sent))

sentences[0]
# ['Artificial', 'intelligence', 'was', 'founded', 'as', 'an', 'academic', 'discipline'

        使用 TextBlob，支持相同的文本处理任务。它与NLTK的区别在于更高级的语义结果和易于使用的数据结构：解析句子已经生成了丰富的语义信息。

from textblob import TextBlob

text = '''
Artificial intelligence was founded as an academic discipline in 1956, and in the years since it has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an "AI winter"), followed by new approaches, success, and renewed funding. AI research has tried and discarded many different approaches, including simulating the brain, modeling human problem solving, formal logic, large databases of knowledge, and imitating animal behavior. In the first decades of the 21st century, highly mathematical and statistical machine learning has dominated the field, and this technique has proved highly successful, helping to solve many challenging problems throughout industry and academia.
'''

blob = TextBlob(text)

blob.ngrams()
#[WordList(['Artificial', 'intelligence', 'was']),
# WordList(['intelligence', 'was', 'founded']),
# WordList(['was', 'founded', 'as']),

blob.tokens
# WordList(['Artificial', 'intelligence', 'was', 'founded', 'as', 'an', 'academic', 'discipline', 'in', '1956', ',', 'and', 'in',

        借助现代 NLP 库 Spacy，文本处理只是主要语义任务的丰富管道中的第一步。与其他库不同，它需要首先加载目标语言的模型。最近的模型不是启发式的，而是人工神经网络，尤其是变压器，它提供了更丰富的抽象，可以更好地与其他模型相结合。

import spacy
nlp = spacy.load('en_core_web_lg')

text = '''
Artificial intelligence was founded as an academic discipline in 1956, and in the years since it has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an "AI winter"), followed by new approaches, success, and renewed funding. AI research has tried and discarded many different approaches, including simulating the brain, modeling human problem solving, formal logic, large databases of knowledge, and imitating animal behavior. In the first decades of the 21st century, highly mathematical and statistical machine learning has dominated the field, and this technique has proved highly successful, helping to solve many challenging problems throughout industry and academia.
'''

doc = nlp(text)
tokens = [token for token in doc]

print(tokens)
# [Artificial, intelligence, was, founded, as, an, academic, discipline

2.2 文本语法

任务：解析、词性标记、名词短语提取

        从 NLTK 开始，支持所有语法任务。它们的输出作为 Python 原生数据结构提供，并且始终可以显示为简单的文本输出。

from nltk.tokenize import word_tokenize
from nltk import pos_tag, RegexpParser

# Source: Wikipedia, Artificial Intelligence, https://en.wikipedia.org/wiki/Artificial_intelligence
text = '''
Artificial intelligence was founded as an academic discipline in 1956, and in the years since it has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an "AI winter"), followed by new approaches, success, and renewed funding. AI research has tried and discarded many different approaches, including simulating the brain, modeling human problem solving, formal logic, large databases of knowledge, and imitating animal behavior. In the first decades of the 21st century, highly mathematical and statistical machine learning has dominated the field, and this technique has proved highly successful, helping to solve many challenging problems throughout industry and academia.
'''

pos_tag(word_tokenize(text))
# [('Artificial', 'JJ'),
#  ('intelligence', 'NN'),
#  ('was', 'VBD'),
#  ('founded', 'VBN'),
#  ('as', 'IN'),
#  ('an', 'DT'),
#  ('academic', 'JJ'),
#  ('discipline', 'NN'),

# noun chunk parser
# source: https://www.nltk.org/book_1ed/ch07.html
grammar = "NP: {<DT>?<JJ>*<NN>}"
parser = RegexpParser(grammar)

parser.parse(pos_tag(word_tokenize(text)))
#(S
#  (NP Artificial/JJ intelligence/NN)
#  was/VBD
#  founded/VBN
#  as/IN
#  (NP an/DT academic/JJ discipline/NN)
#  in/IN
#  1956/CD

文本 Blob 在处理文本时立即提供 POS 标记。使用另一种方法，创建包含丰富语法信息的解析树。

from textblob import TextBlob

text = '''
Artificial intelligence was founded as an academic discipline in 1956, and in the years since it has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an "AI winter"), followed by new approaches, success, and renewed funding. AI research has tried and discarded many different approaches, including simulating the brain, modeling human problem solving, formal logic, large databases of knowledge, and imitating animal behavior. In the first decades of the 21st century, highly mathematical and statistical machine learning has dominated the field, and this technique has proved highly successful, helping to solve many challenging problems throughout industry and academia.
'''

blob = TextBlob(text)
blob.tags
#[('Artificial', 'JJ'),
# ('intelligence', 'NN'),
# ('was', 'VBD'),
# ('founded', 'VBN'),

blob.parse()
# Artificial/JJ/B-NP/O
# intelligence/NN/I-NP/O
# was/VBD/B-VP/O
# founded/VBN/I-VP/O

Spacy 库使用转换器神经网络来支持其语法任务。

import spacy
nlp = spacy.load('en_core_web_lg')

for token in nlp(text):
    print(f'{token.text:<20}{token.pos_:>5}{token.tag_:>5}')

#Artificial            ADJ   JJ
#intelligence         NOUN   NN
#was                   AUX  VBD
#founded              VERB  VBN

2.3 文本语义

任务：命名实体识别、词义消歧、语义角色标记

语义分析是NLP方法开始不同的领域。使用 NLTK 时，生成的语法信息将在字典中查找以识别例如命名实体。因此，在处理较新的文本时，可能无法识别实体。

from nltk import download as nltk_download
from nltk.tokenize import word_tokenize
from nltk import pos_tag, ne_chunk

nltk_download('maxent_ne_chunker')
nltk_download('words')

# Source: Wikipedia, Spacecraft, https://en.wikipedia.org/wiki/Spacecraft
text = '''
As of 2016, only three nations have flown crewed spacecraft: USSR/Russia, USA, and China. The first crewed spacecraft was Vostok 1, which carried Soviet cosmonaut Yuri Gagarin into space in 1961, and completed a full Earth orbit. There were five other crewed missions which used a Vostok spacecraft. The second crewed spacecraft was named Freedom 7, and it performed a sub-orbital spaceflight in 1961 carrying American astronaut Alan Shepard to an altitude of just over 187 kilometers (116 mi). There were five other crewed missions using Mercury spacecraft.
'''

pos_tag(word_tokenize(text))
# [('Artificial', 'JJ'),
#  ('intelligence', 'NN'),
#  ('was', 'VBD'),
#  ('founded', 'VBN'),
#  ('as', 'IN'),
#  ('an', 'DT'),
#  ('academic', 'JJ'),
#  ('discipline', 'NN'),

# noun chunk parser
# source: https://www.nltk.org/book_1ed/ch07.html
print(ne_chunk(pos_tag(word_tokenize(text))))
# (S
#   As/IN
#   of/IN
#   [...]
#   (ORGANIZATION USA/NNP)
#   [...]
#   which/WDT
#   carried/VBD
#   (GPE Soviet/JJ)
#   cosmonaut/NN
#   (PERSON Yuri/NNP Gagarin/NNP)

Spacy 库使用的转换器模型包含一个隐式的“时间戳”：它们的训练时间。这决定了模型使用了哪些文本，因此模型能够识别哪些文本。

import spacy
nlp = spacy.load('en_core_web_lg')

text = '''
As of 2016, only three nations have flown crewed spacecraft: USSR/Russia, USA, and China. The first crewed spacecraft was Vostok 1, which carried Soviet cosmonaut Yuri Gagarin into space in 1961, and completed a full Earth orbit. There were five other crewed missions which used a Vostok spacecraft. The second crewed spacecraft was named Freedom 7, and it performed a sub-orbital spaceflight in 1961 carrying American astronaut Alan Shepard to an altitude of just over 187 kilometers (116 mi). There were five other crewed missions using Mercury spacecraft.
'''

doc = nlp(paragraph)
for token in doc.ents:
    print(f'{token.text:<25}{token.label_:<15}')

# 2016                   DATE
# only three             CARDINAL
# USSR                   GPE
# Russia                 GPE
# USA                    GPE
# China                  GPE
# first                  ORDINAL
# Vostok 1               PRODUCT
# Soviet                 NORP
# Yuri Gagarin           PERSON

2.4 文档语义

任务：文本分类、主题建模、情感分析、毒性识别

情感分析也是NLP方法差异不同的任务：在词典中查找单词含义与在单词或文档向量上编码的学习单词相似性。

TextBlob 具有内置的情感分析，可返回文本中的极性（整体正面或负面内涵）和主观性（个人意见的程度）。

from textblob import TextBlob

text = '''
Artificial intelligence was founded as an academic discipline in 1956, and in the years since it has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an "AI winter"), followed by new approaches, success, and renewed funding. AI research has tried and discarded many different approaches, including simulating the brain, modeling human problem solving, formal logic, large databases of knowledge, and imitating animal behavior. In the first decades of the 21st century, highly mathematical and statistical machine learning has dominated the field, and this technique has proved highly successful, helping to solve many challenging problems throughout industry and academia.
'''

blob = TextBlob(text)
blob.sentiment
#Sentiment(polarity=0.16180290297937355, subjectivity=0.42155589508530683)

Spacy 不包含文本分类功能，但可以作为单独的管道步骤进行扩展。下面的代码很长，包含几个 Spacy 内部对象和数据结构 - 以后的文章将更详细地解释这一点。

## train single label categorization from multi-label dataset
def convert_single_label(dataset, filename):
    db = DocBin()
    nlp = spacy.load('en_core_web_lg')

    for index, fileid in enumerate(dataset):
        cat_dict = {cat: 0 for cat in dataset.categories()}
        cat_dict[dataset.categories(fileid).pop()] = 1

        doc = nlp(get_text(fileid))
        doc.cats = cat_dict

        db.add(doc)

    db.to_disk(filename)

## load trained model and apply to text
nlp = spacy.load('textcat_multilabel_model/model-best')

text = dataset.raw(42)

doc = nlp(text)

estimated_cats = sorted(doc.cats.items(), key=lambda i:float(i[1]), reverse=True)

print(dataset.categories(42))
# ['orange']

print(estimated_cats)
# [('nzdlr', 0.998894989490509), ('money-supply', 0.9969857335090637), ... ('orange', 0.7344251871109009),

SciKit Learn 是一个通用的机器学习库，提供许多聚类和分类算法。它仅适用于数字输入，因此需要对文本进行矢量化，例如使用 GenSims 预先训练的词向量，或使用内置的特征矢量化器。仅举一个例子，这里有一个片段，用于将原始文本转换为单词向量，然后将 KMeans分类器应用于它们。

from sklearn.feature_extraction import DictVectorizer
from sklearn.cluster import KMeans

vectorizer = DictVectorizer(sparse=False)
x_train = vectorizer.fit_transform(dataset['train'])

kmeans = KMeans(n_clusters=8, random_state=0, n_init="auto").fit(x_train)

print(kmeans.labels_.shape)
# (8551, )

print(kmeans.labels_)
# [4 4 4 ... 6 6 6]

最后，Gensim是一个专门用于大规模语料库的主题分类的库。以下代码片段加载内置数据集，矢量化每个文档的令牌，并执行聚类分析算法 LDA。仅在 CPU 上运行时，这些最多可能需要 15 分钟。

# source: https://radimrehurek.com/gensim/auto_examples/tutorials/run_lda.html, https://radimrehurek.com/gensim/auto_examples/howtos/run_downloader_api.html

import logging
import gensim.downloader as api
from gensim.corpora import Dictionary
from gensim.models import LdaModel

logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)

docs = api.load('text8')
dictionary = Dictionary(docs)
corpus = [dictionary.doc2bow(doc) for doc in docs]

_ = dictionary[0]
id2word = dictionary.id2token

# Define and train the model
model = LdaModel(
    corpus=corpus,
    id2word=id2word,
    chunksize=2000,
    alpha='auto',
    eta='auto',
    iterations=400,
    num_topics=10,
    passes=20,
    eval_every=None
)

print(model.num_topics)
# 10

print(model.top_topics(corpus)[6])
#  ([(4.201401e-06, 'done'),
#    (4.1998064e-06, 'zero'),
#    (4.1478743e-06, 'eight'),
#    (4.1257395e-06, 'one'),
#    (4.1166854e-06, 'two'),
#    (4.085097e-06, 'six'),
#    (4.080696e-06, 'language'),
#    (4.050306e-06, 'system'),
#    (4.041121e-06, 'network'),
#    (4.0385708e-06, 'internet'),
#    (4.0379923e-06, 'protocol'),
#    (4.035399e-06, 'open'),
#    (4.033435e-06, 'three'),
#    (4.0334166e-06, 'interface'),
#    (4.030141e-06, 'four'),
#    (4.0283044e-06, 'seven'),
#    (4.0163245e-06, 'no'),
#    (4.0149207e-06, 'i'),
#    (4.0072555e-06, 'object'),
#    (4.007036e-06, 'programming')],

三、公用事业

3.1 语料库管理

NLTK为JSON格式的纯文本，降价甚至Twitter提要提供语料库阅读器。它通过传递文件路径来创建，然后提供基本统计信息以及迭代器以处理所有找到的文件。

from  nltk.corpus.reader.plaintext import PlaintextCorpusReader

corpus = PlaintextCorpusReader('wikipedia_articles', r'.*\.txt')

print(corpus.fileids())
# ['AI_alignment.txt', 'AI_safety.txt', 'Artificial_intelligence.txt', 'Machine_learning.txt', ...]

print(len(corpus.sents()))
# 47289

print(len(corpus.words()))
# 1146248

Gensim 处理文本文件以形成每个文档的词向量表示，然后可用于其主要用例主题分类。文档需要由包装遍历目录的迭代器处理，然后将语料库构建为词向量集合。但是，这种语料库表示很难外部化并与其他库重用。以下片段是上面的摘录 - 它将加载 Gensim 中包含的数据集，然后创建一个基于词向量的表示。

import gensim.downloader as api
from gensim.corpora import Dictionary

docs = api.load('text8')
dictionary = Dictionary(docs)
corpus = [dictionary.doc2bow(doc) for doc in docs]

print('Number of unique tokens: %d' % len(dictionary))
# Number of unique tokens: 253854

print('Number of documents: %d' % len(corpus))
# Number of documents: 1701

3.2 数据

NLTK提供了几个即用型数据集，例如路透社新闻摘录，欧洲议会会议记录以及古腾堡收藏的开放书籍。请参阅完整的数据集和模型列表。

from nltk.corpus import reuters

print(len(reuters.fileids()))
#10788

print(reuters.categories()[:43])
# ['acq', 'alum', 'barley', 'bop', 'carcass', 'castor-oil', 'cocoa', 'coconut', 'coconut-oil', 'coffee', 'copper', 'copra-cake', 'corn', 'cotton', 'cotton-oil', 'cpi', 'cpu', 'crude', 'dfl', 'dlr', 'dmk', 'earn', 'fuel', 'gas', 'gnp', 'gold', 'grain', 'groundnut', 'groundnut-oil', 'heat', 'hog', 'housing', 'income', 'instal-debt', 'interest', 'ipi', 'iron-steel', 'jet', 'jobs', 'l-cattle', 'lead', 'lei', 'lin-oil']

SciKit Learn包括来自新闻组，房地产甚至IT入侵检测的数据集，请参阅完整列表。这是后者的快速示例。

from sklearn.datasets import fetch_20newsgroups

dataset = fetch_20newsgroups()
dataset.data[1]
# "From: guykuo@carson.u.washington.edu (Guy Kuo)\nSubject: SI Clock Poll - Final Call\nSummary: Final call for SI clock reports\nKeywords: SI,acceleration,clock,upgrade\nArticle-I.D.: shelley.1qvfo9INNc3s\nOrganization: University of Washington\nLines: 11\nNNTP-Posting-Host: carson.u.washington.edu\n\nA fair number of brave souls who upgraded their SI clock oscillator have\nshared their experiences for this poll.

四、结论

        对于 Python 中的 NLP 项目，存在大量的库选择。为了帮助您入门，本文提供了 NLP 任务驱动的概述，其中包含紧凑的库解释和代码片段。从文本处理开始，您了解了如何从文本创建标记和引理。继续语法分析，您学习了如何生成词性标签和句子的语法结构。到达语义，识别文本中的命名实体以及文本情感也可以在几行代码中解决。对于语料库管理和访问预结构化数据集的其他任务，您还看到了库示例。总而言之，本文应该让你在处理核心 NLP 任务时为下一个 NLP 项目提供一个良好的开端。

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