spaCy 基本使用
关于 spaCy
相比 NLTK
- spacy 速度更快
- 可以和深度学习结合
安装
(base) $ python -m spacy download en
import spacy
加载英文模型，需要先安装模型 $ sudo python -m spacy download en
如果显示 ✘ Couldn't link model to 'en'
则注意执行命令前加上 sudo
# 加载英文模型
nlp = spacy.load('en')
doc = nlp('Start using Neo4j with built-in guides.Learn the basics of graph database technology ')
type(doc)
# spacy.tokens.doc.Doc
分词
for token in doc:
print(token)
'''
Start
using
Neo4j
with
built
-
in
guides
.
Learn
the
basics
of
graph
database
technology
'''
分句
for sent in doc.sents:
print(sent)
'''
Start using Neo4j within seconds, with built-in guides and sample datasets for popular use cases.
Learn the basics of graph database technology, from building a data model to deploying a graph-powered application.
'''
词性
for token in doc:
print('{} {}'.format(token, token.pos_))
'''
Start VERB
using VERB
Neo4j PROPN
within ADP
seconds NOUN
, PUNCT
with ADP
built VERB
- PUNCT
in ADP
guides NOUN
and CCONJ
sample NOUN
datasets NOUN
for ADP
popular ADJ
use NOUN
cases NOUN
. PUNCT
Learn VERB
the DET
basics NOUN
of ADP
graph PROPN
database NOUN
technology NOUN
, PUNCT
from ADP
building VERB
a DET
data NOUN
model NOUN
to ADP
deploying VERB
a DET
graph NOUN
- PUNCT
powered VERB
application NOUN
. PUNCT
'''
命名体识别
doc2 = nlp('arXiv is a free distribution service and an open-access archive for 1,812,439 scholarly articles. Materials on this site are not peer-reviewed by arXiv.')
for ent in doc2.ents:
print('{}, {} '.format(ent, ent.label_)) # 实体，类型
'''
arXiv, ORG
1,812,439, CARDINAL
arXiv, ORG
'''
from spacy import displacy
displacy.render(doc2, style='ent')
displacy.render(doc2, style='ent')
displacy.render(doc2, style='dep')
频次统计
# 找到一本书中 人名出现频次
def read_file(filename):
with open(filename, 'r') as file:
return file.read()
def read_file_to_list(filename):
with open(filename, 'r') as file:
return file.readlines()
text = read_file('data/pride_and_prejudice.txt')
precessed_text = nlp(text) # 执行的时间比较长
# 查看统计
# sents 内部并非字符串，而是 spacy.tokens.span.Span 类型
sents = [sent for sent in precessed_text.sents]
print(len(sents))
# 7153
from collections import Counter
def find_person(doc):
c = Counter()
names = []
for ent in precessed_text.ents:
if ent.label_ == 'PERSON':
# print(ent)
c[ent.lemma_] += 1
# names.append(ent)
return c.most_common(10)
find_person(precessed_text)
'''
[('Elizabeth', 600),
('Darcy', 355),
('Jane', 277),
('Bingley', 260),
('Bennet', 258),
('Collins', 166),
('Wickham', 108),
('Lizzy', 94),
('Gardiner', 90),
('Lady Catherine', 76)]
'''
恐怖袭击分析
text2 = read_file_to_list('data/rand-terrorism-dataset.txt')
text2[:5]
'''
['CHILE. An explosion from a single stick of dynamite went off on the patio of the Santiago Binational Center, causing $21,000 in damages.\n',
'ISRAEL. Palestinian terrorists fired five mortar shells into the collective settlement at Masada, causing slight damage but no injuries.\n',
'GUATEMALA. A bomb was thrown over the wall surrounding the U.S. Marines guards house in Guatemala City, causing damage but no injuries.\n',
'FRANCE. Five French students bombed the Paris offices of Chase Manhattan Bank before dawn. Trans-World Airways and the Bank of America were also bombed. They claimed to be protesting the U.S. involvement in the Vietnam war.\n',
'UNITED STATES - Unidentified anti-Castro Cubans attempted to bomb the Miami branch of the Spanish National Tourist Office.\n']
'''
# 哪些恐怖组织在哪些国家造成了多少次恐怖袭击
nlp2 = [nlp(art) for art in text2]
# nlp2.sents[:3]
# 选取下面常见数据来展示
common_terrorist_groups = [
'taliban',
'al - qaeda',
'hamas',
'fatah',
'plo',
'bilad al - rafidayn'
]
common_locations = [
'iraq',
'baghdad',
'kirkuk',
'mosul',
'afghanistan',
'kabul',
'basra',
'palestine',
'gaza',
'israel',
'istanbul',
'beirut',
'pakistan'
]
from collections import defaultdict
# 构建计数器
location_entity_dict = defaultdict(Counter)
# 找一个实体，识别每一篇文章
for art in nlp2:
art_terrorist_group = [ent.lemma_ for ent in art.ents if ent.label_ == 'PERSON' or ent.label_ == 'ORG'] # 恐怖组织或人
art_locations = [ent.lemma_ for ent in art.ents if ent.label_ == 'GPE'] #地点
# print(len(art_terrorist_group))
# print(len(art_locations))
# 剔除
terrorist_common = [ent for ent in art_terrorist_group if ent in common_terrorist_groups]
locations_common = [ent for ent in art_locations if ent in common_locations]
# 统计组织干了什么坏事
for found_ent in terrorist_common:
for found_loc in locations_common:
location_entity_dict[found_ent][found_loc] += 1
print(location_entity_dict)
defaultdict(<class 'collections.Counter'>, {})
location_entity_dict
# defaultdict(collections.Counter, {})
location_entity_dict = defaultdict(Counter)
for article in nlp2:
article_terrorist_groups = [ent.lemma_ for ent in article.ents if ent.label_=='PERSON' or ent.label_ =='ORG']#人或者组织
article_locations = [ent.lemma_ for ent in article.ents if ent.label_=='GPE']
terrorist_common = [ent for ent in article_terrorist_groups if ent in common_terrorist_groups]
locations_common = [ent for ent in article_locations if ent in common_locations]
for found_entity in terrorist_common:
for found_location in locations_common:
location_entity_dict[found_entity][found_location] += 1
location_entity_dict
# defaultdict(collections.Counter, {})
