NLP（六） 分块、句法分析、依存分析

原文链接：http://www.one2know.cn/nlp6/

• 内置分块器
  分块：从文本中抽取短语

import nltk

text = 'Lalbagh Botanical Garden is a well known botanical garden in Bengaluru, India.'

# 文本切割成多个句子
sentences = nltk.sent_tokenize(text)
for sentence in sentences:
    words = nltk.word_tokenize(sentence)
    tags = nltk.pos_tag(words)
    chunks = nltk.ne_chunk(tags) # 实现分块，返回树结构
    print(chunks)

输出：

(S
  (PERSON Lalbagh/NNP)
  (PERSON Botanical/NNP Garden/NNP)
  is/VBZ
  a/DT
  well/RB
  known/VBN
  botanical/JJ
  garden/NN
  in/IN
  (GPE Bengaluru/NNP)
  ,/,
  (GPE India/NNP)
  ./.)

• 编写简单的RE分块器

import nltk

text = 'Ravi is the CEO of a company. He is very powerful public speaker also.'

# 词性语法规则
grammar = '\n'.join([
    'NP: {<DT>*<NNP>}', # 一个或多个DT后紧跟一个NNP
    'NP: {<JJ>*<NN>}', # 一个或多个JJ后紧跟一个NN
    'NP: {<NNP>+}', # 一个或多个NNP组成
])

sentences = nltk.sent_tokenize(text)
for sentence in sentences:
    words = nltk.word_tokenize(sentence)
    tags = nltk.pos_tag(words)
    # 将语法规则放到RegexpParser对象中
    chunkparser = nltk.RegexpParser(grammar)
    result = chunkparser.parse(tags)
    print(result)

输出：

(S
  (NP Ravi/NNP)
  is/VBZ
  (NP the/DT CEO/NNP)
  of/IN
  a/DT
  (NP company/NN)
  ./.)
(S
  He/PRP
  is/VBZ
  very/RB
  (NP powerful/JJ public/JJ speaker/NN)
  also/RB
  ./.)

• 训练分块器
  IOB标注格式：

列	描述
IOB第一列	输入句子中的单词
IOB第二列	单词对应的词性
IOB第三列	I（内部词），O（外部词），B（开始词）；加上词种类的后缀
例子：
Rockwell NNP B-NP
International NNP I-NP
Corp. NNP I-NP
's POS B-NP
Tulsa NNP I-NP
unit NN I-NP
said VBD B-VP
it PRP B-NP
代码：

import nltk
from nltk.corpus import conll2000,treebank_chunk # 两个数据集

# 简单的分块器，抽取NNP(专有名词)
def mySimpleChunker():
    grammar = 'NP: {<NNP>+}'
    return nltk.RegexpParser(grammar)

# 不抽取任何东西，只用于检验算法能否正常运行
def test_nothing(data):
    cp = nltk.RegexpParser("")
    print(cp.evaluate(data))

# 测试mySimpleChunker()函数
def test_mysimplechunker(data):
    schunker = mySimpleChunker()
    print(schunker.evaluate(data))

datasets = [
    conll2000.chunked_sents('test.txt',chunk_types=['NP']),
    treebank_chunk.chunked_sents(),
]

# 前50个IOB标注语句 计算分块器的准确率
for dataset in datasets:
    test_nothing(dataset[:50])
    print('---------------------')
    test_mysimplechunker(dataset[:50])
    print()

输出：

ChunkParse score:
    IOB Accuracy:  38.6%%
    Precision:      0.0%%
    Recall:         0.0%%
    F-Measure:      0.0%%
---------------------
ChunkParse score:
    IOB Accuracy:  48.2%%
    Precision:     71.1%%
    Recall:        17.2%%
    F-Measure:     27.7%%

ChunkParse score:
    IOB Accuracy:  45.0%%
    Precision:      0.0%%
    Recall:         0.0%%
    F-Measure:      0.0%%
---------------------
ChunkParse score:
    IOB Accuracy:  50.7%%
    Precision:     51.9%%
    Recall:         8.8%%
    F-Measure:     15.1%%

• 递归下降句法分析
  递归先序遍历句法分析树
  NLTK的RD分析器

import nltk

def RDParserExample(grammar,textlist):
    # RecursiveDescentParser递归下降分析器
    parser = nltk.parse.RecursiveDescentParser(grammar)
    for text in textlist:
        sentence = nltk.word_tokenize(text)
        for tree in parser.parse(sentence):
            print(tree)
            tree.draw()

# 利用grammar创建CFG对象
grammar = nltk.CFG.fromstring("""
S -> NP VP
NP -> NNP VBZ
VP -> IN NNP | DT NN IN NNP
NNP -> 'Tajmahal' | 'Agra' | 'Bangalore' | 'Karnataka'
VBZ -> 'is'
IN -> 'in' | 'of'
DT -> 'the'
NN -> 'capital'
""")

# 测试
text = [
    "Tajmahal is in Agra",
    "Bangalore is the capital of Karnataka",
]
RDParserExample(grammar,text)

输出：

(S (NP (NNP Tajmahal) (VBZ is)) (VP (IN in) (NNP Agra)))
(S
  (NP (NNP Bangalore) (VBZ is))
  (VP (DT the) (NN capital) (IN of) (NNP Karnataka)))

• shift-reduce句法分析
  shift-reduce句法分析器：从左到右单线程，也可以从上到下多线程

import nltk

def SRParserExample(grammer,textlist):
    parser = nltk.parse.ShiftReduceParser(grammer)
    for text in textlist:
        sentence = nltk.word_tokenize(text)
        for tree in parser.parse(sentence):
            print(tree)
            tree.draw()

grammar = nltk.CFG.fromstring("""
S -> NP VP
NP -> NNP VBZ
VP -> IN NNP | DT NN IN NNP
NNP -> 'Tajmahal' | 'Agra' | 'Bangalore' | 'Karnataka'
VBZ -> 'is'
IN -> 'in' | 'of'
DT -> 'the'
NN -> 'capital'
""")

text = [
    "Tajmahal is in Agra",
    "Bangalore is the capital of Karnataka",
]

SRParserExample(grammar,text)

输出：

(S (NP (NNP Tajmahal) (VBZ is)) (VP (IN in) (NNP Agra)))

• 依存句法分析和主观依存分析

import nltk

# 依存相关规则
grammar = nltk.grammar.DependencyGrammar.fromstring("""
'savings' -> 'small'
'yield' -> 'savings'
'gains' -> 'large'
'yield' -> 'gains'
""")

sentence = 'small savings yield large gains'
dp = nltk.parse.ProjectiveDependencyParser(grammar)
print(sorted(dp.parse(sentence.split())))
for t in sorted(dp.parse(sentence.split())):
    print(t)
    t.draw()

输出：

[Tree('yield', [Tree('savings', ['small']), Tree('gains', ['large'])])]
(yield (savings small) (gains large))

• 线图句法分析

from nltk.grammar import CFG
from nltk.parse.chart import ChartParser,BU_LC_STRATEGY

# BNF格式文法 开始符号：S 终结符号：单词
grammar = CFG.fromstring("""
S -> T1 T4
T1 -> NNP VBZ
T2 -> DT NN
T3 ->IN NNP
T4 -> T3 | T2 T3
NNP -> 'Tajmahal' | 'Agra' | 'Bangalore' | 'Karnataka'
VBZ -> 'is'
IN -> 'in' | 'of'
DT -> 'the'
NN -> 'capital'
""")

cp = ChartParser(grammar,BU_LC_STRATEGY,trace=True)
# trace=True可以看见分析过程
# strategy=BU_LC_STRATEGY是默认的，不写好像也行

sentence = 'Bangalore is the capital of Karnataka'
tokens = sentence.split()
chart = cp.chart_parse(tokens) # 对单词列表分析，并存到chart对象 
parses = list(chart.parses(grammar.start())) # 将chart取到的所有分析树赋给parses
print('Total Edges :',len(chart.edges())) # 输出chart对象所有边的数量
for tree in parses: # 打印所有分析树
    print(tree)
tree.draw()

输出：

|.Bangal.  is  . the  .capita.  of  .Karnat.|
|[------]      .      .      .      .      .| [0:1] 'Bangalore'
|.      [------]      .      .      .      .| [1:2] 'is'
|.      .      [------]      .      .      .| [2:3] 'the'
|.      .      .      [------]      .      .| [3:4] 'capital'
|.      .      .      .      [------]      .| [4:5] 'of'
|.      .      .      .      .      [------]| [5:6] 'Karnataka'
|[------]      .      .      .      .      .| [0:1] NNP -> 'Bangalore' *
|[------>      .      .      .      .      .| [0:1] T1 -> NNP * VBZ
|.      [------]      .      .      .      .| [1:2] VBZ -> 'is' *
|[-------------]      .      .      .      .| [0:2] T1 -> NNP VBZ *
|[------------->      .      .      .      .| [0:2] S  -> T1 * T4
|.      .      [------]      .      .      .| [2:3] DT -> 'the' *
|.      .      [------>      .      .      .| [2:3] T2 -> DT * NN
|.      .      .      [------]      .      .| [3:4] NN -> 'capital' *
|.      .      [-------------]      .      .| [2:4] T2 -> DT NN *
|.      .      [------------->      .      .| [2:4] T4 -> T2 * T3
|.      .      .      .      [------]      .| [4:5] IN -> 'of' *
|.      .      .      .      [------>      .| [4:5] T3 -> IN * NNP
|.      .      .      .      .      [------]| [5:6] NNP -> 'Karnataka' *
|.      .      .      .      .      [------>| [5:6] T1 -> NNP * VBZ
|.      .      .      .      [-------------]| [4:6] T3 -> IN NNP *
|.      .      .      .      [-------------]| [4:6] T4 -> T3 *
|.      .      [---------------------------]| [2:6] T4 -> T2 T3 *
|[=========================================]| [0:6] S  -> T1 T4 *
Total Edges : 24
(S
  (T1 (NNP Bangalore) (VBZ is))
  (T4 (T2 (DT the) (NN capital)) (T3 (IN of) (NNP Karnataka))))