Python (NLTK) - 提取名词短语的更有效方法？

Question

我有一个涉及大量文本数据的机器学习任务。我想识别并提取训练文本中的名词短语，以便稍后在管道中使用它们进行特征构建。我已经从文本中提取了我想要的名词短语类型，但我对 NLTK 还很陌生，所以我以一种可以分解列表理解中的每个步骤的方式来解决这个问题，如下所示。

但我真正的问题是，我是在重新发明轮子吗？有没有我没有看到的更快的方法？

import nltk
import pandas as pd

myData = pd.read_excel("\User\train_.xlsx")
texts = myData['message']

# Defining a grammar & Parser
NP = "NP: {(<V\w+>|<NN\w?>)+.*<NN\w?>}"
chunkr = nltk.RegexpParser(NP)

tokens = [nltk.word_tokenize(i) for i in texts]

tag_list = [nltk.pos_tag(w) for w in tokens]

phrases = [chunkr.parse(sublist) for sublist in tag_list]

leaves = [[subtree.leaves() for subtree in tree.subtrees(filter = lambda t: t.label == 'NP')] for tree in phrases]

将我们最终得到的元组列表的列表展平为只是元组列表的列表

leaves = [tupls for sublists in leaves for tupls in sublists]

将提取的术语合并为一个二元组

nounphrases = [unigram[0][1]+' '+unigram[1][0] in leaves]

Answer 1

看看，如果不需要中间步骤，则无需多次遍历所有行。

使用 ne_chunk 和

的解决方案

和

[代码]:

from nltk import word_tokenize, pos_tag, ne_chunk
from nltk import RegexpParser
from nltk import Tree
import pandas as pd

def get_continuous_chunks(text, chunk_func=ne_chunk):
    chunked = chunk_func(pos_tag(word_tokenize(text)))
    continuous_chunk = []
    current_chunk = []

    for subtree in chunked:
        if type(subtree) == Tree:
            current_chunk.append(" ".join([token for token, pos in subtree.leaves()]))
        elif current_chunk:
            named_entity = " ".join(current_chunk)
            if named_entity not in continuous_chunk:
                continuous_chunk.append(named_entity)
                current_chunk = []
        else:
            continue

    return continuous_chunk

df = pd.DataFrame({'text':['This is a foo, bar sentence with New York city.', 
                           'Another bar foo Washington DC thingy with Bruce Wayne.']})

df['text'].apply(lambda sent: get_continuous_chunks((sent)))

[输出]:

0                   [New York]
1    [Washington, Bruce Wayne]
Name: text, dtype: object

要使用自定义 RegexpParser :

from nltk import word_tokenize, pos_tag, ne_chunk
from nltk import RegexpParser
from nltk import Tree
import pandas as pd

# Defining a grammar & Parser
NP = "NP: {(<V\w+>|<NN\w?>)+.*<NN\w?>}"
chunker = RegexpParser(NP)

def get_continuous_chunks(text, chunk_func=ne_chunk):
    chunked = chunk_func(pos_tag(word_tokenize(text)))
    continuous_chunk = []
    current_chunk = []

    for subtree in chunked:
        if type(subtree) == Tree:
            current_chunk.append(" ".join([token for token, pos in subtree.leaves()]))
        elif current_chunk:
            named_entity = " ".join(current_chunk)
            if named_entity not in continuous_chunk:
                continuous_chunk.append(named_entity)
                current_chunk = []
        else:
            continue

    return continuous_chunk


df = pd.DataFrame({'text':['This is a foo, bar sentence with New York city.', 
                           'Another bar foo Washington DC thingy with Bruce Wayne.']})


df['text'].apply(lambda sent: get_continuous_chunks(sent, chunker.parse))

[输出]:

0                  [bar sentence, New York city]
1    [bar foo Washington DC thingy, Bruce Wayne]
Name: text, dtype: object

Answer 2

我建议参考之前的帖子：

他们建议使用 TextBlob 作为实现此目的的最简单方法（如果在处理方面不是最有效的方法）并且那里的讨论解决了您的问题。

from textblob import TextBlob
txt = """Natural language processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages."""
blob = TextBlob(txt)
print(blob.noun_phrases)

Answer 3

以上方法都没有给我所需的结果。以下是我建议的功能

from nltk import word_tokenize, pos_tag, ne_chunk
from nltk import RegexpParser
from nltk import Tree
import re


def get_noun_phrases(text):
    pos = pos_tag(word_tokenize(text))
    count = 0
    half_chunk = ""
    for word, tag in pos:
        if re.match(r"NN.*", tag):
            count+=1
            if count>=1:
                half_chunk = half_chunk + word + " "
        else:
            half_chunk = half_chunk+"---"
            count = 0
    half_chunk = re.sub(r"-+","?",half_chunk).split("?")
    half_chunk = [x.strip() for x in half_chunk if x!=""]
    return half_chunk

Python (NLTK) - 提取名词短语的更有效方法？

Python (NLTK) - more efficient way to extract noun phrases?

nlp

nltk

python-3.x

pandas

text-chunking