7

我有一个涉及大量文本数据的机器学习任务。我想在训练文本中识别和提取名词短语，以便稍后在管道中将它们用于特征构建。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推导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，我是否在这里重新发明轮子？有没有我看不到的更快的方法来做到这一点？

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一个元组列表的列表

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

将提取的术语加入一个二元组

nounphrases = [unigram[0][1]+' '+unigram[1][0] in leaves]
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3 回答 3

12

看看为什么我的 NLTK 函数在处理 DataFrame 时很慢？，如果您不需要中间步骤，则无需多次遍历所有行。

和ne_chunk解决方案来自

[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)))

[出去]：

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))

[出去]：

0                  [bar sentence, New York city]
1    [bar foo Washington DC thingy, Bruce Wayne]
Name: text, dtype: object
于 2018-03-31T04:33:53.573 回答
1

我建议参考这个先前的线程： Extracting all Nouns from a text file using nltk

他们建议使用 TextBlob 作为实现这一目标的最简单方法（如果不是在处理方面最有效的方法），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)
于 2020-11-20T00:05:38.730 回答
0

上述方法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
于 2021-02-11T09:08:02.693 回答