Question

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，以便稍后在管道中使用它们进行特征构造。我从文本中提取了我想要的名词短语的类型，但我对NLTK很新，所以我解决了这个问题，我可以分解列表理解中的每一步，如下所示。

但我真正的问题是，我在这里重新发明轮子吗？有没有更快的方法来做到这一点，我没有看到？

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列表展平为只是一个元组列表列表

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]

Answer 1

看看Why is my NLTK function slow when processing the DataFrame?，如果您不需要中间步骤，则无需多次遍历所有行。

使用ne_chunk和来自

的解决方案

[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)))

[OUT]：

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

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))

[OUT]：

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作为实现此目的的最简单方法（如果不是在处理方面最有效的方法），那么那里的讨论将解决您的问题。

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

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

Python（NLTK） - 提取名词短语的更有效方法？

3 个答案: