Python(NLTK)-提取名词短语的更有效方法? [英] Python (NLTK) - more efficient way to extract noun phrases?

问题描述

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并提取名词短语，以便稍后在管道中将其用于特征构建. 我已经从文本中提取了我想要的名词短语的类型，但是我对NLTK还是很陌生，所以我以一种可以分解列表理解的每一步的方式来解决这个问题，如下所示.

I've got a machine learning task involving a large amount of text data. I want to identify, and extract, noun-phrases in the training text so I can use them for feature construction later on in the pipeline. I've extracted the type of noun-phrases I wanted from text but I'm fairly new to NLTK, so I approached this problem in a way where I can break down each step in list comprehensions like you can see below.

但是我真正的问题是，我在这里重塑车轮吗?有没有一种我看不到的更快的方法?

But my real question is, am I reinventing the wheel here? Is there a faster way to do this that I'm not seeing?

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放宽到 只是元组列表的列表

flatten the list of lists of lists of tuples that we've ended up with, into just a list of lists of tuples

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

将提取的术语加入一个二元组

Join the extracted terms into one bigram

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

推荐答案

看看

Take a look at Why is my NLTK function slow when processing the DataFrame?, there's no need to iterate through all rows multiple times if you don't need intermediate steps.

使用ne_chunk和

• NLTK命名实体识别到Python列表和

如何提取GPE(位置)使用NLTK ne_chunk?

[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

这篇关于Python(NLTK)-提取名词短语的更有效方法?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！