Question

您好我正在尝试根据此处的第二个示例从一串文本中提取关系：https://web.archive.org/web/20120907184244/http://nltk.googlecode.com/svn/trunk/doc/howto/relextract.html

从诸如“出版商周刊的迈克尔詹姆斯编辑”之类的字符串中，我想要的结果是输出如下：

[PER：'Michael James']'，编辑'[ORG：'Publishers Weekly']

这样做的最佳方法是什么？ extract_rels期望什么格式以及如何格式化输入以满足该要求？

试图自己做，但它不起作用。这是我从本书中改编的代码。我没有打印任何结果。我做错了什么？

class doc():
 pass

doc.headline = ['this is expected by nltk.sem.extract_rels but not used in this script']

def findrelations(text):
roles = """
(.*(                   
analyst|
editor|
librarian).*)|
researcher|
spokes(wo)?man|
writer|
,\sof\sthe?\s*  # "X, of (the) Y"
"""
ROLES = re.compile(roles, re.VERBOSE)
tokenizedsentences = nltk.sent_tokenize(text)
for sentence in tokenizedsentences:
    taggedwords  = nltk.pos_tag(nltk.word_tokenize(sentence))
    doc.text = nltk.batch_ne_chunk(taggedwords)
    print doc.text
    for rel in relextract.extract_rels('PER', 'ORG', doc, corpus='ieer', pattern=ROLES):
        print relextract.show_raw_rtuple(rel) # doctest: +ELLIPSIS

text =“出版商周刊的Michael James编辑”

findrelations（文本）

Answer 1

这里有一个基于你的代码（只需几个调整），效果很好;）

import nltk
import re 
from nltk.chunk import ne_chunk_sents
from nltk.sem import relextract


def findrelations(text):
    roles = """
    (.*(                   
    analyst|
    editor|
    librarian).*)|
    researcher|
    spokes(wo)?man|
    writer|
    ,\sof\sthe?\s*  # "X, of (the) Y"
    """
    ROLES = re.compile(roles, re.VERBOSE)

    sentences = nltk.sent_tokenize(text)
    tokenized_sentences = [nltk.word_tokenize(sentence) for sentence in sentences]
    tagged_sentences = [nltk.pos_tag(sentence) for sentence in tokenized_sentences]
    chunked_sentences = nltk.ne_chunk_sents(tagged_sentences)


    for doc in chunked_sentences:
        print doc
        for rel in relextract.extract_rels('PER', 'ORG', doc, corpus='ace', pattern=ROLES):
            #it is a tree, so you need to work on it to output what you want
            print relextract.show_raw_rtuple(rel) 

findrelations('Michael James editor of Publishers Weekly')

（S （PERSON Michael / NNP）（PERSON James / NNP）编辑/ NN 中/ IN （组织出版社/ NNS周刊/ NNP））

如何从NLTK中的文本中提取关系

1 个答案: