nlp - 如何从NLTK中的文本提取关系

嗨，我正在尝试根据此处的倒数第二个示例从文本字符串中提取关系:https://web.archive.org/web/20120907184244/http://nltk.googlecode.com/svn/trunk/doc/howto/relextract.html

从诸如“Publishers Weekly的Michael James编辑器”之类的字符串中，我想要的结果是具有以下输出:



做到这一点的最佳方法是什么？ extract_rels期望采用什么格式？我该如何格式化输入以满足该要求？

尝试自己做，但是没有用。
这是我从书中改编的代码。我没有得到任何打印结果。我究竟做错了什么？

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

最佳答案

这里的代码基于您的代码(很少调整)，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
的/IN
(组织出版商/NNS周刊/NNP)

关于nlp - 如何从NLTK中的文本提取关系，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/12264593/