从数据中提取特定信息

Question

如何将数据格式转换为：

James Smith was born on November 17, 1948

变成类似

的东西

("James Smith", DOB, "November 17, 1948")

无需依赖字符串的位置索引

我试过以下方法

from nltk import word_tokenize, pos_tag

new = "James Smith was born on November 17, 1948"
sentences = word_tokenize(new)
sentences = pos_tag(sentences)
grammar = "Chunk: {<NNP*><NNP*>}"
cp = nltk.RegexpParser(grammar)
result = cp.parse(sentences)
print(result)

如何进一步进行以获得所需的输出。

Answer 1

用 'was born on' 拆分字符串，然后 trim 空格并分配给 name 和 dob

Answer 2

你总是可以使用正则表达式。正则表达式 (\S+)\s(\S+)\s\bwas born on\b\s(\S+)\s(\S+),\s(\S+) 将匹配和 return 来自上述字符串格式的数据。

这是实际操作：https://regex101.com/r/W2ykKS/1

python 中的正则表达式：

import re

regex = r"(\S+)\s(\S+)\s\bwas born on\b\s(\S+)\s(\S+),\s(\S+)"
test_str = "James Smith was born on November 17, 1948"

matches = re.search(regex, test_str)

# group 0 in a regex is the input string

print(matches.group(1)) # James
print(matches.group(2)) # Smith
print(matches.group(3)) # November
print(matches.group(4)) # 17
print(matches.group(5)) # 1948

从数据中提取特定信息

Extracting specific information from data

python

python-3.x

nltk

stanford-nlp

information-retrieval