Named Entity Recognition Toolkit
Project description
Named Entity Recognition Toolkit
Provide a toolkit for rapidly extracting useful entities from text using various Python packages.
Installation
pip install ner-kit
Examples
Example 1: Stanford CoreNLP
from nerkit.stanza import *
# First, set environment variable CORENLP_HOME to the CoreNLP folder
corenlp_root_path=r"{}\stanford-corenlp-4.3.2"
text="我喜欢游览广东孙中山故居景点!"
list_token=get_entity_list(text,corenlp_root_path=corenlp_root_path,language="chinese")
for token in list_token:
print(f"{token['value']}\t{token['pos']}\t{token['ner']}")
Example 2: HanLP
from nerkit.HanLP import *
text = "我喜欢游览广东孙中山故居景点!"
res=get_entity_list_by_hanlp(text,recognize='place')
print(res)
for s in res:
st=str(s)
ws=st.split("/")
if ws[1]=="nr":
print(ws[0],ws[1])
Example 3: Stanford CoreNLP (Not official version)
import os
from nerkit.StanfordCoreNLP import get_entity_list
text="我喜欢游览广东孙中山故居景点!"
current_path = os.path.dirname(os.path.realpath(__file__))
res=get_entity_list(text,resource_path=f"{current_path}/stanfordcorenlp/stanford-corenlp-latest/stanford-corenlp-4.3.2")
print(res)
for w,tag in res:
if tag in ['PERSON','ORGANIZATION','LOCATION']:
print(w,tag)
License
The ner-kit
project is provided by Donghua Chen.
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