工具分享: 中文实体标注工具

发现了一款比较方便标注的工具https://github.com/jiesutd/SUTDAnnotator ，使用python2编写的界面，相对比较轻量，适合个人使用。但如果是团体使用的，还是web界面的会比较好。

 

运行Annotator_backup.py得到如下界面：open导入文件,选中要标注的词语，按下已设定好的快捷键A-V，即可完成对应的标注

            

 修改添加上自己所需的实体名：在对应的快捷键的右边输入实体名，按下remap按钮即可重新分配快捷键对应的实体名

 导出结果：export

                    

                    

 

 

其他更详细的操作参照github中的描述

 

将标注好的ann文件转成常用的训练样本的格式，以下有python3实现

 

 

import re

# txt2ner_train_data turn label str into ner trainable data
# s :labeled str  eg.\'我来到[@1999年#YEAR*]的[@上海#LOC*]的[@东华大学#SCHOOL*]\'
# save_path: ner_trainable_txt name
def str2ner_train_data(s,save_path):
    ner_data = []
    result_1 = re.finditer(r\'\[\@\', s)
    result_2 = re.finditer(r\'\*\]\', s)
    begin = []
    end = []
    for each in result_1:
        begin.append(each.start())
    for each in result_2:
        end.append(each.end())
    assert len(begin) == len(end)
    i = 0
    j = 0
    while i < len(s):
        if i not in begin:
            ner_data.append([s[i], 0])
            i = i + 1
        else:
            ann = s[i + 2:end[j] - 2]
            entity, ner = ann.rsplit(\'#\')
            if (len(entity) == 1):
                ner_data.append([entity, \'S-\' + ner])
            else:
                if (len(entity) == 2):
                    ner_data.append([entity[0], \'B-\' + ner])
                    ner_data.append([entity[1], \'E-\' + ner])
                else:
                    ner_data.append([entity[0], \'B-\' + ner])
                    for n in range(1, len(entity) - 1):
                        ner_data.append([entity[n], \'I-\' + ner])
                    ner_data.append([entity[-1], \'E-\' + ner])

            i = end[j]
            j = j + 1

    f = open(save_path, \'w\', encoding=\'utf-8\')
    for each in ner_data:
        f.write(each[0] + \' \' + str(each[1]))
        f.write(\'\n\')
    f.close()
# txt2ner_train_data turn label str into ner trainable data
# file_path :labeled multi lines\' txt  eg.\'我来到[@1999年#YEAR*]的[@上海#LOC*]的[@东华大学#SCHOOL*]\'
# save_path: ner_trainable_txt name
def txt2ner_train_data(file_path,save_path):
    fr=open(file_path,\'r\',encoding=\'utf-8\')
    lines=fr.readlines()
    s=\'\'
    for line in lines:
        line=line.replace(\'\n\',\'\')
        line=line.replace(\' \',\'\')
        s=s+line
    fr.close()
    str2ner_train_data(s, save_path)

if(__name__==\'__main__\'):
    s = \'我来到[@1999年#YEAR*]的[@上海#LOC*]的[@东华大学#SCHOOL*]\'
    save_path = \'s.txt\'
    str2ner_train_data(s, save_path)
    file_path=\'D:\\codes\\python_codes\\SUTDAnnotator-master\\demotext\\ChineseDemo.txt.ann\'
    txt2ner_train_data(file_path,\'s1.txt\')

　　

 

 

 

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