Rasa: CRFEntityExtractor predicted_entities probelm

Created on 8 Jul 2020  ·  5Comments  ·  Source: RasaHQ/rasa

Rasa version 1.10.3:

Python version 3.7.7:

windows 10 (windows, osx, ...):

Issue:
CRFEntityExtractor can't extract chinese nlp entities.

Content of test result file CRFEntityExtractor_errors.json:
'''json
[
{
"text": "我想查的号码是12260222425",
"entities": [
{
"start": 7,
"end": 18,
"value": "12260222425",
"entity": "number"
}
],
"predicted_entities": [
{
"entity": "number",
"start": 0,
"end": 18,
"confidence_entity": 0.9842847574519757,
"value": "我想查的号码是12260222425",
"extractor": "CRFEntityExtractor"
}
]
},
{
"text": "我想查身份证441222296712305681",
"entities": [
{
"start": 6,
"end": 24,
"value": "441222296712305681",
"entity": "number"
}
],
"predicted_entities": [
{
"entity": "number",
"start": 0,
"end": 24,
"confidence_entity": 0.9912246457406692,
"value": "我想查身份证441222296712305681",
"extractor": "CRFEntityExtractor"
}
]
},
{
"text": "查电话11160222425",
"entities": [
{
"start": 1,
"end": 3,
"value": "电话",
"entity": "type"
},
{
"start": 3,
"end": 14,
"value": "11160222425",
"entity": "number"
}
],
"predicted_entities": [
{
"entity": "number",
"start": 0,
"end": 14,
"confidence_entity": 0.9876110342010952,
"value": "查电话11160222425",
"extractor": "CRFEntityExtractor"
}
]
},
{
"text": "请告诉我电话号码为19860222425",
"entities": [
{
"start": 4,
"end": 8,
"value": "电话号码",
"entity": "type"
},
{
"start": 9,
"end": 20,
"value": "19860222425",
"entity": "number"
}
],
"predicted_entities": [
{
"entity": "number",
"start": 0,
"end": 20,
"confidence_entity": 0.9923245728477686,
"value": "请告诉我电话号码为19860222425",
"extractor": "CRFEntityExtractor"
}
]
},
{
"text": "查电话号码19800222425",
"entities": [
{
"start": 1,
"end": 5,
"value": "电话号码",
"entity": "type"
},
{
"start": 5,
"end": 16,
"value": "19800222425",
"entity": "number"
}
],
"predicted_entities": [
{
"entity": "number",
"start": 0,
"end": 16,
"confidence_entity": 0.9947824185301072,
"value": "查电话号码19800222425",
"extractor": "CRFEntityExtractor"
}
]
},
{
"text": "查询电话19862618425",
"entities": [
{
"start": 2,
"end": 4,
"value": "电话",
"entity": "type"
},
{
"start": 4,
"end": 15,
"value": "19862618425",
"entity": "number"
}
],
"predicted_entities": [
{
"entity": "number",
"start": 0,
"end": 15,
"confidence_entity": 0.9904694037192807,
"value": "查询电话19862618425",
"extractor": "CRFEntityExtractor"
}
]
},
{
"text": "查询身份证",
"entities": [
{
"start": 2,
"end": 5,
"value": "身份证",
"entity": "type"
}
],
"predicted_entities": [
{
"entity": "type",
"start": 0,
"end": 5,
"confidence_entity": 0.8171515788864899,
"value": "查询身份证",
"extractor": "CRFEntityExtractor"
}
]
},
{
"text": "我要查下身份证",
"entities": [
{
"start": 4,
"end": 7,
"value": "身份证",
"entity": "type"
}
],
"predicted_entities": [
{
"entity": "type",
"start": 0,
"end": 7,
"confidence_entity": 0.8545899093913647,
"value": "我要查下身份证",
"extractor": "CRFEntityExtractor"
}
]
},
{
"text": "查询的号码是11160222425",
"entities": [
{
"start": 6,
"end": 17,
"value": "11160222425",
"entity": "number"
}
],
"predicted_entities": [
{
"entity": "number",
"start": 0,
"end": 17,
"confidence_entity": 0.9835158735904833,
"value": "查询的号码是11160222425",
"extractor": "CRFEntityExtractor"
}
]
},
{
"text": "12260222425这手机号",
"entities": [
{
"start": 0,
"end": 11,
"value": "12260222425",
"entity": "number"
},
{
"start": 12,
"end": 15,
"value": "手机号",
"entity": "type"
}
],
"predicted_entities": [
{
"entity": "number",
"start": 0,
"end": 15,
"confidence_entity": 0.955705773586323,
"value": "12260222425这手机号",
"extractor": "CRFEntityExtractor"
}
]
},
{
"text": "我想查身份证",
"entities": [
{
"start": 3,
"end": 6,
"value": "身份证",
"entity": "type"
}
],
"predicted_entities": [
{
"entity": "type",
"start": 0,
"end": 6,
"confidence_entity": 0.7205535113669648,
"value": "我想查身份证",
"extractor": "CRFEntityExtractor"
}
]
},
{
"text": "帮我查询身份证",
"entities": [
{
"start": 4,
"end": 7,
"value": "身份证",
"entity": "type"
}
],
"predicted_entities": [
{
"entity": "type",
"start": 0,
"end": 7,
"confidence_entity": 0.8164589270039054,
"value": "帮我查询身份证",
"extractor": "CRFEntityExtractor"
}
]
},
{
"text": "最近的违章记录",
"entities": [
{
"start": 3,
"end": 7,
"value": "违章记录",
"entity": "business"
}
],
"predicted_entities": [
{
"entity": "business",
"start": 0,
"end": 7,
"confidence_entity": 0.984410185837026,
"value": "最近的违章记录",
"extractor": "CRFEntityExtractor"
}
]
},
{
"text": "住宿信息怎么样",
"entities": [
{
"start": 0,
"end": 4,
"value": "住宿信息",
"entity": "business"
}
],
"predicted_entities": [
{
"entity": "business",
"start": 0,
"end": 7,
"confidence_entity": 0.8651053068267097,
"value": "住宿信息怎么样",
"extractor": "CRFEntityExtractor"
}
]
},
{
"text": "查违法信息",
"entities": [
{
"start": 1,
"end": 5,
"value": "违法信息",
"entity": "business"
}
],
"predicted_entities": [
{
"entity": "business",
"start": 0,
"end": 5,
"confidence_entity": 0.9656421865682633,
"value": "查违法信息",
"extractor": "CRFEntityExtractor"
}
]
},
{
"text": "我要查询这个人的犯罪记录",
"entities": [
{
"start": 8,
"end": 12,
"value": "犯罪记录",
"entity": "business"
}
],
"predicted_entities": [
{
"entity": "business",
"start": 0,
"end": 12,
"confidence_entity": 0.9757523659603139,
"value": "我要查询这个人的犯罪记录",
"extractor": "CRFEntityExtractor"
}
]
},
{
"text": "这个人有没有违法",
"entities": [
{
"start": 6,
"end": 8,
"value": "违法",
"entity": "business"
}
],
"predicted_entities": [
{
"entity": "business",
"start": 0,
"end": 8,
"confidence_entity": 0.768765304063969,
"value": "这个人有没有违法",
"extractor": "CRFEntityExtractor"
}
]
},
{
"text": "查下教育信息",
"entities": [
{
"start": 2,
"end": 6,
"value": "教育信息",
"entity": "business"
}
],
"predicted_entities": [
{
"entity": "business",
"start": 0,
"end": 6,
"confidence_entity": 0.9995679280138035,
"value": "查下教育信息",
"extractor": "CRFEntityExtractor"
}
]
},
{
"text": "查下今天上海的天气",
"entities": [
{
"start": 2,
"end": 4,
"value": "今天",
"entity": "date_time"
},
{
"start": 4,
"end": 6,
"value": "上海",
"entity": "address"
}
],
"predicted_entities": [
{
"entity": "address",
"start": 0,
"end": 9,
"confidence_entity": 0.979807108097493,
"value": "查下今天上海的天气",
"extractor": "CRFEntityExtractor"
}
]
},
{
"text": "深圳昨天下冰雹了，明天还会下吗？",
"entities": [
{
"start": 0,
"end": 2,
"value": "深圳",
"entity": "address"
},
{
"start": 9,
"end": 11,
"value": "明天",
"entity": "date_time"
}
],
"predicted_entities": [
{
"entity": "date_time",
"start": 0,
"end": 16,
"confidence_entity": 0.8427497095495378,
"value": "深圳昨天下冰雹了，明天还会下吗？",
"extractor": "CRFEntityExtractor"
}
]
},
{
"text": "查下广州的天气怎么样",
"entities": [
{
"start": 2,
"end": 4,
"value": "广州",
"entity": "address"
}
],
"predicted_entities": [
{
"entity": "address",
"start": 0,
"end": 10,
"confidence_entity": 0.8881646255574454,
"value": "查下广州的天气怎么样",
"extractor": "CRFEntityExtractor"
}
]
},
{
"text": "我现在在上海",
"entities": [
{
"start": 4,
"end": 6,
"value": "上海",
"entity": "address"
}
],
"predicted_entities": [
{
"entity": "address",
"start": 0,
"end": 6,
"confidence_entity": 0.9762155698306253,
"value": "我现在在上海",
"extractor": "CRFEntityExtractor"
}
]
},
{
"text": "常德的天气怎么样？",
"entities": [
{
"start": 0,
"end": 2,
"value": "常德",
"entity": "address"
}
],
"predicted_entities": [
{
"entity": "address",
"start": 0,
"end": 9,
"confidence_entity": 0.765170136595972,
"value": "常德的天气怎么样？",
"extractor": "CRFEntityExtractor"
}
]
},
{
"text": "今天的",
"entities": [
{
"start": 0,
"end": 2,
"value": "今天",
"entity": "date_time"
}
],
"predicted_entities": [
{
"entity": "date_time",
"start": 0,
"end": 3,
"confidence_entity": 0.7083242453974274,
"value": "今天的",
"extractor": "CRFEntityExtractor"
}
]
},
{
"text": "查询深圳的天气",
"entities": [
{
"start": 2,
"end": 4,
"value": "深圳",
"entity": "address"
}
],
"predicted_entities": [
{
"entity": "address",
"start": 0,
"end": 7,
"confidence_entity": 0.9755224976844531,
"value": "查询深圳的天气",
"extractor": "CRFEntityExtractor"
}
]
},
{
"text": "帮我查下今天的",
"entities": [
{
"start": 4,
"end": 6,
"value": "今天",
"entity": "date_time"
}
],
"predicted_entities": [
{
"entity": "date_time",
"start": 0,
"end": 7,
"confidence_entity": 0.9040225226085086,
"value": "帮我查下今天的",
"extractor": "CRFEntityExtractor"
}
]
}
]
'''

Content of nlu model files (*.md) :
weather_nlu.md:
'''md

intent:request_weather

intent:inform

intent:greet

  • 你好
  • hello
  • 你好呀
    '''

number_nlu.md:
'''md

intent:request_number

intent:inform_business

synonym:身份证号码

  • 身份证
  • 身份证号码

synonym:电话号码

  • 电话
  • 电话号
  • 电话号码
  • 手机
  • 手机号
  • 手机号码

synonym:违章记录

  • 违章
  • 违章信息
  • 违章记录
  • 违章处罚
  • 违章情况

synonym:教育经历

  • 教育
  • 教育经历
  • 教育信息
  • 教育情况

synonym:出行轨迹

  • 出行
  • 出行路线
  • 出行信息
  • 出行轨迹
  • 出行记录

synonym:开房信息

  • 开房记录
  • 开房
  • 住宿记录
  • 住宿信息

  • 开房信息

  • synonym:犯罪记录

  • 犯罪
  • 犯罪信息
  • 犯罪记录
  • 违法记录
  • 违法
  • 违法犯罪

regex:phone_number

  • ((\d{3,4}-)?\d{7,8})|(((+86)|(86))?(1)\d{10})

regex:number

  • ([1-9]\d{5}(18|19|([23]\d))\d{2}((0[1-9])|(10|11|12))(([0-2][1-9])|10|20|30|31)\d{3}[0-9Xx])|([1-9]\d{5}\d{2}((0[1-9])|(10|11|12))(([0-2][1-9])|10|20|30|31)\d{2}[0-9Xx])
    '''

chitchat_nlu.md
'''md

intent:greet

  • 你好
  • 你好啊
  • 早上好
  • 晚上好
  • hello
  • hi
  • 嗨
  • 嗨喽
  • 见到你很高兴
  • 嘿
  • 早
  • 上午好
  • hello哈喽
  • 哈喽哈喽
  • hello hello
  • 喂喂

intent:goodbye

  • goodbye
  • bye
  • bye bye
  • 88
  • 886
  • 再见
  • 拜
  • 拜拜
  • 拜拜，下次再聊
  • 下次见
  • 回头见
  • 下次再见
  • 下次再聊
  • 有空再聊
  • 先这样吧
  • 好了，就说这么多了
  • 好了，先这样
  • 没事

intent:whoareyou

  • 你是谁
  • 我知道你吗
  • 谁
  • 我认识你吗
  • 这是谁啊
  • 是谁
  • 请问你是谁
  • 请问我认识你吗
  • 你是哪位
  • 你是？
  • 是谁？
  • 可以告诉我你的名字吗
  • 你叫什么名字

intent:whattodo

  • 你支持什么功能
  • 你有什么功能
  • 你能干什么
  • 你能做什么

intent:thanks

  • 谢谢
  • thanks
  • thank you
  • 真的太感谢你了，帮了我大忙
  • 谢谢你帮了我大忙
  • 你帮了我大忙，谢谢你小智
  • 非常感谢
  • 谢了

intent:deny

  • 不了
  • 算了
  • 不用了
  • 不需要
  • 就这样吧
  • 不
  • no
  • 不可以
  • 不是的
  • 不认同
  • 否定
  • 不是这样子的
  • 我不同意你的观点
  • 不同意
  • 不好
  • 你长得很美，就不要想得太美。
  • 拒绝
  • 不行

intent:affirm

  • 知道了
  • 是的
  • 当然
  • 好的
  • ok
  • 嗯
  • 可以
  • 你可以这么做
  • 你做得可以啊
  • 同意
  • 听起来不错
  • 是这样的
  • 的确是这样子的
  • 我同意你的观点
  • 对的
  • 好滴
  • 行
  • 还行
  • 当然可以

intent:chitchat

  • 你是不是傻
  • 你喜欢靓仔靓女吗
  • 能跟我说说你的男朋友吗
  • 嘿，朋友，你好啊?
  • 你是堆的
  • 你今天过得开心吗
  • 你没事吧
  • 今天感觉怎么样
  • 广州今天下雨了
  • 我能够知道谁邀请你吗
  • 请告诉我谁创造了你
  • 你的老板是谁
  • 我想知道谁发明了你
  • 你是哪家公司的
  • 你是谁发明的
  • 你是什么
  • 你还好吗
  • 你现在在干什么
  • 最近怎么样
  • 你最近过得怎么样
  • 谁是林俊杰
  • 卢十瓦是不是疯了
  • 你这个早上干什么了
  • 外面真安静
  • 现在的生活变得越来越好了
  • 是不是下雨了
  • 今天的天气很好，不是吗
  • 外面好冷啊
  • 你认识张天爱吗
  • 现在的世界真乱，你说是不是
  • 你今天看不起不错哟
  • 你觉得华为手机怎么样
  • 你知不知道新型冠形病毒
  • 你现在在干什么

intent:stop

  • 你是不是傻
  • 你就像个傻逼一样
  • 真差劲，什么都不懂
  • 真蠢
  • 什么都查不到
  • 你什么都不会
  • 感觉你什么都不知道
  • 你太蠢了
  • 我不查了，太傻了
  • 就这样吧
  • 感觉你什么都不会
  • 没有了
  • 什么都没查到
  • 我猜你是不是坏了
    '''

Content of configuration file (config.yml) :

language: "zh"

pipeline:
- name: "MitieNLP"
  model: "data/total_word_feature_extractor_zh.dat"
- name: "JiebaTokenizer"
  dictionary_path: "data/dict"
- name: "RegexFeaturizer"
- name: "MitieFeaturizer"
- name: "CRFEntityExtractor"
- name: "EntitySynonymMapper"
- name: "SklearnIntentClassifier"

policies:
  - name: KerasPolicy
    epochs: 500
    max_history: 5
  - name: FallbackPolicy
    fallback_action_name: 'action_default_fallback'
  - name: MemoizationPolicy
    max_history: 5
  - name: FormPolicy

Content of domain file (domain.yml) :

session_config:
  session_expiration_time: 60
  carry_over_slots_to_new_session: true
intents:
- affirm
- deny
- greet
- goodbye
- thanks
- whoareyou
- whattodo
- request_weather
- request_number
- inform
- inform_business
- stop
- chitchat
entities:
- date_time
- address
- type
- number
- business
- data_time
slots:
  address:
    type: unfeaturized
    auto_fill: false
  business:
    type: unfeaturized
    auto_fill: false
  date_time:
    type: unfeaturized
    auto_fill: false
  number:
    type: unfeaturized
    auto_fill: false
  requested_slot:
    type: unfeaturized
  type:
    type: unfeaturized
    auto_fill: false
responses:
  utter_answer_affirm:
  - text: 嗯嗯，好的！
  - text: 嗯嗯，很开心能够帮您解决问题~
  - text: 嗯嗯，还需要什么我能够帮助您的呢？
  utter_answer_greet:
  - text: 您好！请问我可以帮到您吗？
  - text: 您好！很高兴为您服务。请说出您要查询的功能？
  utter_answer_goodbye:
  - text: 再见
  - text: 拜拜
  - text: 虽然我有万般舍不得，但是天下没有不散的宴席~祝您安好！
  - text: 期待下次再见！
  - text: 嗯嗯，下次需要时随时记得我哟~
  - text: see you!
  utter_answer_deny:
  - text: 主人，您不开心吗？不要离开我哦
  - text: 怎么了，主人？
  utter_answer_thanks:
  - text: 嗯呢。不用客气~
  - text: 这是我应该做的，主人~
  - text: 嗯嗯，合作愉快！
  utter_answer_whoareyou:
  - text: 您好！我是小蒋呀，您的AI智能助理
  utter_answer_whattodo:
  - text: 您好！很高兴为您服务，我目前只支持查询天气哦。
  utter_ask_date_time:
  - text: 请问您要查询哪一天的天气？
  utter_ask_address:
  - text: 请问您要查下哪里的天气？
  utter_ask_number:
  - text: 请问您要查的{type}号码是多少？
  utter_ask_business:
  - text: 请问您要查询什么业务呢？
  utter_default:
  - text: 没听懂，请换种说法吧~
  utter_ask_continue:
  - text: 请问您还要继续吗?
  utter_noworries:
  - text: 不用客气 :)
  - text: 没事啦
  - text: 不客气哈，都是老朋友了 :)
  utter_wrong_business:
  - text: 当前还不支持{business}业务，请重新输入。
  utter_wrong_type:
  - text: 当前还不支持查询{type}。
  utter_wrong_number:
  - text: 您输入的{number}有误，请重新输入。
  utter_chitchat:
  - text: 呃呃呃呃呃
  - text: 您这是在尬聊吗？
actions:
- utter_answer_affirm
- utter_answer_deny
- utter_answer_greet
- utter_answer_goodbye
- utter_answer_thanks
- utter_answer_whoareyou
- utter_answer_whattodo
- utter_ask_date_time
- utter_ask_address
- utter_ask_number
- utter_ask_business
- utter_ask_type
- action_default_fallback
- utter_default
- utter_ask_continue
- utter_noworries
- utter_wrong_business
- utter_wrong_type
- utter_wrong_number
- utter_chitchat
forms:
- weather_form
- number_form
area type
Source
picture jingj5

All 5 comments

What's the return result from the model that you training from this data and pipeline and what's your expected result.

howl-anderson picture howl-anderson on 8 Jul 2020

What's the return result from the model that you training from this data and pipeline and what's your expected result.

大佬，我记得你的名字啊。 模型训练的测试结果上面列出来了。
比如我的输入是： 今天天气怎么样？ ， 然后这个CRF输出的实体是： 今天天气怎么样。
我希望的输出是： 今天， 这种正确的输出MitieEntityExtractor可以达到，但它训练起来太慢了。
请大佬指点，感谢！

jingj5 picture jingj5 on 8 Jul 2020

如果是 MITIE 效果良好，但是 CRF 效果较差，差异最大的应该是特征提取：MITIE 因为是预先训练的所有会有很好的特征，CRF 本身提供的特征不够多（也可能是配置的不够好）导致效果较差。你可以联系我微信 here-we-meet，沟通会比较高效。

We will communicate via a local IM, if this issue can have a good solution, I will feedback here.

howl-anderson picture howl-anderson on 8 Jul 2020
❤1

Thanks for the issue, @wochinge will get back to you about it soon!

You may find help in the docs and the forum, too 🤗
sara-tagger picture sara-tagger on 8 Jul 2020

Thanks @howl-anderson very much for help。
This issue has sloved in rasa=1.10.5.
The code with crf_entity_extractor.py 198 line number. The function call clean_up_entities has been removed.

jingj5 picture jingj5 on 8 Jul 2020
👍1
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