GabrielLin / Rasa_NLU_Chi

Turn Chinese natural language into structured data 中文自然语言理解

Rasa NLU for Chinese, a fork from RasaHQ/rasa_nlu.

中文Blog

Files you should have:

• data/total_word_feature_extractor_zh.dat

Trained from Chinese corpus by MITIE wordrep tools (takes 2-3 days for training)

For training, please build the MITIE Wordrep Tool. Note that Chinese corpus should be tokenized first before feeding into the tool for training. Close-domain corpus that best matches user case works best.

A trained model from Chinese Wikipedia Dump and Baidu Baike can be downloaded from 中文Blog.

• data/examples/rasa/demo-rasa_zh.json

Should add as much examples as possible.

Usage:

1. Clone this project, and run

python setup.py install

2. Modify configuration.

Currently for Chinese we have two pipelines:

Use MITIE+Jieba (sample_configs/config_jieba_mitie.json):

["nlp_mitie", "tokenizer_jieba", "ner_mitie", "ner_synonyms", "intent_classifier_mitie"]

RECOMMENDED: Use MITIE+Jieba+sklearn (sample_configs/config_jieba_mitie_sklearn.json):

["nlp_mitie", "tokenizer_jieba", "ner_mitie", "ner_synonyms", "intent_featurizer_mitie", "intent_classifier_sklearn"]

3. Train model by running:

To use Jieba User Defined Dictionary, please put your dictionary files under rasa_nlu_chi/jieba_userdict/ before you do training and testing.

python -m rasa_nlu.train -c sample_configs/config_jieba_mitie_sklearn.json

If you specify your project name in configure file, this will save your model at /models/your_project_name.

Otherwise, your model will be saved at /models/default

4. Run the rasa_nlu server:

python -m rasa_nlu.server -c sample_configs/config_jieba_mitie_sklearn.json

5. Open a new terminal and now you can curl results from the server, for example:

$ curl -XPOST localhost:5000/parse -d '{"q":"我发烧了该吃什么药？", "project": "rasa_nlu_test", "model": "model_20170921-170911"}' | python -mjson.tool
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100   652    0   552  100   100    157     28  0:00:03  0:00:03 --:--:--   157
{
    "entities": [
        {
            "end": 3,
            "entity": "disease",
            "extractor": "ner_mitie",
            "start": 1,
            "value": "发烧"
        }
    ],
    "intent": {
        "confidence": 0.5397186422631861,
        "name": "medical"
    },
    "intent_ranking": [
        {
            "confidence": 0.5397186422631861,
            "name": "medical"
        },
        {
            "confidence": 0.16206323981749196,
            "name": "restaurant_search"
        },
        {
            "confidence": 0.1212448457737397,
            "name": "affirm"
        },
        {
            "confidence": 0.10333600028547868,
            "name": "goodbye"
        },
        {
            "confidence": 0.07363727186010374,
            "name": "greet"
        }
    ],
    "text": "我发烧了该吃什么药？"
}