MITIE 模型

Question

我一直在探索使用预训练的 MITIE 模型进行命名实体提取。无论如何我可以看看他们实际的 ner 模型而不是使用预训练模型？该模型是否开源？

Answer 1

Setting things up:

For starters, you can download the English Language Model which contains Corpus of annotated text from a huge dump in a file called total_word_feature_extractor.dat.

After that, download/clone the MITIE-Master Project from their official Git.

If you are running Windows O.S then download CMake.

If you are running a x64 based Windows O.S, then install Visual Studio 2015 Community edition for the C++ compiler.

After downloading, the above, extract all of them into a folder.

从开始 > 所有应用程序 > Visual Studio 打开 VS 2015 开发人员命令提示符，然后导航到工具文件夹，您将在其中看到 5 个子文件夹。

下一步是构建 ner_conll、ner_stream、train_freebase_relation_detector 和 wordrep 包，方法是在 Visual Studio 开发人员命令提示符中使用以下 Cmake 命令。

像这样：

对于ner_conll:

cd "C:\Users\xyz\Documents\MITIE-master\tools\ner_conll"

i) mkdir build ii) cd build 三）cmake -G "Visual Studio 14 2015 Win64" .. iv) cmake --build . --config Release --target install

对于ner_stream:

cd "C:\Users\xyz\Documents\MITIE-master\tools\ner_stream"

i) mkdir build ii) cd build 三）cmake -G "Visual Studio 14 2015 Win64" .. iv) cmake --build . --config Release --target install

对于train_freebase_relation_detector:

cd "C:\Users\xyz\Documents\MITIE-master\tools\train_freebase_relation_detector"

i) mkdir build ii) cd build 三）cmake -G "Visual Studio 14 2015 Win64" .. iv) cmake --build . --config Release --target install

对于 wordrep:

cd "C:\Users\xyz\Documents\MITIE-master\tools\wordrep"

i) mkdir build ii) cd build 三）cmake -G "Visual Studio 14 2015 Win64" .. iv) cmake --build . --config Release --target install

构建它们后，您会收到 150-160 条警告，别担心。

现在，导航至 "C:\Users\xyz\Documents\MITIE-master\examples\cpp\train_ner"

使用 Visual Studio 手动注释文本的代码制作 JSON 文件 "data.json"，如下所示：

{
  "AnnotatedTextList": [
    {
      "text": "I want to travel from New Delhi to Bangalore tomorrow.",
      "entities": [
        {
          "type": "FromCity",
          "startPos": 5,
          "length": 2
        },
        {
          "type": "ToCity",
          "startPos": 8,
          "length": 1
        },
        {
          "type": "TimeOfTravel",
          "startPos": 9,
          "length": 1
        }
      ]
    }
  ]
}

您可以添加更多的话语并对其进行注释，训练数据越多，预测的准确性就越好。

这个带注释的 JSON 也可以通过 jQuery 或 Angular 等前端工具创建。但为了简洁起见，我手工创建了它们。

现在，解析带注释的 JSON 文件并将其传递给 ner_training_instance 的 add_entity 方法。

但是 C++ 不支持反射反序列化 JSON，这就是为什么你可以使用这个库 Rapid JSON Parser。从他们的 Git 页面下载包并将其放在 "C:\Users\xyz\Documents\MITIE-master\mitielib\include\mitie".

下

现在我们必须自定义 train_ner_example.cpp 文件，以便解析我们注释的自定义实体 JSON 并将其传递给 MITIE 进行训练。

#include "mitie\rapidjson\document.h"
#include "mitie\ner_trainer.h"

#include <iostream>
#include <vector>
#include <list>
#include <tuple>
#include <string>
#include <map>
#include <sstream>
#include <fstream>

using namespace mitie;
using namespace dlib;
using namespace std;
using namespace rapidjson;

string ReadJSONFile(string FilePath)
{
    ifstream file(FilePath);
    string test;
    cout << "path: " << FilePath;
    try
    {
        std::stringstream buffer;
        buffer << file.rdbuf();
        test = buffer.str();
        cout << test;
        return test;
    }
    catch (exception &e)
    {
        throw std::exception(e.what());
    }
}

//Helper function to tokenize a string based on multiple delimiters such as ,.;:- or whitspace
std::vector<string> SplitStringIntoMultipleParameters(string input, string delimiter)
{
    std::stringstream stringStream(input);
    std::string line;

    std::vector<string> TokenizedStringVector;

    while (std::getline(stringStream, line))
    {
        size_t prev = 0, pos;
        while ((pos = line.find_first_of(delimiter, prev)) != string::npos)
        {
            if (pos > prev)
                TokenizedStringVector.push_back(line.substr(prev, pos - prev));
            prev = pos + 1;
        }
        if (prev < line.length())
            TokenizedStringVector.push_back(line.substr(prev, string::npos));
    }
    return TokenizedStringVector;
}

//Parse the JSON and store into appropriate C++ containers to process it.
std::map<string, list<tuple<string, int, int>>> FindUtteranceTuple(string stringifiedJSONFromFile)
{
    Document document;
    cout << "stringifiedjson : " << stringifiedJSONFromFile;
    document.Parse(stringifiedJSONFromFile.c_str());

    const Value& a = document["AnnotatedTextList"];
    assert(a.IsArray());

    std::map<string, list<tuple<string, int, int>>> annotatedUtterancesMap;

    for (int outerIndex = 0; outerIndex < a.Size(); outerIndex++)
    {
        assert(a[outerIndex].IsObject());
        assert(a[outerIndex]["entities"].IsArray());
        const Value &entitiesArray = a[outerIndex]["entities"];

        list<tuple<string, int, int>> entitiesTuple;

        for (int innerIndex = 0; innerIndex < entitiesArray.Size(); innerIndex++)
        {
            entitiesTuple.push_back(make_tuple(entitiesArray[innerIndex]["type"].GetString(), entitiesArray[innerIndex]["startPos"].GetInt(), entitiesArray[innerIndex]["length"].GetInt()));
        }

        annotatedUtterancesMap.insert(pair<string, list<tuple<string, int, int>>>(a[outerIndex]["text"].GetString(), entitiesTuple));
    }

    return annotatedUtterancesMap;
}

int main(int argc, char **argv)
{

    try {

        if (argc != 3)
        {
            cout << "You must give the path to the MITIE English total_word_feature_extractor.dat file." << endl;
            cout << "So run this program with a command like: " << endl;
            cout << "./train_ner_example ../../../MITIE-models/english/total_word_feature_extractor.dat" << endl;
            return 1;
        }

        else
        {
            string filePath = argv[2];
            string stringifiedJSONFromFile = ReadJSONFile(filePath);

            map<string, list<tuple<string, int, int>>> annotatedUtterancesMap = FindUtteranceTuple(stringifiedJSONFromFile);


            std::vector<string> tokenizedUtterances;
            ner_trainer trainer(argv[1]);

            for each (auto item in annotatedUtterancesMap)
            {
                tokenizedUtterances = SplitStringIntoMultipleParameters(item.first, " ");
                mitie::ner_training_instance *currentInstance = new mitie::ner_training_instance(tokenizedUtterances);
                for each (auto entity in item.second)
                {
                    currentInstance -> add_entity(get<1>(entity), get<2>(entity), get<0>(entity).c_str());
                }
                // trainingInstancesList.push_back(currentInstance);
                trainer.add(*currentInstance);
                delete currentInstance;
            }


            trainer.set_num_threads(4);

            named_entity_extractor ner = trainer.train();

            serialize("new_ner_model.dat") << "mitie::named_entity_extractor" << ner;

            const std::vector<std::string> tagstr = ner.get_tag_name_strings();
            cout << "The tagger supports " << tagstr.size() << " tags:" << endl;
            for (unsigned int i = 0; i < tagstr.size(); ++i)
                cout << "\t" << tagstr[i] << endl;
            return 0;
        }
    }

    catch (exception &e)
    {
        cerr << "Failed because: " << e.what();
    }
}

add_entity接受3个参数，可以是向量的标记化字符串，自定义实体类型名称，单词在句子中的起始索引和单词范围。

现在我们必须在开发人员命令提示符 Visual Studio 中使用以下命令构建 ner_train_example.cpp。

1) cd "C:\Users\xyz\Documents\MITIE-master\examples\cpp\train_ner" 2) mkdir build 3) cd build 4) cmake -G "Visual Studio 14 2015 Win64" .. 5) cmake --build . --config Release --target install 6) cd Release

7) train_ner_example "C:\Users\xyz\Documents\MITIE-master\MITIE-models\english\total_word_feature_extractor.dat" "C:\Users\xyz\Documents\MITIE-master\examples\cpp\train_ner\data.json"

成功执行上述操作后，我们将获得一个 new_ner_model.dat 文件，它是我们话语的序列化和训练版本。

现在，该 .dat 文件可以传递给 RASA 或单独使用。

将其传递给 RASA：

制作config.json文件如下：

{
    "project": "demo",
    "path": "C:\Users\xyz\Desktop\RASA\models",
    "response_log": "C:\Users\xyz\Desktop\RASA\logs",
    "pipeline": ["nlp_mitie", "tokenizer_mitie", "ner_mitie", "ner_synonyms", "intent_entity_featurizer_regex", "intent_classifier_mitie"], 
    "data": "C:\Users\xyz\Desktop\RASA\data\examples\rasa.json",
    "mitie_file" : "C:\Users\xyz\Documents\MITIE-master\examples\cpp\train_ner\Release\new_ner_model.dat",
    "fixed_model_name": "demo",
    "cors_origins": ["*"],
    "aws_endpoint_url": null,
    "token": null,
    "num_threads": 2,
    "port": 5000
}

MITIE 模型

MITIE ner model

python

model

named-entity-recognition

rasa-nlu