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ner

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Named Entity Recognition

파일 탐색기

최종 버전 다운로드 (.zip)

- - __init__.py
  - extractor.py
  - match.py
  - span.py
- - __init__.py
  - token.py
  - tokenizer.py
- __init__.py
- corpus.py
- evaluation.py
- layers.py
- network.py
- utils.py
example.ipynb
LICENSE
ner.py
README.md
requirements.txt
setup.py
training_example.ipynb

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/deeppavlov/ner

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd ner

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

$ pip3 install -r requirements.txt

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

$ pip3 install git+https://github.com/deepmipt/ner

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

// repository documentation

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# This repository is outdated please move to https://github.com/deepmipt/DeepPavlov

# Neural Networks for Named Entity Recognition

In this repo you can find several neural network architectures for named entity recognition from the paper "_Application of a Hybrid Bi-LSTM-CRF model to the task of Russian Named Entity Recognition_" https://arxiv.org/pdf/1709.09686.pdf, which is inspired by LSTM+CRF architecture from https://arxiv.org/pdf/1603.01360.pdf.

NER class from ner/network.py provides methods for construction, training and inference neural networks for Named Entity Recognition.

We provide pre-trained CNN model for Russian Named Entity Recognition.
The model was trained on three datatasets:

- Gareev corpus [1] (obtainable by request to authors)
- FactRuEval 2016 [2]
- NE3 (extended Persons-1000) [3, 4]

The pre-trained model can recognize such entities as:

- Persons (PER)
- Locations (LOC)
- Organizations (ORG)

An example of usage of the pre-trained model is provided in [example.ipynb](https://github.com/deepmipt/ner/blob/master/example.ipynb).

Remark: at training stage the corpora were lemmatized and lowercased.
So text must be tokenized and lemmatized and lowercased before feeding it into the model.

The F1 measure for presented model along with other published solution provided in the table below:

| Models                | Gareev’s dataset | Persons-1000 | FactRuEval 2016 |
|---------------------- |:----------------:|:------------:|:---------------:|
| Gareev et al. [1]     | 75.05            |              |                 |
| Malykh et al. [5]     | 62.49            |              |                 |
| Trofimov  [6]         |                  | 95.57        |                 |
| Rubaylo et al. [7]    |                  |              | 78.13           |
| Sysoev et al. [8]     |                  |              | 74.67           |
| Ivanitsky et al.  [9] |                  |              | **87.88**       |
| Mozharova et al.  [10] |                  | 97.21        |                 |
| Our (Bi-LSTM+CRF)     | **87.17**        | **99.26**    | 82.10           ||

### Usage

#### Installing
The toolkit is implemented in Python 3 and requires a number of packages. To install all needed packages use:
```
$ pip3 install -r requirements.txt
```

or

```
$ pip3 install git+https://github.com/deepmipt/ner
```

Warning: there is no GPU version of TensorFlow specified in the requirements file

#### Command-Line Interface
The simplest way to use pre-trained Russian NER model is via command line interface:

$ echo "На конспирологическом саммите в США глава Федерального Бюро Расследований сделал невероятное заявление" | ./ner.py

На O
    конспирологическом O
    саммите O
    в O
    США B-LOC
    глава O
    Федерального B-ORG
    Бюро I-ORG
    Расследований I-ORG
    сделал O
    невероятное O
    заявление O

And for interactive usage simply type:

$ ./ner.py

#### Usage as module

```
>>> import ner
>>> extractor = ner.Extractor()
>>> for m in extractor("На конспирологическом саммите в США глава Федерального Бюро Расследований сделал невероятное заявление"):
...     print(m)
Match(tokens=[Token(span=(32, 35), text='США')], span=Span(start=32, end=35), type='LOC')
Match(tokens=[Token(span=(42, 54), text='Федерального'), Token(span=(55, 59), text='Бюро'), Token(span=(60, 73), text='Расследований')], span=Span(start=42, end=73), type='ORG')
```

### Training

To see how to train the network and what format of data is required see [training_example.ipynb](https://github.com/deepmipt/ner/blob/master/training_example.ipynb) jupyter notebook.

### Literature

[1] - Rinat Gareev, Maksim Tkachenko, Valery Solovyev, Andrey Simanovsky, Vladimir Ivanov: Introducing Baselines for Russian Named Entity Recognition. Computational Linguistics and Intelligent Text Processing, 329 -- 342 (2013).

[2] - https://github.com/dialogue-evaluation/factRuEval-2016

[3] - http://ai-center.botik.ru/Airec/index.php/ru/collections/28-persons-1000

[4] - http://labinform.ru/pub/named_entities/descr_ne.htm

[5] -  Reproducing Russian NER Baseline Quality without Additional Data. In proceedings of the 3rd International Workshop on ConceptDiscovery in Unstructured Data, Moscow, Russia, 54 – 59 (2016)

[6] - Rubaylo A. V., Kosenko M. Y.: Software utilities for natural language information
retrievial. Almanac of modern science and education, Volume 12 (114), 87 – 92.(2016)

[7] - Sysoev A. A., Andrianov I. A.: Named Entity Recognition in Russian: the Power of Wiki-Based Approach. dialog-21.ru

[8] - Ivanitskiy Roman, Alexander Shipilo, Liubov Kovriguina: Russian Named Entities Recognition and Classification Using Distributed Word and Phrase Representations. In SIMBig, 150 – 156. (2016).

[9] - Mozharova V., Loukachevitch N.: Two-stage approach in Russian named entity recognition. In Intelligence, Social Media and Web (ISMW FRUCT), 2016 International FRUCT Conference, 1 – 6 (2016)