NlpToolkit-NamedEntityRecognition-Cy 1.0.9
NER Corpus Processing Library

Named Entity Recognition Task
============

In named entity recognition, one tries to find the strings within a text that correspond to proper names (excluding TIME and MONEY) and classify the type of entity denoted by these strings. The problem is difficult partly due to the ambiguity in sentence segmentation; one needs to extract which words belong to a named entity, and which not. Another difficulty occurs when some word may be used as a name of either a person, an organization or a location. For example, Deniz may be used as the name of a person, or - within a compound - it can refer to a location Marmara Denizi 'Marmara Sea', or an organization Deniz Taşımacılık 'Deniz Transportation'.

The standard approach for NER is a word-by-word classification, where the classifier is trained to label the words in the text with tags that indicate the presence of particular kinds of named entities. After giving the class labels (named entity tags) to our training data, the next step is to select a group of features to discriminate different named entities for each input word.

[<sub>ORG</sub> Türk Hava Yolları] bu [<sub>TIME</sub> Pazartesi'den] itibaren [<sub>LOC</sub> İstanbul] [<sub>LOC</sub> Ankara] hattı için indirimli satışlarını [<sub>MONEY</sub> 90 TL'den] başlatacağını açıkladı.

[<sub>ORG</sub> Turkish Airlines] announced that from this [<sub>TIME</sub> Monday] on it will start its discounted fares of [<sub>MONEY</sub> 90TL] for [<sub>LOC</sub> İstanbul] [<sub>LOC</sub> Ankara] route.

See the Table below for typical generic named entity types.

|Tag|Sample Categories|
|---|---|
|PERSON|people, characters|
|ORGANIZATION|companies, teams|
|LOCATION|regions, mountains, seas|
|TIME|time expressions|
|MONEY|monetarial expressions|

Video Lectures
============

[<img src="https://github.com/StarlangSoftware/TurkishNamedEntityRecognition/blob/master/video.jpg" width="50%">](https://youtu.be/tuuc5W5oNPw)

For Developers
============
You can also see [Python](https://github.com/starlangsoftware/TurkishNamedEntityRecognition-Py), [Java](https://github.com/starlangsoftware/TurkishNamedEntityRecognition), [C++](https://github.com/starlangsoftware/TurkishNamedEntityRecognition-CPP), [Swift](https://github.com/starlangsoftware/TurkishNamedEntityRecognition-Swift), [Js](https://github.com/starlangsoftware/TurkishNamedEntityRecognition-Js), or [C#](https://github.com/starlangsoftware/TurkishNamedEntityRecognition-CS) repository.

## Requirements

* [Python 3.7 or higher](#python)
* [Git](#git)

### Python 

To check if you have a compatible version of Python installed, use the following command:

    python -V
    
You can find the latest version of Python [here](https://www.python.org/downloads/).

### Git

Install the [latest version of Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git).

## Pip Install

	pip3 install NlpToolkit-NamedEntityRecognition-Cy
	
## Download Code

In order to work on code, create a fork from GitHub page. 
Use Git for cloning the code to your local or below line for Ubuntu:

	git clone <your-fork-git-link>

A directory called DataStructure will be created. Or you can use below link for exploring the code:

	git clone github.com/starlangsoftware/TurkishNamedEntityRecognition-Cy.git

## Open project with Pycharm IDE

Steps for opening the cloned project:

* Start IDE
* Select **File | Open** from main menu
* Choose `TurkishNamedEntityRecognition-Cy` file
* Select open as project option

Detailed Description
============

+ [Gazetteer](#gazetteer)

## Gazetteer

Bir Gazetter yüklemek için

	Gazetteer(self, name: str, fileName: str)

Hazır Gazetteerleri kullanmak için

	AutoNER()

Bir Gazetteer'de bir kelime var mı diye kontrol etmek için

	contains(self, word: str) -> bool

# Cite

	@INPROCEEDINGS{8093439,
  	author={B. {Ertopçu} and A. B. {Kanburoğlu} and O. {Topsakal} and O. {Açıkgöz} and A. T. {Gürkan} and B. {Özenç} and İ. {Çam} and B. {Avar} and G. {Ercan} 	and O. T. {Yıldız}},
  	booktitle={2017 International Conference on Computer Science and Engineering (UBMK)}, 
  	title={A new approach for named entity recognition}, 
  	year={2017},
  	volume={},
  	number={},
  	pages={474-479},
  	doi={10.1109/UBMK.2017.8093439}}