nlptoolkit-ner v1.0.1

Named Entity Recognition

Task Definition

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.

ORG Türk Hava Yolları bu TIME Pazartesi'den itibaren LOC İstanbul hattı için indirimli satışlarını MONEY 90 TL'den başlatacağını açıkladı.

ORG Turkish Airlines announced that from this TIME Monday on it will start its discounted fares of MONEY 90TL for LOC İstanbul route.

See the Table below for typical generic named entity types.

Data Annotation

Preparation

1. Collect a set of sentences to annotate.
2. Each sentence in the collection must be named as xxxx.yyyyy in increasing order. For example, the first sentence to be annotated will be 0001.train, the second 0002.train, etc.
3. Put the sentences in the same folder such as Turkish-Phrase.
4. Build the Java project and put the generated sentence-ner.jar file into another folder such as Program.
5. Put Turkish-Phrase and Program folders into a parent folder.

Annotation

1. Open sentence-ner.jar file.
2. Wait until the data load message is displayed.
3. Click Open button in the Project menu.
4. Choose a file for annotation from the folder Turkish-Phrase.
5. For each word in the sentence, click the word, and choose approprite entity tag from PERSON, ORGANIZATION, LOCATION, TIME, or MONEY tags.
6. Click one of the next buttons to go to other files.

Classification DataSet Generation

After annotating sentences, you can use DataGenerator package to generate classification dataset for the Named Entity Recognition task.

Generation of ML Models

After generating the classification dataset as above, one can use the Classification package to generate machine learning models for the Named Entity Recognition task.

Video Lectures

For Developers

You can also see either Python, Cython, Java, C++, C#, or C# repository.

Requirements

• Node.js 14 or higher
• Git

Node.js

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

node -v

You can find the latest version of Node.js here.

Git

Install the latest version of Git.

Npm Install

npm install nlptoolkit-ner

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 util will be created. Or you can use below link for exploring the code:

git clone https://github.com/starlangsoftware/ner-js.git

Open project with Webstorm IDE

Steps for opening the cloned project:

• Start IDE
• Select File | Open from main menu
• Choose Ner-Js file
• Select open as project option
• Couple of seconds, dependencies will be downloaded.

Detailed Description

ParseTree

In order to find the named entities in a parse tree, one uses autoNER method of the TreeAutoNER class.

parseTree = ...
turkishNer = TurkishTreeAutoNER(ViewLayerType.Turkish)
turkishNer.autoNER(parseTree)

Sentence

In order to find the named entities in a simple sentence, one uses autoNER method of the SentenceAutoNER class.

sentence = ...
turkishNer = TurkishSentenceAutoNER()
turkishNer.autoNER(sentence)

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}}