Name Entity Recognition

Named Entity Recognition (NER) is a process of identifying and recognizing entities through text.

The goal of the project is to create a model that is able to find an entity from the raw data and can determine the category to which the element belongs. There are four categories: names of people, organizations, places and more. Identified by the labels PER, ORG, LOC, and O respectively.

Table of contents

• Development Setup
• Requirements
  • Download the glove data
  • Run

Development Setup

Requirements

conda create --name ner python=3.7.11
conda activate ner
pip install -r requirements.txt

Download the glove data

$ cd dataset
$ python download_glove.py

Run

python main.py --char-embedding-dim CHAR-EMB-DIM --char-len -- CHAR-LEN --hidden-dim HIDDEN-DIM --embedding-dim EMB-DIM --epochs EPOCHS --batch-size BATCH-SIZE --lr LEARNING-RATE --dropout DROPOUT --bidirectional BIDIRECTIONAL --num-layers NUM-LAYERS --only-test ONLY-TEST

where

• CHAR-EMB-DIM is the dimension of the char embedding, default is 10
• CHAR-LEN is the maximum length of the char sequence, default is 8
• HIDDEN-DIM is the dimension of the hidden layer, default is 256
• EMB-DIM is the dimension of the word embedding, default is 300
• EPOCHS is the number of epochs, default is 50
• BATCH-SIZE is the batch size, default is 64
• LEARNING-RATE is the learning rate, default is 0.001
• DROPOUT is the dropout rate, default is 0.5
• BIDIRECTIONAL is the bidirectional flag, default is True
• NUM-LAYERS is the number of layers, default is 2
• ONLY-TEST is the only test flag, default is False

for example, for training:

python main.py

for testing

python main.py --only-test True