Description:

Named Entity Recognition is the task of extracting named entities like Person, Place etc from the text. It is an important step in extracting information from unstructured text data. I will explore various approaches for entity extraction using both existing libraries and also implementing state of the art approaches from scratch

Agenda for the Talk:

Introducing Named Entity Recognition

Standard Named Entity using NLTK and Spacy

Training Custom Entity Tagger using Spacy or Rasa

Standard Algorithms for NER

• Conditional Random Field (CRF)
• Deep Learning for NER using LSTM in Keras
• Structured Deep Learning for NER using LSTM-CRF
• End-to-End NER via Bi-directional LSTM-CNN-CRF

Prerequisites:

A Basic Knowledge of Python, Machine Learning and Natural Language Processing.

Content URLs:

The content of the talk will be shared after the session in form of Github Repository.

Speaker Info:

Subramanya T A is Senior Data Scientist at Sentienz. He heads the Data Science team at Sentienz.

Speaker Links:

LinkedIn Profile: https://www.linkedin.com/in/subramanya-t-a-7306a729/

Sentienz Website: http://www.sentienz.com/