NER: Extracting entities from an article Description : I have dataset of categorised articles and to extract specific values from respective categorised article I have regex created for each category.

Aiming for:


Nlp techniques which learns the context of the content and avoids/minimizes the use of regex
If some new (similar) article comes up, depending on the learning (from 1) it tries to give the specific values.


Steps taken: 


Created a dataframe with various features like : 'Name of the author', 'published date', etc. and got the values from the dataset by using regex


Research_work:

I was considering these options ahead this stage :


Using CNN : it will classify new articles depending on the feature values it learnt on and then use regexs for entity extraction. (It wont achieve the first aim)
Using CRF (medium_article): making use of POS+IOB tagging


Is there any other way around ?
cons of above stated methods?
CRF is a standard for such case but bi-LSTM + CRF are said to be even better (e.g. https://arxiv.org/pdf/1508.01991.pdf). Not sure if you need POS as this is usually solved using the same techniques - may not help in the main task of entity extraction.

Depending how different are articles in specific categories and how much data you have for each of them, you may need to train each category separately and have some classifier in the beginning to decide category - like in your point 1. but word2vec-like classifier may be more robust here.