Description : I have dataset of categorised articles and to extract specific values from respective categorised article I have regex created for each category.
- 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.
- Created a dataframe with various features like : 'Name of the author', 'published date', etc. and got the values from the dataset by using regex
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?