  Data available across the web is largely unstructured. Offers published by
multiple sources like banks, digital wallets, merchants, etc., are one of the
most accessed advertising data in today's world. This data gets accessed by
millions of people on a daily basis and is easily interpreted by humans, but
since it is largely unstructured and diverse, using an algorithmic way to
extract meaningful information out of these offers is hard. Identifying the
essential offer entities (for instance, its amount, the product on which the
offer is applicable, the merchant providing the offer, etc.) from these offers
plays a vital role in targeting the right customers to improve sales. This work
presents and evaluates various existing Named Entity Recognizer (NER) models
which can identify the required entities from offer feeds. We also propose a
novel Hybrid NER model constructed by two-level stacking of Conditional Random
Field, Bidirectional LSTM and Spacy models at the first level and an SVM
classifier at the second. The proposed hybrid model has been tested on offer
feeds collected from multiple sources and has shown better performance in the
offer domain when compared to the existing models.
