Hybrid NER System for Multi-Source Offer Feeds
This work addresses the challenge of automating information extraction from diverse advertising data to improve customer targeting for sales, but it is incremental as it builds on existing NER methods.
The paper tackled the problem of extracting key entities from unstructured multi-source offer feeds by proposing a hybrid NER model, which achieved better performance compared to existing models in the offer domain.
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.