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Wireless Rechargeable Battery Powered WiFi Camera.

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Open Source Entity Recognition for Indian Languages (NER)

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Haptik

One of the key components of most successful NLP applications is the Named Entity Recognition (NER) module which accurately identifies the entities in text such as date, time, location, quantities, names and product specifications. There are already existing sophisticated systems for NER such as spaCy, Stanford NER, etc. but most of them are built with general purpose for a wide range of NLP applications such as Information Retrieval, Document classification and other applications of unstructured data analysis. At Haptik, we focus on continuously improving NLP capabilities of our conversational AI platform, which powers more than few million exchanges on a daily basis. These conversations are spread across hundreds of enterprise bots built for different use-cases such as customer support, e-commerce, etc. Hence, building an accurate and reliable NER system tailored for conversational AI has always been one of the key focus areas of the engineering team at Haptik.

Around 3 years ago we open-sourced one of our key frameworks, Chatbot NER, which is custom built to support entity recognition in text messages. You can read more about it here. After doing thorough research on existing Named Entity Recognition (NER) systems, we felt the strong need for building a framework which can support entity recognition for Indian languages. This led us to upgrade our own NER module i.e Chatbot NER to V2 version to scale its functionalities in local languages. The primary focus of this blog is to help you get started with using basic capabilities of Chatbot NER for English and 5 other Indian languages and their code mixed form.

In version 1, we had provided support for following entity types:

  1. Numeral: Entities that deal with the numeral or numbers such as temperature, budgets, size, quantities etc.
  2. Pattern: Entities which use patterns or regular expressions such as email, phone_number, PNR.
  3. Temporal: Entities for detecting time and date.
  4. Textual: Entities detection by looking at the dictionary or sentence structure. This detection mainly contains detection of entities like cuisine, dish, restaurants, city, location, etc.



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