NLP Projects at Connex One - Sentiment Analysis, Entity Recognition, and Call Summarization

Overview

Explore ongoing NLP projects at Connex One, from sentiment analysis to call summarization. Gain insights into successes and challenges in AI implementation for data-driven solutions.

Syllabus

- Introduction to NLP in Industry -- Overview of NLP applications -- Importance of NLP in the business context - Understanding Sentiment Analysis -- Fundamentals of sentiment analysis -- Tools and techniques for sentiment analysis -- Implementing sentiment analysis in real-world projects -- Case studies and success stories from Connex One - Exploring Entity Recognition -- Introduction to named entity recognition (NER) -- Algorithms and models used in NER -- Practical implementation of NER at Connex One -- Challenges and solutions in NER projects - Call Summarization Techniques -- Basics of call summarization -- Approaches to data collection and preprocessing -- Building models for automatic call summarization -- Evaluating call summarization effectiveness - AI Implementation: Successes and Challenges -- Overcoming technical challenges in NLP projects -- Data privacy and ethical considerations -- Measurement of success in NLP implementations - Hands-on Project Work -- Setting up the project environment -- Guided development of a sentiment analysis system -- Applying NER techniques to a Connex One case study -- Creating a prototype for call summarization - Conclusion and Future Directions in NLP -- Review of key learnings -- Future trends in NLP technology -- Opportunities for further exploration within Connex One

Taught by

Tags