What Are Some Innovative Ways You’ve Used NLP?
Data Science Spotlight
What Are Some Innovative Ways You’ve Used NLP?
In the rapidly evolving field of natural language processing, we've gathered insights from Founders and Lead Data Scientists on their cutting-edge applications. From enhancing chatbot interactivity to speeding up annotation with NLP models, discover the innovative uses of NLP that are making waves in the industry. Here are four compelling examples these experts have implemented, along with the impacts they've had on their work.
- Enhanced Chatbot Interactivity
- Sentiment Analysis on India-China Relations
- Innovative Categorical and Numerical Data NLP
- Speeding Up Annotation with NLP Models
Enhanced Chatbot Interactivity
One such very creative use of Natural Language Processing (NLP) that I have used in my work has been in chatbot development. This way, using NLP, the chatbot better understands and responds to user queries in a way that is more like a human and interactive.
For example, instead of recognizing some keywords, a chatbot can grasp the context and intent behind the user's question. It can, therefore, deal with more complex and varied inquiries, providing correct and helpful answers. The impact was huge because users found the chatbot to be very engaging and effective, resulting in increased satisfaction and more meaningful engagements. This has freed my team from time-wasting, as the chatbot is able to answer routine questions and perform routine tasks, freeing human time to deal with complex issues.
Sentiment Analysis on India-China Relations
Sentiment analysis on tweets to understand what people in India think about China. By analyzing the sentiment (positive, negative, neutral) expressed in these tweets, we could gauge public opinion and identify recurring themes or issues related to China.
Impact:
Understanding Public Opinion: Provided a clear picture of the general sentiment toward China in India, highlighting periods of heightened positivity or negativity.
Media and Communication Strategies: Assisted media outlets and communication teams in tailoring their content and messaging based on current public sentiment trends.
Innovative Categorical and Numerical Data NLP
There are two times when I used NLP in an innovative way. The first time, I had a dataset that had only categorical features, so I decided, instead of trying to decide what type of pre-processing I should do, to just concatenate them into a list of words and use NLP pre-processing approaches.
The second time, I had numerical features, over 28,000 of them, but no traditional data science approaches worked. I modified the problem so that instead of having numerical features, I replaced the numerical features with their names and concatenated the names, and then used NLP pre-processing.
Speeding Up Annotation with NLP Models
In our work, we've used the NLP models to facilitate and speed up the annotation process. A number of our clients also use the existing models to cut down the time for the humans-in-the-loop training. For simple text labeling tasks like NER, anonymization, sentiment analysis, and summarization, a mix of humans and pre-annotations generated by LLMs proves to be most time-efficient and cost-effective.
However, it's too early to rely completely on the annotations provided by NLP models. At least a small portion of the data must be randomly sampled and checked by the human QA/annotation specialist.
