word-context tagger

output using only api
output using bert (untrained) and api

Inspiration Understanding the precise context of words in sentences is crucial for applications like sentiment analysis, chatbots, and NLP-based categorization. Our initial attempts to achieve this using a single model/API showed limitations in accuracy, inspiring us to create a robust pipeline that leverages both BERT's contextual embeddings and a GPT-based API for better categorization.

What it does Our solution, Word-Context Tagger, classifies words in a given sentence into one of several predefined categories (e.g., Finance, Legal, Transportation). It analyzes both the semantic context of each word and its relationship with surrounding words, providing precise and context-aware tags that align with the categories we defined.

How we built it Preprocessing: Text cleaning and tokenization using NLTK to remove noise and irrelevant characters. BERT Integration: Used BERT to generate embeddings for words, capturing their contextual meaning. API Integration: Passed intermediate BERT classifications to a GPT-4-based API to refine categorization based on the sentence's overall context. Hybrid Categorization: Combined the strengths of BERT and GPT-4 to map words exclusively to our predefined categories, ensuring contextual accuracy. Challenges we ran into Inaccurate API-only Results: Sole reliance on the API led to inconsistent categorization. BERT Limitations: Pre-trained BERT models lacked the ability to classify into our custom categories without training, and it failed to handle multiple occurrences of the same word effectively. Time Constraints: Training BERT for our custom categories required a labeled dataset and time, which were unavailable so we created a dummy dataset with a few samples for testing purposes. Over time as we gain more data we can improve the accuracy of our model. Integration Issues: Combining BERT and GPT-4 seamlessly while maintaining accuracy required several iterations. Accomplishments that we're proud of Successfully integrated BERT embeddings with the API for hybrid classification. Demonstrated how to extend BERT's capabilities to custom categories through training, showcasing the potential of our approach. Developed a robust pipeline that improves categorization accuracy while aligning with predefined categories. What we learned Strengths and Weaknesses of BERT: Its ability to generate context-aware embeddings is unparalleled, but it requires task-specific fine-tuning for custom applications. API Refinement: APIs like GPT-4 can effectively complement models like BERT to enhance results. Importance of Hybrid Approaches: Combining models can often outperform standalone systems by addressing individual weaknesses. What's next for Word-Context Tagger Training BERT for Custom Categories: Acquire and label a dataset to train BERT specifically for our predefined categories. Improved API Integration: Experiment with weight adjustments between BERT and the API for even better results. Real-Time Applications: Deploy the solution as an API or tool for industries needing real-time context-aware word tagging, such as finance or customer support. Multi-Language Support: Extend the tool's functionality to support tagging in multiple languages.