Introduction: In this project, the focus has been on Google QUEST Q&A Labeling, an innovative Natural Language Processing (NLP) project aimed at improving the quality and relevance of question-answer interactions in various online platforms. The key points covered include:
About Dataset: It provides details about the Google QUEST Q&A Labeling dataset, which consists of over 6,000 question-answer pairs from diverse online platforms, manually annotated with 30 labels across nine categories to assess content quality.
Exploratory Data Analysis (EDA): This section discusses the data analysis process, showing the distribution of data, category percentages, and the distribution of hosts in the training data. It also presents a heatmap illustrating the correlation between different tags. Abbreviation analysis is also performed to determine the usage of slangs.
Architecture and Model Training Results: It delves into the models used for this project. The baseline model employed BERT embeddings and achieved certain accuracies, while the final model utilized BiLSTM architecture and handled label imbalances. Results and architectural details are provided in the report.
Prediction: This section briefly mentions the prediction process, emphasizing the importance of calibrated probabilities for the model's output.
Conclusion: The project highlights the benefits of Q&A tags labeling, such as improved accuracy, quality assessment, topic modeling, and personalization of responses.
Future Use Cases: It discusses potential future use cases for the Google QUEST Q&A Labeling NLP model, including improved search engines, enhanced customer support, online education, content moderation, personalized recommendations, corporate knowledge bases, medical consultations, and sentiment analysis.
In summary, this project explores the development, application, and potential impact of the Google QUEST Q&A Labeling NLP model, highlighting its contributions to improving the quality of information retrieval and user experiences across various domains.