Duplicate Detection in Quora Question Pairs Using Machine Learning

The detection of question pair similarity on Quora presents a significant challenge in the realm of natural language processing and binary classification. This problem holds vital implications for user experience enhancement, redundancy reduction, and content quality improvement on the platform. In this paper, we explore the formulation of this problem, where each question pair is treated as a binary classification task, with the input comprising text strings and the output indicating whether the questions are duplicates. Log-loss serves as the primary performance metric, measuring the alignment between predicted probabilities and actual labels. The complexity of the task lies in assessing semantic similarity, which entails gauging the degree to which two questions convey similar information despite variations in wording, syntax, and structure. For instance, questions like “How can I lose weight fast?” and “What is the best way to slim down quickly?” exemplify semantic similarity. However, distinguishing between semantically similar and dissimilar questions, such as “How do I get a date?” and “How do I get a date fruit?” poses a unique challenge. Furthermore, natural language processing techniques play a crucial role in transforming textual data into numerical features for machine learning models. These techniques encompass various processes, including text preprocessing, feature extraction, engineering, and selection, each addressing the complexities, ambiguities, and noise inherent in natural language.