Abstract: Quora question pair similarity is a common problem in natural language processing that can be tackled using machine learning techniques. The goal is to identify whether two questions on Quora are similar or not, based on their semantic meaning. One approach to this problem is to use a neural network-based model, such as a Siamese network, which can learn a feature representation of each question and then compute a similarity score between them. Another approach is to use traditional machine learning algorithms, such as logistic regression or support vector machines, to classify pairs of questions as either similar or not. To train these models, you will need a large dataset of question pairs labelled as either similar or not. You can obtain such a dataset from Quora itself, by extracting pairs of questions that have been marked as duplicates by Quora users. Once you have trained and validated your model, you can use it to predict the similarity between new pairs of questions on Quora, which can help improve the user experience and ensure that high-quality answers are provided to users.
Keywords:
• log-loss
• Binary Confusion Matrix
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DOI:
10.17148/ IJARCCE.2023.12356
