In the fast-paced world of online forums, identifying duplicate questions is a persistent challenge. Our study introduces a novel deep learning method to tackle this issue, focusing on Stack Overflow, a popular Q&A platform for programmers. By combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, our approach captures both local nuances and long-term relationships in textual data. This hybrid model leverages advanced word embeddings like Word2Vec and GloVe, significantly enhancing the detection of similar questions. Tested on the extensive Stack Overflow dataset, our model achieved impressive accuracy rates, outperforming traditional methods. This research not only improves user experience on Stack Overflow but also holds potential for other Q&A platforms. It represents a significant advancement in natural language processing and deep learning applications.
Keywords: DeepLearning, NLP , MachineLearning ,DuplicateDetection ,Technology Research, DataScience ,Programming.
Links:
Full Text: https://www.igminresearch.com/articles/html/igmin135
DOI Link: https://dx.doi.org/10.61927/igmin135
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