核心概念
In this research, the authors address the challenges of duplicate question retrieval and confirmation time prediction in software communities using innovative methods that outperform existing baselines.
要約
This study focuses on improving the efficiency of moderators in identifying duplicate questions and predicting confirmation times. By leveraging text and network-based features, the proposed methods show significant performance improvements over state-of-the-art techniques. The research highlights the importance of addressing these challenges in community question answering platforms to enhance user experience and reduce manual efforts.
The study introduces a Siamese neural network approach for duplicate question retrieval, achieving superior results compared to existing models like DupPredictor and DUPE. Additionally, for duplicate confirmation time prediction, both standard machine learning models and neural networks are utilized with text and graph-based features, demonstrating statistically significant improvements.
The dataset used consists of questions from the askubuntu platform, focusing on duplicates within the Ubuntu ecosystem. The research provides insights into handling duplicate questions efficiently by combining text embeddings with network features derived from tag co-occurrence networks.
Overall, this study contributes to enhancing the functionality of community question answering platforms by streamlining processes related to duplicate question identification and confirmation time prediction.
統計
Our method outperforms DupPredictor [33] and DUPE [1] by 5% and 7% respectively.
We obtain Spearman’s rank correlation of 0.20 and 0.213 (statistically significant) for text and graph based features respectively.