Revolutionizing Duplicate Question Detection: A Deep Learning Approach for Stack Overflow

Muhammad Faseeh; Harun Jamil

doi:10.61927/igmin135

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Properties of Indium Antimonide Nanocrystals as Nanoelectronic Elements

Nikolai Dmitrievich Zhukov

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The policy development and current situation of information technology education in Taiwan

Min-Ying Tsai

Engineering Group Mini Review 記事ID: igmin135

Revolutionizing Duplicate Question Detection: A Deep Learning Approach for Stack Overflow

Machine Learning

Muhammad Faseeh and

Harun Jamil ^*

Affiliation

Department of Electronic Engineering, Jeju National University, Jeju-si, Jeju-do, Republic of Korea

DOI10.61927/igmin135 全文HTML 全文PDF この記事を引用する

要約

This study provides a novel way to detect duplicate questions in the Stack Overflow community, posing a daunting problem in natural language processing. Our proposed method leverages the power of deep learning by seamlessly merging Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to capture both local nuances and long-term relationships inherent in textual input. Word embeddings, notably Google’s Word2Vec and GloVe, raise the bar for text representation to new heights. Extensive studies on the Stack Overflow dataset demonstrate the usefulness of our approach, generating excellent results. The combination of CNN and LSTM models improves performance while streamlining preprocessing, establishing our technology as a viable piece in the arsenal for duplicate question detection. Aside from Stack Overflow, our technique has promise for various question-and-answer platforms, providing a robust solution for finding similar questions and paving the path for advances in natural language processing.

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参考文献

Ye X, Manoharan S. Marking essays automatically. In Proceedings of the 2020 4th International Conference on E-Education, E-Business and E-Technology. 2020; 56–60.
Stack Overflow Dataset. https://www.kaggle.com/datasets/stackoverflow/ stackoverflow
Yazdaninia M, Lo D, Sami A. Characterization and prediction of questions without accepted answers on stack overflow. In 2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC). IEEE. 2021; 59–70.
Zhang H, Zeng P, Hu Y, Qian J, Song J, Gao L. Learning visual question answering on controlled semantic noisy labels. Pattern Recognition. 2023; 138:109339.
Roy PK, Saumya S, Singh JP, Banerjee S, Gutub A. Analysis of community question‐answering issues via machine learning and deep learning: State‐of‐the‐art review. CAAI Transactions on Intelligence Technology. 2023; 8(1):95-117.
Fan M, Lin W, Feng Y, Sun M, Li P. A globalization-semantic matching neural network for paraphrase identification. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018; 2067–2075.
Vani K, Gupta D. Text plagiarism classification using syntax-based linguistic features. Expert Systems with Applications. 2017; 88:448–464.
Wang L, Zhang L, Jiang J. Duplicate question detection with deep learning in a stack overflow. IEEE Access. 2020; 8:25964–25975.
Prabowo DA, Herwanto GB. Duplicate question detection in question-answer websites using a convolutional neural network. In 2019 5th International conference on science and technology (ICST). IEEE. 2019; 1:1–6.
Roy PK, Singh JP. Predicting closed questions on community question answering sites using convolutional neural network: Neural Computing and Applications. 2020; 32(14):10555-10572.
Chali Y, Islam R. Question-question similarity in online forums. In Proceedings of the 10th annual meeting of the forum for information retrieval evaluation. 2018; 21–28.
Kamath CN, Bukhari SS, Dengel A. Comparative study between traditional machine learning and deep learning approaches for text classification. In Proceedings of the ACM Symposium on Document Engineering. 2018; 1–11.
Kim Y, Jernite Y, Sontag D, Rush A. Characteraware neural language models. In Proceedings of the AAAI conference on artificial intelligence 2016; 30.
Jiang JY, Zhang M, Li C, Bendersky M, Golbandi N, Najork M. Semantic text matching for long-form documents. In The world wide web conference. 2019; 795–806.
Imtiaz Z, Umer M, Ahmad M, Ullah S, Choi GS, Mehmood A. Duplicate questions pair detection using siamese malstm. IEEE Access. 2020; 8:21932–21942.
Goldberg Y, Levy O. word2vec explained: deriving mikolov et al.’s negative-sampling word-embedding method. arXiv preprint arXiv:1402.3722. 2014.
Eyecioglu A, Keller B. Twitter paraphrase identification with simple overlap features and svms. In Proceedings of the 9th International Workshop on Semantic Evaluation. 2015; 64–69.
Mudgal RK, Niyogi R, Milani A, Franzoni V. Analysis of tweets to find the basis of popularity based on events semantic similarity. International Journal of Web Information Systems. 2018; 14(4):438–452.
Roul RK, Sahoo JK, Arora K. Modified tf-idf term weighting strategies for text categorization. In 2017 14th IEEE India council international conference (INDICON). IEEE. 2017; 1–6.
Dey K, Shrivastava R, Kaushik S. A paraphrase and semantic similarity detection system for user generated short-text content on microblogs. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers. 2016; 2880–2890.
Hassanzadeh H, Groza T, Nguyen A, Hunter J. A supervised approach to quantifying sentence similarity: with application to evidence based medicine. PloS one. 2015; 10(6):e0129392.
Soğancıoğlu G, Öztürk H, Özgür A. Biosses: a semantic sentence similarity estimation system for the biomedical domain. Bioinformatics. 2017; 33(14):i49–i58.
Wu D, Huang J, Yang S. A joint model for sentence semantic similarity learning. In 2017 13th International Conference on Semantics, Knowledge and Grids (SKG). IEEE. 2017; 120–125.
Shaheer S, Hossain I, Sarna SN, Mehedi MHK, Rasel AA. Evaluating Question generation models using QA systems and Semantic Textual Similarity. In 2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC). IEEE. 2023; 0431-0435
Amur ZH, Hooi KY, Bhanbhro H, Dahri K, Soomro GM. Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives. Applied Sciences. 2023; 13(6):3911.
Huang J, Yao S, Lyu C, Ji D. Multi-granularity neural sentence model for measuring short text similarity. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 10177 LNCS. 2017; 439– 455. doi: 10.1007/978-3-319-55753-3_28.
Ferreira R, Cavalcanti GDC, Freitas F, Lins RD, Simske SJ, Riss M. Combining sentence similarities measures to identify paraphrases. Comput. Speech Lang. 2018; 47:59–73. doi: 10.1016/j.csl.2017.07.002.
Jiang JY, Bendersky M, Zhang M, Golbandi N, Li C, Najork M. Semantic text matching for long-form documents. Web Conf. 2019 - Proc. World Wide Web Conf. WWW 2019. 2019; 795–806. doi: 10.1145/3308558.3313707.
Homma Y, Sy S, Yeh C. Detecting Duplicate Questions with Deep Learning. 30th Conf. Neural Inf. Process. Syst. (NIPS 2016), no. Nips. 2016; 1–8. https://pdfs.semanticscholar.org/6ffd/e80e503fe6125237476494e777f4fe6d62c4.pdf

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[1] Ye X, Manoharan S. Marking essays automatically. In Proceedings of the 2020 4th International Conference on E-Education, E-Business and E-Technology. 2020; 56–60.

[2] Stack Overflow Dataset. https://www.kaggle.com/datasets/stackoverflow/ stackoverflow

[3] Yazdaninia M, Lo D, Sami A. Characterization and prediction of questions without accepted answers on stack overflow. In 2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC). IEEE. 2021; 59–70.

[4] Zhang H, Zeng P, Hu Y, Qian J, Song J, Gao L. Learning visual question answering on controlled semantic noisy labels. Pattern Recognition. 2023; 138:109339.

[5] Roy PK, Saumya S, Singh JP, Banerjee S, Gutub A. Analysis of community question‐answering issues via machine learning and deep learning: State‐of‐the‐art review. CAAI Transactions on Intelligence Technology. 2023; 8(1):95-117.

[6] Fan M, Lin W, Feng Y, Sun M, Li P. A globalization-semantic matching neural network for paraphrase identification. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018; 2067–2075.

[7] Vani K, Gupta D. Text plagiarism classification using syntax-based linguistic features. Expert Systems with Applications. 2017; 88:448–464.

[8] Wang L, Zhang L, Jiang J. Duplicate question detection with deep learning in a stack overflow. IEEE Access. 2020; 8:25964–25975.

[9] Prabowo DA, Herwanto GB. Duplicate question detection in question-answer websites using a convolutional neural network. In 2019 5th International conference on science and technology (ICST). IEEE. 2019; 1:1–6.

[10] Roy PK, Singh JP. Predicting closed questions on community question answering sites using convolutional neural network: Neural Computing and Applications. 2020; 32(14):10555-10572.

[11] Chali Y, Islam R. Question-question similarity in online forums. In Proceedings of the 10th annual meeting of the forum for information retrieval evaluation. 2018; 21–28.

[12] Kamath CN, Bukhari SS, Dengel A. Comparative study between traditional machine learning and deep learning approaches for text classification. In Proceedings of the ACM Symposium on Document Engineering. 2018; 1–11.

[13] Kim Y, Jernite Y, Sontag D, Rush A. Characteraware neural language models. In Proceedings of the AAAI conference on artificial intelligence 2016; 30.

[14] Jiang JY, Zhang M, Li C, Bendersky M, Golbandi N, Najork M. Semantic text matching for long-form documents. In The world wide web conference. 2019; 795–806.

[15] Imtiaz Z, Umer M, Ahmad M, Ullah S, Choi GS, Mehmood A. Duplicate questions pair detection using siamese malstm. IEEE Access. 2020; 8:21932–21942.

[16] Goldberg Y, Levy O. word2vec explained: deriving mikolov et al.’s negative-sampling word-embedding method. arXiv preprint arXiv:1402.3722. 2014.

[17] Eyecioglu A, Keller B. Twitter paraphrase identification with simple overlap features and svms. In Proceedings of the 9th International Workshop on Semantic Evaluation. 2015; 64–69.

[18] Mudgal RK, Niyogi R, Milani A, Franzoni V. Analysis of tweets to find the basis of popularity based on events semantic similarity. International Journal of Web Information Systems. 2018; 14(4):438–452.

[19] Roul RK, Sahoo JK, Arora K. Modified tf-idf term weighting strategies for text categorization. In 2017 14th IEEE India council international conference (INDICON). IEEE. 2017; 1–6.

[20] Dey K, Shrivastava R, Kaushik S. A paraphrase and semantic similarity detection system for user generated short-text content on microblogs. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers. 2016; 2880–2890.

[21] Hassanzadeh H, Groza T, Nguyen A, Hunter J. A supervised approach to quantifying sentence similarity: with application to evidence based medicine. PloS one. 2015; 10(6):e0129392.

[22] Soğancıoğlu G, Öztürk H, Özgür A. Biosses: a semantic sentence similarity estimation system for the biomedical domain. Bioinformatics. 2017; 33(14):i49–i58.

[23] Wu D, Huang J, Yang S. A joint model for sentence semantic similarity learning. In 2017 13th International Conference on Semantics, Knowledge and Grids (SKG). IEEE. 2017; 120–125.

[24] Shaheer S, Hossain I, Sarna SN, Mehedi MHK, Rasel AA. Evaluating Question generation models using QA systems and Semantic Textual Similarity. In 2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC). IEEE. 2023; 0431-0435

[25] Amur ZH, Hooi KY, Bhanbhro H, Dahri K, Soomro GM. Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives. Applied Sciences. 2023; 13(6):3911.

[26] Huang J, Yao S, Lyu C, Ji D. Multi-granularity neural sentence model for measuring short text similarity. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 10177 LNCS. 2017; 439– 455. doi: 10.1007/978-3-319-55753-3_28.

[27] Ferreira R, Cavalcanti GDC, Freitas F, Lins RD, Simske SJ, Riss M. Combining sentence similarities measures to identify paraphrases. Comput. Speech Lang. 2018; 47:59–73. doi: 10.1016/j.csl.2017.07.002.

[28] Jiang JY, Bendersky M, Zhang M, Golbandi N, Li C, Najork M. Semantic text matching for long-form documents. Web Conf. 2019 - Proc. World Wide Web Conf. WWW 2019. 2019; 795–806. doi: 10.1145/3308558.3313707.

[29] Homma Y, Sy S, Yeh C. Detecting Duplicate Questions with Deep Learning. 30th Conf. Neural Inf. Process. Syst. (NIPS 2016), no. Nips. 2016; 1–8. https://pdfs.semanticscholar.org/6ffd/e80e503fe6125237476494e777f4fe6d62c4.pdf

Revolutionizing Duplicate Question Detection: A Deep Learning Approach for Stack Overflow

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要約

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