Improving User Intent Detection in Urdu Web Queries with Capsule Net Architectures
Source abstract
Abstract
Detecting the communicative intent behind user queries is critically required by search engines to understand a user’s search goal and retrieve the desired results. Due to increased web searching in local languages, there is an emerging need to support the language understanding for languages other than English. This article presents a distinctive, capsule neural network architecture for intent detection from search queries in Urdu, a widely spoken South Asian language. The proposed two-tiered capsule network utilizes LSTM cells and an iterative routing mechanism between the capsules to effectively discriminate diversely expressed search intents. Since no Urdu queries dataset is available, a benchmark intent-annotated dataset of 11,751 queries was developed, incorporating 11 query domains and annotated with Broder’s intent taxonomy (i.e., navigational, transactional and informational intents). Through rigorous experimentation, the proposed model attained the state of the art accuracy of 91.12%, significantly improving upon several alternate classification techniques and strong baselines. An error analysis revealed systematic error patterns owing to a class imbalance and large lexical variability in Urdu web queries.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Topic Modeling · Information Retrieval and Search Behavior · Text and Document Classification Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science, University of Engineering and Technology, Lahore, Pakistan
Funding
Higher Education Commission (HEC) · National Center in Big Data and Cloud Computing and Innovation Challenge Fund & Action Research
References (40)
Show source-provided references
1. Jansen, B.J., and Booth, D. (2010, January 10–15). Classifying Web Queries by Topic and User Intent. Proceedings of the CHI’10 Extended Abstracts on Human Factors in Computing Systems, Atlanta, GA, USA.10.1145/1753846.1754140 ↗
2. Syntactic complexity of web search queries through the lenses of language models, networks and users10.1016/j.ipm.2016.04.002 ↗
3. Barr, C., Jones, R., and Regelson, M. (2008, January 25–27). The linguistic structure of English web-search queries. Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, EMNLP, Honolulu, HI, USA.10.3115/1613715.1613848 ↗
4. Towards building a Urdu Language Corpus using Common Crawl10.3233/JIFS-179904 ↗
5. A Taxonomy of Web Search10.1145/792550.792552 ↗
6. Chang, Y., and Deng, H. (2020). Query Intent Understanding. Query Understanding for Search Engines, Springer International Publishing.10.1007/978-3-030-58334-7 ↗
7. Sabour, S., Frosst, N., and Hinton, G.E. (2017, January 4–9). Dynamic routing between capsules. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA.
8. Liu, H., Liu, Y., Wong, L.-P., Lee, L.-K., Hao, T., and Yang, Z. (2020). A Hybrid Neural Network BERT-Cap Based on Pre-Trained Language Model and Capsule Network for User Intent Classification. Complexity, 2020.10.1155/2020/8858852 ↗
9. Zhang, C., Li, Y., Du, N., Fan, W., and Yu, P. (2019). Joint Slot Filling and Intent Detection via Capsule Neural Networks, Association for Computational Linguistics.10.18653/v1/P19-1519 ↗
10. Xia, C., Zhang, C., Yan, X., Chang, Y., and Yu, P. (November, January 31). Zero-Shot User Intent Detection via Capsule Neural Networks. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium.10.18653/v1/D18-1348 ↗
11. Lexical Intent Recognition in Urdu Queries Using Deep Neural Networks
12. Determining the informational, navigational, and transactional intent of Web queries10.1016/j.ipm.2007.07.015 ↗
13. Rose, D.E., and Levinson, D. (2004, January 17–20). Understanding User Goals in Web Search. Proceedings of the 13th international conference on World Wide Web, New York, NY, USA.10.1145/988672.988675 ↗
14. Gonzalez-Caro, C., and Baeza-Yates, R. (2011). A Multi-Faceted Approach to Query Intent Classification. String Processing and Information Retrieval. SPIRE 2011, Springer. Lecture Notes in Computer Science.10.1007/978-3-642-24583-1_36 ↗
15. Kang, I.-H., and Kim, G. (August, January 28). Query Type Classification for Web Document Retrieval. Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval, Toronto, ON, Canada.
16. Pass, G., Chowdhury, A., and Torgeson, C. (June, January 30). A Picture of Search. Proceedings of the 1st International Conference on Scalable Information Systems, Hong Kong, China.10.1145/1146847.1146848 ↗
17. Fernández-Martínez, F., Luna-Jiménez, C., Kleinlein, R., Griol, D., Callejas, Z., and Montero, J.M. (2022). Fine-Tuning BERT Models for Intent Recognition Using a Frequency Cut-Off Strategy for Domain-Specific Vocabulary Extension. Appl. Sci., 12.10.3390/app12031610 ↗
18. Trang, N.T.T., Anh, D.T.D., Viet, V.Q., and Woomyoung, P. (2022). Advanced Joint Model for Vietnamese Intent Detection and Slot Tagging, Springer International Publishing.10.1007/978-3-031-08878-0_9 ↗
19. Schuster, S., Gupta, S., Shah, R., and Lewis, M. (2019). Cross-Lingual Transfer Learning for Multilingual Task Oriented Dialog, Association for Computational Linguistics.10.18653/v1/N19-1380 ↗
20. Xu, W., Haider, B., and Mansour, S. (2020). End-to-End Slot Alignment and Recognition for Cross-Lingual NLU, Association for Computational Linguistics.10.18653/v1/2020.emnlp-main.410 ↗
21. Hemphill, C.T., Godfrey, J.J., and Doddington, G.R. (1990, January 24–27). The ATIS Spoken Language Systems Pilot Corpus. Proceedings of the Speech and Natural Language, Hidden Valley, PA, USA.10.3115/116580.116613 ↗
22. Upadhyay, S., Faruqui, M., Tür, G., Dilek, H., and Heck, L. (2018, January 15–20). (Almost) Zero-Shot Cross-Lingual Spoken Language Understanding. Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada.10.1109/ICASSP.2018.8461905 ↗
23. Balodis, K., and Deksne, D. (2019). FastText-Based Intent Detection for Inflected Languages. Information, 10.10.3390/info10050161 ↗
24. Braun, D., Hernandez-Mendez, A., Matthes, F., and Langen, M. (2017, January 15–17). Evaluating Natural Language Understanding Services for Conversational Question Answering Systems. Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue, Saarbrücken, Germany.10.18653/v1/W17-5522 ↗
25. Pinnis, M., Rikters, M.i., and Krišlauks, R. (2018). Tilde’s Machine Translation Systems for WMT 2018, Association for Computational Linguistics.10.18653/v1/W18-6423 ↗
26. Zhang, H.W.S.L.L.C.D., and Xinlei, Z. (2017, January 9–10). Query Classification Using Convolutional Neural Networks. Proceedings of the 10th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China.10.1109/ISCID.2017.212 ↗
27. Hashemi, H.B., and Reiner Kraft, A.A. (2016, January 22–25). Query Intent Detection using Convolutional Neural Networks. Proceedings of the International Conference on Web Search and Data Mining, Workshop on Query Understanding, San Francisco, CA, USA.
28. Deep Bi-Directional LSTM Network for Query Intent Detection10.1016/j.procs.2018.10.341 ↗
29. Ravuri, S.V., and Stolcke, A. (2015, January 6–10). Recurrent neural network and LSTM models for lexical utterance classification. Proceedings of the Sixteenth Annual Conference of the International Speech Communication Association (INTERSPEECH), Dresden, Germany.10.21437/Interspeech.2015-42 ↗
30. Staliūnaitė, I., and Iacobacci, I. (2020, January 4–8). Auxiliary Capsules for Natural Language Understanding. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.10.1109/ICASSP40776.2020.9053899 ↗
31. Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019, January 2–7). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, MN, USA.
32. Mikolov, T., Sutskever, I., Chen, K., Corrado, G., and Dean, J. (2013, January 5–10). Distributed representations of words and phrases and their compositionality. Proceedings of the 26th International Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA.
33. A “Suggested” Picture of Web Search in Turkish10.1145/2891105 ↗
34. A Coefficient of Agreement for Nominal Scales10.1177/001316446002000104 ↗
35. Bin Zia, H., Raza, A.A., and Athar, A. (2018, January 20–26). Urdu Word Segmentation using Conditional Random Fields (CRFs). Proceedings of the 27th International Conference on Computational Linguistics, Santa Fe, NM, USA.
36. Cluster analysis of urdu tweets
37. Kim, Y. (2014, January 26–28). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar.10.3115/v1/D14-1181 ↗
38. Lau, C.Z., Chonglin, S., Zhiyuan, L., and Francis, C.M. (2015). A C-LSTM Neural Network for Text Classification. arXiv.
39. Improving Phrase Chunking by using Contextualized Word Embeddings for a Morphologically-Rich Language10.1007/s13369-021-06343-7 ↗
40. Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv.