## 2 Related Works  
User intent detection from search queries requires an intent-annotated dataset for system learning. The most widely-used intent taxonomy for search queries was developed by [PERSON] [5]. In this taxonomy, a single level structure of three intent classes, namely, the informational, navigational and transactional, was proposed. [PERSON] et al. [12] extended [PERSON]'s taxonomy by defining secondary and tertiary level intent classes for each of the three top level intents. [PERSON] and [PERSON] [13] redefined [PERSON]'s taxonomy by introducing sub-levels and replacing the \"transational intent\" with a \"resource seeking intent.\" In this restructuring, at level 2, five sub-classes for the informational and four for the resource intent were formulated. [PERSON] et al. [14] proposed a different taxonomy from the earlier research, and classified queries as informational, not informational and ambiguous. The intent taxonomies were used to annotate datasets extracted from publicly-released query logs, e.g., the TREC Web Corpus and WT10g collection ([[http://ir.dcs.gla.ac.uk/test_collections/](http://ir.dcs.gla.ac.uk/test_collections/)]([http://ir.dcs.gla.ac.uk/test_collections/](http://ir.dcs.gla.ac.uk/test_collections/)), accessed on 15 June 2022), AltaVista logs [13], DogPile [12], Lycos [15], MSN Search Query log ([[http://www.sobigdata.eu/content/query-log-msn-rfp-2006](http://www.sobigdata.eu/content/query-log-msn-rfp-2006)]([http://www.sobigdata.eu/content/query-log-msn-rfp-2006](http://www.sobigdata.eu/content/query-log-msn-rfp-2006)), accessed on 15 June 2022), Yahoo ([[https://webscope.sandbox.yahoo.com/](https://webscope.sandbox.yahoo.com/)]([https://webscope.sandbox.yahoo.com/](https://webscope.sandbox.yahoo.com/)), accessed on 15 June 2022) and AOL Search query logs [16]. In addition, search query logs from Russian ([[http://switchdetect.yandex.ru/en/datasets](http://switchdetect.yandex.ru/en/datasets)]([http://switchdetect.yandex.ru/en/datasets](http://switchdetect.yandex.ru/en/datasets)), accessed on 15 June 2022), Chinese ([[http://www.sogou.com/labs/](http://www.sogou.com/labs/)]([http://www.sogou.com/labs/](http://www.sogou.com/labs/)), accessed on 15 June 2022), Chilean [17] and Vietnamese [18] search engines were also used. Web query datasets in local languages have also been developed through translation. For example, [PERSON] et al. [19] reported a translated query dataset for Spanish and Thai. In MultiATIS++ [20], an English ATIS dataset [21] was translated in eight languages, extending an earlier research by [PERSON] et al. [22]. PhoATIS, a Vietnamese query dataset, was developed by translating an English ATIS dataset in Vietnamese [18]. Additionally, query datasets for the Estonian, Latvian, Lithuanian, and Russian languages were developed [23] using the Tilde machine-translation system by translating publicly-released datasets [24; 25]. Moreover, two Urdu queries datasets were developed by translating English ATIS and AOL queries datasets in Urdu [11]. Although translated query datasets have been used for intent detection research, they are, however, limited in capturing the local syntactic structure, vocabulary and search characteristics. In this study, a native web queries dataset was designed and developed to capture this variability for user intent detection. Traditionally, user intent detection models have exploited classifiers such as support vector machines (SVM), naive bayes and logistic regression with discriminative features modelled from corpora, query logs or pre-trained language models [6]. Subsequent approaches have extensively developed Convolutional Neural Network (CNN)-based [26; 27] and long short term memory (LSTM)-based [28; 29] architectures for classifying intents from diversely-framed user queries, mostly in English. Recently, capsule neural network-based approaches have been explored for detecting the intent from user queries in virtual assistants' and dialogue systems' scenarios.