A Survey of Query Auto Completion in Information Retrieval

Cai, Fei; Rijke, Maarten de

doi:10.1561/1500000055

Cited by 123 publications

(100 citation statements)

References 5 publications

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“…Query autocompletion (QAC) [1,19,22] feature improves the user search experience by providing the most relevant query completions matching the query prefix entered by the user. QAC has many advantages such as saving user time and keystrokes required to enter the query, avoiding spelling mistakes and formulation of better queries [3]. The candidates for query completion are usually selected from search query logs which record what other users have searched for [1].…”

Section: Query Completion Suggestions In Sesmentioning

confidence: 99%

“…The candidates for query completion are usually selected from search query logs which record what other users have searched for [1]. 1 During query time, those candidates which match the given query prefix are ranked based on various relevance signals such as time, location and user search history to finally arrive at the top k relevant completions to be displayed [3,7]. The value of k usually varies from 4-10 depending on each web search engine.…”

Section: Query Completion Suggestions In Sesmentioning

confidence: 99%

“…Xbox Conversations (Xbox is gaming console device) 3 We have provided label distribution in Table 9 and examples in Table 10. Table 11 shows the Precision, Recall and F1 scores with various models on Zo dataset.…”

Section: Details Of Conversation Datasetsmentioning

confidence: 99%

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Deep learning for detecting inappropriate content in text

Yenala

Jhanwar

Chinnakotla

et al. 2017

Int J Data Sci Anal

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Today, there are a large number of online discussion fora on the internet which are meant for users to express, discuss and exchange their views and opinions on various topics. For example, news portals, blogs, social media channels such as youtube. typically allow users to express their views through comments. In such fora, it has been often observed that user conversations sometimes quickly derail and become inappropriate such as hurling abuses, passing rude and discourteous comments on individuals or certain groups/communities. Similarly, some virtual agents or bots have also been found to respond back to users with inappropriate messages. As a result, inappropriate messages or comments are turning into an online menace slowly degrading the effectiveness of user experiences. Hence, automatic detection and filtering of such inappropriate language has become an important problem for improving the quality of conversations with users as well as virtual agents. In this paper, we propose a novel deep learning-based technique for automatically identifying such inappropriate language. We especially focus on solving this problem in two application scenarios-(a) Query completion suggestions in search engines and (b) Users conversations in messengers. Detecting inappropriate language is challenging due to various natural language phenomenon such as spelling mistakes and variations, polysemy, contextual ambiguity and semantic variations. For identifying inappropriate query suggestions, we propose a novel deep learning architecture called "Convolutional Bi-Directional LSTM (C-BiLSTM)" which combines the strengths of both Convolution Neural Networks (CNN) and Bi-directional LSTMs (BLSTM). For filtering inappropriate conversations, we use LSTM and Bi-directional LSTM (BLSTM) sequential models. The proposed models do not rely on hand-crafted features, are trained end-end as a single model, and effectively capture both local features as well as their global semantics. Evaluating C-BiLSTM, LSTM and BLSTM models on real-world search queries and conversations reveals that they significantly outperform both pattern-based and other hand-crafted feature-based baselines.

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Section: Query Completion Suggestions In Sesmentioning

confidence: 99%

Section: Query Completion Suggestions In Sesmentioning

confidence: 99%

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Deep learning for detecting inappropriate content in text

Yenala

Jhanwar

Chinnakotla

et al. 2017

Int J Data Sci Anal

View full text Add to dashboard Cite

show abstract

“…Term completion (or QAC) carried over to search with Google Suggest 2 , with all major commercial web search engines today oering some form of QAC functionality. QAC systems aim to reduce the time taken by a searcher to enter a query, reduce the likelihood of spelling mistakes (10-15% of queries entered were misspelt before the introduction of QAC [10]), discover relevant search items, and even assist with potential query reformulation(s) [6,20].…”

Section: Related Workmentioning

confidence: 99%

“…Literature in the area of QAC is diverse, with QAC oen viewed as a ranking problem [2,6]. Works have focused upon improving QAC suggestion candidates with a variety of techniques (both heuristic-based and learning-based [6]), as well as how such approaches are evaluated.…”

Section: Related Workmentioning

confidence: 99%

Large-scale Generative Query Autocompletion

Maxwell¹,

Bailey²,

Hawking³

2017

Proceedings of the 22nd Australasian Document Computing Symposium

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ABSTRACTery Autocompletion (QAC) systems are interactive tools that assist a searcher in entering a query, given a partial query prex. Existing QAC research -with a number of notable exceptionsrelies upon large existing query logs from which to extract historical queries. ese queries are then ordered by some ranking algorithm as candidate completions, given the query prex. Given the numerous search environments (e.g. enterprises, personal or secured data repositories) in which large query logs are unavailable, the need for synthetic -or generative -QAC systems will become increasingly important. Generative QAC systems may be used to augment traditional query-based approaches, and/or entirely replace them in certain privacy sensitive applications. Even in commercial Web search engines, a signicant proportion (up to 15%) of queries issued daily have never been seen previously, meaning there will always be opportunity to assist users in formulating queries which have not occurred historically. In this paper, we describe a system that can construct generative QAC suggestions within a user-acceptable timeframe (⇠58ms), and report on a series of experiments over three publicly available, large-scale question sets that investigate dierent aspects of the system's performance.

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Interactive faceted query suggestion for exploratory search: Whole‐session effectiveness and interaction engagement

Ruotsalo

Kaski

2019

Asso for Info Science & Tech

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The outcome of exploratory information retrieval is not only dependent on the effectiveness of individual responses to a set of queries, but also on relevant information retrieved during the entire exploratory search session. We study the effect of search assistance, operationalized as an interactive faceted query suggestion, for both whole-session effectiveness and engagement through interactive faceted query suggestion. A user experiment is reported, where users performed exploratory search tasks, comparing interactive faceted query suggestion and a control condition with only conventional typed-query interaction. Data comprised of interaction and search logs show that the availability of interactive faceted query suggestion substantially improves whole-session effectiveness by increasing recall without sacrificing precision. The increased engagement with interactive faceted query suggestion is targeted to direct situated navigation around the initial query scope, but is not found to improve individual queries on average. The results imply that research in exploratory search should focus on measuring and designing tools that engage users with directed situated navigation support for improving whole-session performance.

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A Survey of Query Auto Completion in Information Retrieval

Cited by 123 publications

References 5 publications

Deep learning for detecting inappropriate content in text

Deep learning for detecting inappropriate content in text

Large-scale Generative Query Autocompletion

Interactive faceted query suggestion for exploratory search: Whole‐session effectiveness and interaction engagement

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