Future Directions of Query Understanding

[1]  W. Bruce Croft,et al.  Embedding-based Query Language Models , 2016, ICTIR.

[2]  W. Bruce Croft,et al.  Neural Ranking Models with Weak Supervision , 2017, SIGIR.

[3]  Jennifer Chu-Carroll,et al.  Building Watson: An Overview of the DeepQA Project , 2010, AI Mag..

[4]  Milad Shokouhi,et al.  Fighting search engine amnesia: reranking repeated results , 2013, SIGIR.

[5]  Gerhard Weikum,et al.  WWW 2007 / Track: Semantic Web Session: Ontologies ABSTRACT YAGO: A Core of Semantic Knowledge , 2022 .

[6]  Yang Song,et al.  Query suggestion by constructing term-transition graphs , 2012, WSDM '12.

[7]  Phil Blunsom,et al.  A Convolutional Neural Network for Modelling Sentences , 2014, ACL.

[8]  Larry P. Heck,et al.  Learning deep structured semantic models for web search using clickthrough data , 2013, CIKM.

[9]  Wei Chu,et al.  Modeling the impact of short- and long-term behavior on search personalization , 2012, SIGIR '12.

[10]  Ruhi Sarikaya An overview of the system architecture and key components The Technology Behind Personal Digital Assistants , 2022 .

[11]  Jakob Grue Simonsen,et al.  A Hierarchical Recurrent Encoder-Decoder for Generative Context-Aware Query Suggestion , 2015, CIKM.

[12]  W. Bruce Croft,et al.  Relevance-based Word Embedding , 2017, SIGIR.

[13]  James Allan,et al.  A Comparative Study of Utilizing Topic Models for Information Retrieval , 2009, ECIR.

[14]  Yoshua Bengio,et al.  Learning Concept Embeddings for Query Expansion by Quantum Entropy Minimization , 2014, AAAI.

[15]  Jianqiang Wang,et al.  Matching Meaning for Cross-Language Information Retrieval , 2012, Inf. Process. Manag..

[16]  Ido Guy,et al.  Searching by Talking: Analysis of Voice Queries on Mobile Web Search , 2016, SIGIR.

[17]  Hang Li,et al.  Semantic Matching in Search , 2014, SMIR@SIGIR.

[18]  Ricardo Baeza-Yates,et al.  Predicting The Next App That You Are Going To Use , 2015, WSDM.

[19]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[20]  Yoon Kim,et al.  Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.

[21]  W. Bruce Croft,et al.  Estimating Embedding Vectors for Queries , 2016, ICTIR.

[22]  Nemanja Djuric,et al.  Search Retargeting using Directed Query Embeddings , 2015, WWW.

[23]  Gilad Mishne,et al.  Towards recency ranking in web search , 2010, WSDM '10.

[24]  Marie-Francine Moens,et al.  Monolingual and Cross-Lingual Information Retrieval Models Based on (Bilingual) Word Embeddings , 2015, SIGIR.

[25]  Mausam,et al.  Open Information Extraction Systems and Downstream Applications , 2016, IJCAI.

[26]  Yangyang Shi,et al.  Deep LSTM based Feature Mapping for Query Classification , 2016, NAACL.

[27]  Alan R. Aronson,et al.  Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program , 2001, AMIA.

[28]  Fabrizio Silvestri,et al.  Context- and Content-aware Embeddings for Query Rewriting in Sponsored Search , 2015, SIGIR.

[29]  Mohand Boughanem,et al.  Towards a graph-based user profile modeling for a session-based personalized search , 2009, Knowledge and Information Systems.

[30]  Wei Gao,et al.  Exploiting query logs for cross-lingual query suggestions , 2010, TOIS.

[31]  Robert S. Taylor Question-Negotiation and Information Seeking in Libraries , 1968, Coll. Res. Libr..

[32]  Qing Zeng-Treitler,et al.  Exploring and developing consumer health vocabularies. , 2006, Journal of the American Medical Informatics Association : JAMIA.

[33]  Susan T. Dumais,et al.  Personalizing Search via Automated Analysis of Interests and Activities , 2005, SIGIR.

[34]  Zhiyuan Liu,et al.  End-to-End Neural Ad-hoc Ranking with Kernel Pooling , 2017, SIGIR.

[35]  James P. Callan,et al.  Learning to Reweight Terms with Distributed Representations , 2015, SIGIR.

[36]  Yelong Shen,et al.  A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval , 2014, CIKM.

[37]  Alexander Kotov,et al.  Embedding-based Query Expansion for Weighted Sequential Dependence Retrieval Model , 2017, SIGIR.

[38]  Jon M. Kleinberg,et al.  Bursty and Hierarchical Structure in Streams , 2002, Data Mining and Knowledge Discovery.

[39]  Wei Shen,et al.  Is Concept Mapping Useful for Biomedical Information Retrieval? , 2015, CLEF.

[40]  Xinlei Chen,et al.  Never-Ending Learning , 2012, ECAI.

[41]  Yi Zhang,et al.  Probase+: Inferring Missing Links in Conceptual Taxonomies , 2017, IEEE Transactions on Knowledge and Data Engineering.

[42]  Bhaskar Mitra,et al.  Improving Document Ranking with Dual Word Embeddings , 2016, WWW.

[43]  Didier Sornette,et al.  Robust dynamic classes revealed by measuring the response function of a social system , 2008, Proceedings of the National Academy of Sciences.

[44]  Milad Shokouhi,et al.  Time-sensitive query auto-completion , 2012, SIGIR '12.

[45]  Hinrich Schütze,et al.  The SMAPH system for query entity recognition and disambiguation , 2014, ERD '14.

[46]  Hinrich Schütze,et al.  Personalized search , 2002, CACM.

[47]  Dae Hoon Park,et al.  A Neural Language Model for Query Auto-Completion , 2017, SIGIR.

[48]  W. Bruce Croft,et al.  Asking Clarifying Questions in Open-Domain Information-Seeking Conversations , 2019, SIGIR.

[49]  Idan Szpektor,et al.  Identifying Web Queries with Question Intent , 2016, WWW.

[50]  Kyunghyun Cho,et al.  Task-Oriented Query Reformulation with Reinforcement Learning , 2017, EMNLP.

[51]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[52]  Rabab Kreidieh Ward,et al.  Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval , 2015, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[53]  Marie-Francine Moens,et al.  A survey on question answering technology from an information retrieval perspective , 2011, Inf. Sci..

[54]  Ido Guy,et al.  Personalized social search based on the user's social network , 2009, CIKM.

[55]  Paolo Ferragina,et al.  From TagME to WAT: a new entity annotator , 2014, ERD '14.

[56]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[57]  John Lafferty,et al.  Information retrieval as statistical translation , 1999, SIGIR 1999.

[58]  Daniel Jurafsky,et al.  Neural Language Correction with Character-Based Attention , 2016, ArXiv.

[59]  Shubhra Kanti Karmaker Santu,et al.  Modeling the Influence of Popular Trending Events on User Search Behavior , 2017, WWW.

[60]  Idan Szpektor,et al.  Improving Term Weighting for Community Question Answering Search Using Syntactic Analysis , 2014, CIKM.

[61]  Nigel Collier,et al.  Normalising Medical Concepts in Social Media Texts by Learning Semantic Representation , 2016, ACL.

[62]  Yann LeCun,et al.  What is the best multi-stage architecture for object recognition? , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[63]  Jennifer Widom,et al.  Scaling personalized web search , 2003, WWW '03.

[64]  M. de Rijke,et al.  Time-sensitive Personalized Query Auto-Completion , 2014, CIKM.

[65]  Guy Divita,et al.  Failure Analysis of MetaMap Transfer (MMTx) , 2004, MedInfo.

[66]  Fabio Crestani,et al.  Building user profiles from topic models for personalised search , 2013, CIKM.

[67]  Xiaojie Liu,et al.  Constraining Word Embeddings by Prior Knowledge - Application to Medical Information Retrieval , 2016, AIRS.

[68]  Susan T. Dumais,et al.  Understanding temporal query dynamics , 2011, WSDM '11.

[69]  Fernando Diaz,et al.  Temporal profiles of queries , 2007, TOIS.

[70]  Hongbo Deng,et al.  Ranking Relevance in Yahoo Search , 2016, KDD.

[71]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[72]  Oren Kurland,et al.  Query Expansion Using Word Embeddings , 2016, CIKM.

[73]  Nitish Aggarwal,et al.  Proactive Information Retrieval: Anticipating Users' Information Need , 2016, ECIR.

[74]  Nick Craswell,et al.  Learning to Match using Local and Distributed Representations of Text for Web Search , 2016, WWW.

[75]  M. de Rijke,et al.  An Introduction to Click Models for Web Search: SIGIR 2015 Tutorial , 2015, SIGIR.

[76]  Quoc V. Le,et al.  Distributed Representations of Sentences and Documents , 2014, ICML.

[77]  Eugene Agichtein,et al.  Crowdsourcing for (almost) Real-time Question Answering , 2016 .

[78]  Suhas Ranganath Leveraging Catalog Knowledge Graphs for Query Attribute Identification in E-Commerce Sites , 2018, ArXiv.

[79]  Yan Zhang,et al.  Tailor knowledge graph for query understanding: linking intent topics by propagation , 2014, EMNLP.

[80]  Giuseppe Ottaviano,et al.  Fast and Space-Efficient Entity Linking for Queries , 2015, WSDM.

[81]  Bhaskar Mitra,et al.  Query Auto-Completion for Rare Prefixes , 2015, CIKM.

[82]  Marta R. Costa-jussà,et al.  Towards Interlingua Neural Machine Translation , 2019, ArXiv.

[83]  Diane Kelly,et al.  Methods for Evaluating Interactive Information Retrieval Systems with Users , 2009, Found. Trends Inf. Retr..

[84]  Nick Craswell,et al.  Query Expansion with Locally-Trained Word Embeddings , 2016, ACL.

[85]  Ye Zhang,et al.  A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification , 2015, IJCNLP.

[86]  Praveen Paritosh,et al.  Freebase: a collaboratively created graph database for structuring human knowledge , 2008, SIGMOD Conference.