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Adapting Deep Learning Methods for Mental Health Prediction on Social Media.

, and . W-NUT@EMNLP, page 322-327. Association for Computational Linguistics, (2019)

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TakeLab at SemEval-2016 Task 6: Stance Classification in Tweets Using a Genetic Algorithm Based Ensemble., , , , , , , , and . SemEval@NAACL-HLT, page 464-468. The Association for Computer Linguistics, (2016)Exploiting Document-Based Features for Clarification in Conversational Search., , and . ECIR (1), volume 13185 of Lecture Notes in Computer Science, page 413-427. Springer, (2022)Adapting Deep Learning Methods for Mental Health Prediction on Social Media., and . CoRR, (2020)Evaluating Mixed-initiative Conversational Search Systems via User Simulation., , and . WSDM, page 888-896. ACM, (2022)User Engagement Prediction for Clarification in Search., , and . ECIR (1), volume 12656 of Lecture Notes in Computer Science, page 619-633. Springer, (2021)Exploiting Simulated User Feedback for Conversational Search: Ranking, Rewriting, and Beyond., , , , and . SIGIR, page 632-642. ACM, (2023)Impact of multi-path by ISS structure on GEROS-ISS measured waveforms., , , , , , , and . IGARSS, page 5603-5606. IEEE, (2016)Estimating the Usefulness of Clarifying Questions and Answers for Conversational Search., , , and . ECIR (3), volume 14610 of Lecture Notes in Computer Science, page 384-392. Springer, (2024)Adapting Deep Learning Methods for Mental Health Prediction on Social Media., and . W-NUT@EMNLP, page 322-327. Association for Computational Linguistics, (2019)Towards Self-Contained Answers: Entity-Based Answer Rewriting in Conversational Search., , and . CHIIR, page 209-218. ACM, (2024)