Christina Lioma

Christina Lioma

Professor

Member of:


    1. A Belief Model of Query Difficulty That Uses Subjective Logic

      Lioma, Christina, Blanco, R., Mochales-Palau, R. & Moens, M., 2009, ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory. Springer, p. 92-103

      Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    2. Published

      A Diagnostic Study of Explainability Techniques for Text Classification

      Atanasova, Pepa Kostadinova, Simonsen, Jakob Grue, Lioma, Christina & Augenstein, Isabelle, 2020, Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, p. 3256-3274

      Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    3. A Logical Inference Approach to Query Expansion with Social Tags

      Lioma, Christina, Blanco, R. & Moens, M., 2009, ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory. p. 358-361

      Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    4. A cascaded classification approach to semantic head recognition

      Michelbacher, L., Kothari, A., Lioma, Christina, Schütze, H. & Forst, M., 1 Jan 2011, EMNLP 2011 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference. p. 793-803 11 p.

      Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

    5. Published

      A hierarchical recurrent encoder-decoder for generative context-aware query suggestion

      Sordoni, A., Bengio, Y., Vahabi, H., Lioma, Christina, Simonsen, Jakob Grue & Nie, J., 2015, CIKM '15 Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. Association for Computing Machinery, p. 553-562 10 p.

      Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    6. Published

      A study of factuality, objectivity and relevance: three desiderata in large-scale information retrieval?

      Lioma, Christina, Larsen, B., Lu, W. & Huang, Y., 2016, Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies. Association for Computing Machinery, p. 107-117 11 p.

      Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    7. Published

      A study of metrics of distance and correlation between ranked lists for compositionality detection

      Lioma, Christina & Hansen, N. D., Aug 2017, In: Cognitive Systems Research. 44, p. 40-49 10 p.

      Research output: Contribution to journalJournal articleResearchpeer-review

    8. A subjective logic formalisation of the principle of polyrepresentation for information needs

      Lioma, Christina, Larsen, B., Schütze, H. & Ingwersen, P., 2010, IIiX '10 Proceedings of the third symposium on Information interaction in context : Pages 125-134 ACM New York, NY, USA ©2010 . Association for Computing Machinery, p. 125-134

      Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    9. A syntactically-based query reformulation technique for information retrieval

      Lioma, Christina & Ounis, I., 2008, In: Information Processing & Management. 44, 1, p. 143-162 20 p.

      Research output: Contribution to journalJournal articleResearchpeer-review

    10. Published

      Adaptive distributional extensions to DFR ranking

      Petersen, C., Simonsen, Jakob Grue, Järvelin, K. & Lioma, Christina, 2016, Proceedings of the 25th ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, p. 2005-2008 4 p.

      Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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