FDCM: Towards Balanced and Generalizable Concept-based Models for Effective Medical Ranking
Abstract
Concept-based IR is expected to improve the quality of medical ranking since it captures more semantics than BOW representations. However, bringing concepts and BOW together into a transparent IR framework is challenging. We propose a new aggregation parameter to combine conceptual and term-based Dirichlet Compound Model scores effectively. The determination of this linear parameter is the result of exploring to what degree the difference of the conceptual and term-based sum of IDFs is influential to the integration. Instead of employing heuristics to find combined models, this paper aims to build the grounds for establishing reasonable aggregation standards based on semantic query performance predictors.
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This video is the recorded presentation for the paper "FDCM: Towards Balanced and Generalizable Concept-based Models for Effective Medical Ranking". We briefly go over motivation, methodologies and evaluations of the paper.
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References
[1]
Asma Ben Abacha and Pierre Zweigenbaum. 2011. Automatic extraction of semantic relations between medical entities: a rule based approach. Journal of biomedical semantics, Vol. 2, 5 (2011), S4.
[2]
Maristella Agosti, Giorgio Maria Di Nunzio, Stefano Marchesin, and Gianmaria Silvello. 2019. Medical Retrieval using Structured Information Extracted from Knowledge Bases. Proc. of the 27th SEBD (in print) (2019).
[3]
Alan R Aronson. 1997. The MMI Ranking Function. Available in the website: https://ii. nlm. nih. gov/MTI/Details/mmi. shtml (1997).
[4]
Mohammad Bahrani and Thomas Roelleke. 2018. Novel Query Performance Predictors and their Correlations for Medical Applications. In Proc. 1st International Workshop on Generalization in Information Retrieval (GLARE 2018). http://glare2018. dei. unipd. it/paper/glare2018-paper3. pdf.
[5]
Ronan Cummins, Jiaul H Paik, and Yuanhua Lv. 2015. A Pólya urn document language model for improved information retrieval. ACM Transactions on Information Systems (TOIS), Vol. 33, 4 (2015), 21.
[6]
Charles Elkan. 2006. Clustering documents with an exponential-family approximation of the Dirichlet compound multinomial distribution. In Proceedings of the 23rd international conference on Machine learning. ACM, 289--296.
[7]
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. 2019. BioBERT: pre-trained biomedical language representation model for biomedical text mining. arXiv preprint arXiv:1901.08746 (2019).
[8]
Nut Limsopatham, Craig Macdonald, and Iadh Ounis. 2013. A task-specific query and document representation for medical records search. In European Conference on Information Retrieval. Springer, 747--751.
[9]
Carlo Meghini, Fausto Rabitti, and Costantino Thanos. 1991. Conceptual modeling of multimedia documents. Computer, Vol. 24, 10 (1991), 23--30.
[10]
Edgar Meij, Dolf Trieschnigg, Maarten De Rijke, and Wessel Kraaij. 2010. Conceptual language models for domain-specific retrieval. Information Processing & Management, Vol. 46, 4 (2010), 448--469.
[11]
Jiaul H Paik. 2013. A novel TF-IDF weighting scheme for effective ranking. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. 343--352.
[12]
Jay M Ponte and W Bruce Croft. 1998. A language modeling approach to information retrieval. In Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval. 275--281.
[13]
Thomas Roelleke, Andreas Kaltenbrunner, and Ricardo Baeza-Yates. 2015. Harmony assumptions in information retrieval and social networks. Comput. J., Vol. 58, 11 (2015), 2982--2999.
[14]
Wei Shen and Jian-Yun Nie. 2015. Is concept mapping useful for biomedical information retrieval?. In International conference of the cross-language evaluation forum for European languages. Springer, 281--286.
[15]
Ellen M Voorhees and William R Hersh. 2012. Overview of the TREC 2012 Medical Records Track.
[16]
Chunye Wang and Ramakrishna Akella. 2015. Concept-based relevance models for medical and semantic information retrieval. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. 173--182.
[17]
Chengxiang Zhai and John Lafferty. 2004. A study of smoothing methods for language models applied to information retrieval. ACM Transactions on Information Systems (TOIS), Vol. 22, 2 (2004), 179--214.
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- FDCM: Towards Balanced and Generalizable Concept-based Models for Effective Medical Ranking
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Information & Contributors
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Published In
October 2020
3619 pages
ISBN:9781450368599
DOI:10.1145/3340531
- General Chairs:
- Mathieu d'Aquin,
- Stefan Dietze,
- Program Chairs:
- Claudia Hauff,
- Edward Curry,
- Philippe Cudre Mauroux
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Published: 19 October 2020
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CIKM '20: The 29th ACM International Conference on Information and Knowledge Management
October 19 - 23, 2020
Virtual Event, Ireland
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