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Phenomena Explanation from Text: Unsupervised Learning of Interpretable and Statistically Significant Knowledge

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Data Management Technologies and Applications (DATA 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1446))

Abstract

Learning knowledge from text is becoming increasingly important as the amount of unstructured content on the Web rapidly grows. Despite recent breakthroughs in natural language understanding, the explanation of phenomena from textual documents is still a difficult and poorly addressed problem. Additionally, current NLP solutions often require labeled data, are domain-dependent, and based on black box models. In this paper, we introduce POIROT, a new descriptive text mining methodology for phenomena explanation from documents corpora. POIROT is designed to provide accurate and interpretable results in unsupervised settings, quantifying them based on their statistical significance. We evaluated POIROT on a medical case study, with the aim of learning the “voice of patients” from short social posts. Taking Esophageal Achalasia as a reference, we automatically derived scientific correlations with 79% F1-measure score and built useful explanations of the patients’ viewpoint on topics such as symptoms, treatments, drugs, and foods. We make the source code and experiment details publicly available (https://github.com/unibodatascience/POIROT).

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Notes

  1. 1.

    NLU Kaggle competition on COVID-19. https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge.

  2. 2.

    Folding is the process of adding new vectors to a space after its construction, without rebuilding it.

  3. 3.

    https://www.amae.it/.

  4. 4.

    https://www.orpha.net/consor/cgi-bin/SupportGroup_Search.php?lng=EN&data_id=106412.

  5. 5.

    https://www.facebook.com/groups/36705181245/.

  6. 6.

    https://www.textrazor.com/.

  7. 7.

    https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html#lexicon.

  8. 8.

    https://github.com/disi-unibo-nlu/POIROT.

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Acknowledgments

Gianluca Moro has developed this research and is the author of this methodology and its mathematical and algorithmic solutions, preliminarily reported also in [20, 21], which are also included in his text mining course at the University of Bologna since the 2014/15 academic year and applied for the discovery of the reasons that contribute to cause aircraft accidents (https://unibodatascience.github.io/textmining/) from the raw textual reports collected by the US National Transportation Safety Board (NTSB).

Giacomo Frisoni has successfully applied this methodology to the real medical case study included in this work and in [20, 21] and has contributed in this work to add the LDA and pLSA techniques and to apply and compare them in the case study.

We want to thank Cristina Lanni (https://www.researchgate.net/profile/Cristina_Lanni/research) (Researcher at the University of Pavia) and Celeste Napolitano (President of AMAE and National Secretary of the Italian Society of Narrative Medicine) for their precious help in building the dataset and participating in the realization of Achalasia gold standards.

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Authors and Affiliations

  1. Department of Computer Science and Engineering – DISI, University of Bologna, Cesena Campus, Via dell’Università 50, 47521, Cesena, Italy

    Giacomo Frisoni & Gianluca Moro

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  1. Giacomo Frisoni
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Correspondence to Gianluca Moro .

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Editors and Affiliations

  1. MODESTE/ESEO, Angers, France

    Slimane Hammoudi

  2. Fraunhofer FIT and RWTH Aachen University, Aachen, Germany

    Christoph Quix

  3. University of Coimbra, Coimbra, Portugal

    Jorge Bernardino

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Frisoni, G., Moro, G. (2021). Phenomena Explanation from Text: Unsupervised Learning of Interpretable and Statistically Significant Knowledge. In: Hammoudi, S., Quix, C., Bernardino, J. (eds) Data Management Technologies and Applications. DATA 2020. Communications in Computer and Information Science, vol 1446. Springer, Cham. https://doi.org/10.1007/978-3-030-83014-4_14

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