Léa Deleris


2019

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Extracting Factual Min/Max Age Information from Clinical Trial Studies
Yufang Hou | Debasis Ganguly | Léa Deleris | Francesca Bonin
Proceedings of the 2nd Clinical Natural Language Processing Workshop

Population age information is an essential characteristic of clinical trials. In this paper, we focus on extracting minimum and maximum (min/max) age values for the study samples from clinical research articles. Specifically, we investigate the use of a neural network model for question answering to address this information extraction task. The min/max age QA model is trained on the massive structured clinical study records from ClinicalTrials.gov. For each article, based on multiple min and max age values extracted from the QA model, we predict both actual min/max age values for the study samples and filter out non-factual age expressions. Our system improves the results over (i) a passage retrieval based IE system and (ii) a CRF-based system by a large margin when evaluated on an annotated dataset consisting of 50 research papers on smoking cessation.

2018

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Know Who Your Friends Are: Understanding Social Connections from Unstructured Text
Léa Deleris | Francesca Bonin | Elizabeth Daly | Stéphane Deparis | Yufang Hou | Charles Jochim | Yassine Lassoued | Killian Levacher
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

Having an understanding of interpersonal relationships is helpful in many contexts. Our system seeks to assist humans with that task, using textual information (e.g., case notes, speech transcripts, posts, books) as input. Specifically, our system first extracts qualitative and quantitative information elements (which we call signals) about interactions among persons, aggregates those to provide a condensed view of relationships and then enables users to explore all facets of the resulting social (multi-)graph through a visual interface.

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Decision Conversations Decoded
Léa Deleris | Debasis Ganguly | Killian Levacher | Martin Stephenson | Francesca Bonin
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

We describe the vision and current version of a Natural Language Processing system aimed at group decision making facilitation. Borrowing from the scientific field of Decision Analysis, its essential role is to identify alternatives and criteria associated with a given decision, to keep track of who proposed them and of the expressed sentiment towards them. Based on this information, the system can help identify agreement and dissent or recommend an alternative. Overall, it seeks to help a group reach a decision in a natural yet auditable fashion.

2017

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Named Entity Recognition in the Medical Domain with Constrained CRF Models
Charles Jochim | Léa Deleris
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

This paper investigates how to improve performance on information extraction tasks by constraining and sequencing CRF-based approaches. We consider two different relation extraction tasks, both from the medical literature: dependence relations and probability statements. We explore whether adding constraints can lead to an improvement over standard CRF decoding. Results on our relation extraction tasks are promising, showing significant increases in performance from both (i) adding constraints to post-process the output of a baseline CRF, which captures “domain knowledge”, and (ii) further allowing flexibility in the application of those constraints by leveraging a binary classifier as a pre-processing step.