Killian Levacher


2018

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The ADELE Corpus of Dyadic Social Text Conversations:Dialog Act Annotation with ISO 24617-2
Emer Gilmartin | Christian Saam | Brendan Spillane | Maria O’Reilly | Ketong Su | Arturo Calvo | Loredana Cerrato | Killian Levacher | Nick Campbell | Vincent Wade
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 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.

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Towards Automated Extraction of Business Constraints from Unstructured Regulatory Text
Rahul Nair | Killian Levacher | Martin Stephenson
Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations

Large organizations spend considerable resources in reviewing regulations and ensuring that their business processes are compliant with the law. To make compliance workflows more efficient and responsive, we present a system for machine-driven annotations of legal documents. A set of natural language processing pipelines are designed and aimed at addressing some key questions in this domain: (a) is this (new) regulation relevant for me? (b) what set of requirements does this law impose?, and (c) what is the regulatory intent of a law? The system is currently undergoing user trials within our organization.

2017

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Annotation of greeting, introduction, and leavetaking in dialogues
Emer Gilmartin | Brendan Spillane | Maria O’Reilly | Christian Saam | Ketong Su | Benjamin R. Cowan | Killian Levacher | Arturo Calvo Devesa | Lodana Cerrato | Nick Campbell | Vincent Wade
Proceedings of the 13th Joint ISO-ACL Workshop on Interoperable Semantic Annotation (ISA-13)