Francesca Bonin


2020

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HBCP Corpus: A New Resource for the Analysis of Behavioural Change Intervention Reports
Francesca Bonin | Martin Gleize | Ailbhe Finnerty | Candice Moore | Charles Jochim | Emma Norris | Yufang Hou | Alison J. Wright | Debasis Ganguly | Emily Hayes | Silje Zink | Alessandra Pascale | Pol Mac Aonghusa | Susan Michie
Proceedings of the 12th Language Resources and Evaluation Conference

Due to the fast pace at which research reports in behaviour change are published, researchers, consultants and policymakers would benefit from more automatic ways to process these reports. Automatic extraction of the reports’ intervention content, population, settings and their results etc. are essential in synthesising and summarising the literature. However, to the best of our knowledge, no unique resource exists at the moment to facilitate this synthesis. In this paper, we describe the construction of a corpus of published behaviour change intervention evaluation reports aimed at smoking cessation. We also describe and release the annotation of 57 entities, that can be used as an off-the-shelf data resource for tasks such as entity recognition, etc. Both the corpus and the annotation dataset are being made available to the community.

2019

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A Summarization System for Scientific Documents
Shai Erera | Michal Shmueli-Scheuer | Guy Feigenblat | Ora Peled Nakash | Odellia Boni | Haggai Roitman | Doron Cohen | Bar Weiner | Yosi Mass | Or Rivlin | Guy Lev | Achiya Jerbi | Jonathan Herzig | Yufang Hou | Charles Jochim | Martin Gleize | Francesca Bonin | Francesca Bonin | David Konopnicki
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

We present a novel system providing summaries for Computer Science publications. Through a qualitative user study, we identified the most valuable scenarios for discovery, exploration and understanding of scientific documents. Based on these findings, we built a system that retrieves and summarizes scientific documents for a given information need, either in form of a free-text query or by choosing categorized values such as scientific tasks, datasets and more. Our system ingested 270,000 papers, and its summarization module aims to generate concise yet detailed summaries. We validated our approach with human experts.

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A Summarization System for Scientific Documents
Shai Erera | Michal Shmueli-Scheuer | Guy Feigenblat | Ora Peled Nakash | Odellia Boni | Haggai Roitman | Doron Cohen | Bar Weiner | Yosi Mass | Or Rivlin | Guy Lev | Achiya Jerbi | Jonathan Herzig | Yufang Hou | Charles Jochim | Martin Gleize | Francesca Bonin | Francesca Bonin | David Konopnicki
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

We present a novel system providing summaries for Computer Science publications. Through a qualitative user study, we identified the most valuable scenarios for discovery, exploration and understanding of scientific documents. Based on these findings, we built a system that retrieves and summarizes scientific documents for a given information need, either in form of a free-text query or by choosing categorized values such as scientific tasks, datasets and more. Our system ingested 270,000 papers, and its summarization module aims to generate concise yet detailed summaries. We validated our approach with human experts.

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Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction
Yufang Hou | Charles Jochim | Martin Gleize | Francesca Bonin | Debasis Ganguly
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

While the fast-paced inception of novel tasks and new datasets helps foster active research in a community towards interesting directions, keeping track of the abundance of research activity in different areas on different datasets is likely to become increasingly difficult. The community could greatly benefit from an automatic system able to summarize scientific results, e.g., in the form of a leaderboard. In this paper we build two datasets and develop a framework (TDMS-IE) aimed at automatically extracting task, dataset, metric and score from NLP papers, towards the automatic construction of leaderboards. Experiments show that our model outperforms several baselines by a large margin. Our model is a first step towards automatic leaderboard construction, e.g., in the NLP domain.

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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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SLIDE - a Sentiment Lexicon of Common Idioms
Charles Jochim | Francesca Bonin | Roy Bar-Haim | Noam Slonim
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Towards a music-language mapping
Michele Berlingerio | Francesca Bonin
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.

2014

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A Context-Aware NLP Approach For Noteworthiness Detection in Cellphone Conversations
Francesca Bonin | Jose San Pedro | Nuria Oliver
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

2013

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Laugher and Topic Transition in Multiparty Conversation
Emer Gilmartin | Francesca Bonin | Carl Vogel | Nick Campbell
Proceedings of the SIGDIAL 2013 Conference

2012

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Annotating Archaeological Texts: An Example of Domain-Specific Annotation in the Humanities
Francesca Bonin | Fabio Cavulli | Aronne Noriller | Massimo Poesio | Egon W. Stemle
Proceedings of the Sixth Linguistic Annotation Workshop

2010

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Contrastive Filtering of Domain-Specific Multi-Word Terms from Different Types of Corpora
Francesca Bonin | Felice Dell’Orletta | Giulia Venturi | Simonetta Montemagni
Proceedings of the 2010 Workshop on Multiword Expressions: from Theory to Applications

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A Contrastive Approach to Multi-word Extraction from Domain-specific Corpora
Francesca Bonin | Felice Dell’Orletta | Simonetta Montemagni | Giulia Venturi
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

In this paper, we present a novel approach to multi-word terminology extraction combining a well-known automatic term recognition approach, the C--NC value method, with a contrastive ranking technique, aimed at refining obtained results either by filtering noise due to common words or by discerning between semantically different types of terms within heterogeneous terminologies. Differently from other contrastive methods proposed in the literature that focus on single terms to overcome the multi-word terms' sparsity problem, the proposed contrastive function is able to handle variation in low frequency events by directly operating on pre-selected multi-word terms. This methodology has been tested in two case studies carried out in the History of Art and Legal domains. Evaluation of achieved results showed that the proposed two--stage approach improves significantly multi--word term extraction results. In particular, for what concerns the legal domain it provides an answer to a well-known problem in the semi--automatic construction of legal ontologies, namely that of singling out law terms from terms of the specific domain being regulated.