Hegler Tissot


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C3SL at SemEval-2021 Task 1: Predicting Lexical Complexity of Words in Specific Contexts with Sentence Embeddings
Raul Almeida | Hegler Tissot | Marcos Didonet Del Fabro
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

We present our approach to predicting lexical complexity of words in specific contexts, as entered LCP Shared Task 1 at SemEval 2021. The approach consists of separating sentences into smaller chunks, embedding them with Sent2Vec, and reducing the embeddings into a simpler vector used as input to a neural network, the latter for predicting the complexity of words and expressions. Results show that the pre-trained sentence embeddings are not able to capture lexical complexity from the language when applied in cross-domain applications.


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Relative and Incomplete Time Expression Anchoring for Clinical Text
Louise Dupuis | Nicol Bergou | Hegler Tissot | Sumithra Velupillai
Proceedings of the 3rd Clinical Natural Language Processing Workshop

Extracting and modeling temporal information in clinical text is an important element for developing timelines and disease trajectories. Time information in written text varies in preciseness and explicitness, posing challenges for NLP approaches that aim to accurately anchor temporal information on a timeline. Relative and incomplete time expressions (RI-Timexes) are expressions that require additional information for their temporal anchor to be resolved, but few studies have addressed this challenge specifically. In this study, we aimed to reproduce and verify a classification approach for identifying anchor dates and relations in clinical text, and propose a novel relation classification approach for this task.


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Annotating Temporal Information in Clinical Notes for Timeline Reconstruction: Towards the Definition of Calendar Expressions
Natalia Viani | Hegler Tissot | Ariane Bernardino | Sumithra Velupillai
Proceedings of the 18th BioNLP Workshop and Shared Task

To automatically analyse complex trajectory information enclosed in clinical text (e.g. timing of symptoms, duration of treatment), it is important to understand the related temporal aspects, anchoring each event on an absolute point in time. In the clinical domain, few temporally annotated corpora are currently available. Moreover, underlying annotation schemas - which mainly rely on the TimeML standard - are not necessarily easily applicable for applications such as patient timeline reconstruction. In this work, we investigated how temporal information is documented in clinical text by annotating a corpus of medical reports with time expressions (TIMEXes), based on TimeML. The developed corpus is available to the NLP community. Starting from our annotations, we analysed the suitability of the TimeML TIMEX schema for capturing timeline information, identifying challenges and possible solutions. As a result, we propose a novel annotation schema that could be useful for timeline reconstruction: CALendar EXpression (CALEX).


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UFPRSheffield: Contrasting Rule-based and Support Vector Machine Approaches to Time Expression Identification in Clinical TempEval
Hegler Tissot | Genevieve Gorrell | Angus Roberts | Leon Derczynski | Marcos Didonet Del Fabro
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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Analysis of Temporal Expressions Annotated in Clinical Notes
Hegler Tissot | Angus Roberts | Leon Derczynski | Genevieve Gorrell | Marcus Didonet Del Fabro
Proceedings of the 11th Joint ACL-ISO Workshop on Interoperable Semantic Annotation (ISA-11)