Mention du « Best Paper Award » de la conférence JURIX2020 attribuée à un article rédigé par des membres du Laboratoire

Hannes Westermann, Jaromír Šavelka, Vern R. Walker, Kevin D. Ashley et Karim Benyekhlef — directeur du Laboratoire de cyberjustice — ont coécrit un article intitulé « Sentence Embeddings and High-Speed Similarity Search for Fast Computer Assisted Annotation of Legal Documents » qui a reçu la mention du Best Paper lors de la conférence Jurix 2020. 

Annonce Jurix Best Paper 2020

Avant-propos

Human-performed annotation of sentences in legal documents is an important prerequisite to many machine learning based systems supporting legal tasks. Typically, the annotation is done sequentially, sentence by sentence, which is often time consuming and, hence, expensive. In this paper, we introduce a proof-of-concept system for annotating sentences “laterally.” The approach is based on the observation that sentences that are similar in meaning often have the same label in terms of a particular type system. We use this observation in allowing annotators to quickly view and annotate sentences that are semantically similar to a given sentence, across an entire corpus of documents. Here, we present the interface of the system and empirically evaluate the approach. The experiments show that lateral annotation has the potential to make the annotation process quicker and more consistent.

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Ce contenu a été mis à jour le 16 décembre 2020 à 12 h 24 min.