Sophie Chesney


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Multi-word Entity Classification in a Highly Multilingual Environment
Sophie Chesney | Guillaume Jacquet | Ralf Steinberger | Jakub Piskorski
Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017)

This paper describes an approach for the classification of millions of existing multi-word entities (MWEntities), such as organisation or event names, into thirteen category types, based only on the tokens they contain. In order to classify our very large in-house collection of multilingual MWEntities into an application-oriented set of entity categories, we trained and tested distantly-supervised classifiers in 43 languages based on MWEntities extracted from BabelNet. The best-performing classifier was the multi-class SVM using a TF.IDF-weighted data representation. Interestingly, one unique classifier trained on a mix of all languages consistently performed better than classifiers trained for individual languages, reaching an averaged F1-value of 88.8%. In this paper, we present the training and test data, including a human evaluation of its accuracy, describe the methods used to train the classifiers, and discuss the results.

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Incongruent Headlines: Yet Another Way to Mislead Your Readers
Sophie Chesney | Maria Liakata | Massimo Poesio | Matthew Purver
Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism

This paper discusses the problem of incongruent headlines: those which do not accurately represent the information contained in the article with which they occur. We emphasise that this phenomenon should be considered separately from recognised problematic headline types such as clickbait and sensationalism, arguing that existing natural language processing (NLP) methods applied to these related concepts are not appropriate for the automatic detection of headline incongruence, as an analysis beyond stylistic traits is necessary. We therefore suggest a number of alternative methodologies that may be appropriate to the task at hand as a foundation for future work in this area. In addition, we provide an analysis of existing data sets which are related to this work, and motivate the need for a novel data set in this domain.


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Towards a Semantic Model for Textual Entailment Annotation
Assaf Toledo | Stavroula Alexandropoulou | Sophie Chesney | Sophia Katrenko | Heid Klockmann | Pepjin Kokke | Benno Kruit | Yoad Winter
Linguistic Issues in Language Technology, Volume 9, 2014 - Perspectives on Semantic Representations for Textual Inference

We introduce a new formal semantic model for annotating textual entailments that describes restrictive, intersective, and appositive modification. The model contains a formally defined interpreted lexicon, which specifies the inventory of symbols and the supported semantic operators, and an informally defined annotation scheme that instructs annotators in which way to bind words and constructions from a given pair of premise and hypothesis to the interpreted lexicon. We explore the applicability of the proposed model to the Recognizing Textual Entailment (RTE) 1–4 corpora and describe a first-stage annotation scheme on which we based the manual annotation work. The constructions we annotated were found to occur in 80.65% of the entailments in RTE 1–4 and were annotated with cross-annotator agreement of 68% on average. The annotated parts of the RTE corpora are publicly available for further research.

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Annotating by Proving using SemAnTE
Assaf Toledo | Stavroula Alexandropoulou | Sophie Chesney | Robert Grimm | Pepijn Kokke | Benno Kruit | Kyriaki Neophytou | Antony Nguyen | Yoad Winter
Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics