Richard Evans

Also published as: R. Evans


2021

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SemEval-2021 Task 1: Lexical Complexity Prediction
Matthew Shardlow | Richard Evans | Gustavo Henrique Paetzold | Marcos Zampieri
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

This paper presents the results and main findings of SemEval-2021 Task 1 - Lexical Complexity Prediction. We provided participants with an augmented version of the CompLex Corpus (Shardlow et al. 2020). CompLex is an English multi-domain corpus in which words and multi-word expressions (MWEs) were annotated with respect to their complexity using a five point Likert scale. SemEval-2021 Task 1 featured two Sub-tasks: Sub-task 1 focused on single words and Sub-task 2 focused on MWEs. The competition attracted 198 teams in total, of which 54 teams submitted official runs on the test data to Sub-task 1 and 37 to Sub-task 2.

2019

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Sentence Simplification for Semantic Role Labelling and Information Extraction
Richard Evans | Constantin Orasan
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

In this paper, we report on the extrinsic evaluation of an automatic sentence simplification method with respect to two NLP tasks: semantic role labelling (SRL) and information extraction (IE). The paper begins with our observation of challenges in the intrinsic evaluation of sentence simplification systems, which motivates the use of extrinsic evaluation of these systems with respect to other NLP tasks. We describe the two NLP systems and the test data used in the extrinsic evaluation, and present arguments and evidence motivating the integration of a sentence simplification step as a means of improving the accuracy of these systems. Our evaluation reveals that their performance is improved by the simplification step: the SRL system is better able to assign semantic roles to the majority of the arguments of verbs and the IE system is better able to identify fillers for all IE template slots.

2018

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WLV at SemEval-2018 Task 3: Dissecting Tweets in Search of Irony
Omid Rohanian | Shiva Taslimipoor | Richard Evans | Ruslan Mitkov
Proceedings of The 12th International Workshop on Semantic Evaluation

This paper describes the systems submitted to SemEval 2018 Task 3 “Irony detection in English tweets” for both subtasks A and B. The first system leveraging a combination of sentiment, distributional semantic, and text surface features is ranked third among 44 teams according to the official leaderboard of the subtask A. The second system with slightly different representation of the features ranked ninth in subtask B. We present a method that entails decomposing tweets into separate parts. Searching for contrast within the constituents of a tweet is an integral part of our system. We embrace an extensive definition of contrast which leads to a vast coverage in detecting ironic content.

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Classifying Referential and Non-referential It Using Gaze
Victoria Yaneva | Le An Ha | Richard Evans | Ruslan Mitkov
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

When processing a text, humans and machines must disambiguate between different uses of the pronoun it, including non-referential, nominal anaphoric or clause anaphoric ones. In this paper we use eye-tracking data to learn how humans perform this disambiguation and use this knowledge to improve the automatic classification of it. We show that by using gaze data and a POS-tagger we are able to significantly outperform a common baseline and classify between three categories of it with an accuracy comparable to that of linguistic-based approaches. In addition, the discriminatory power of specific gaze features informs the way humans process the pronoun, which, to the best of our knowledge, has not been explored using data from a natural reading task.

2017

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Combining Multiple Corpora for Readability Assessment for People with Cognitive Disabilities
Victoria Yaneva | Constantin Orăsan | Richard Evans | Omid Rohanian
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

Given the lack of large user-evaluated corpora in disability-related NLP research (e.g. text simplification or readability assessment for people with cognitive disabilities), the question of choosing suitable training data for NLP models is not straightforward. The use of large generic corpora may be problematic because such data may not reflect the needs of the target population. The use of the available user-evaluated corpora may be problematic because these datasets are not large enough to be used as training data. In this paper we explore a third approach, in which a large generic corpus is combined with a smaller population-specific corpus to train a classifier which is evaluated using two sets of unseen user-evaluated data. One of these sets, the ASD Comprehension corpus, is developed for the purposes of this study and made freely available. We explore the effects of the size and type of the training data used on the performance of the classifiers, and the effects of the type of the unseen test datasets on the classification performance.

2015

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Six Good Predictors of Autistic Text Comprehension
Victoria Yaneva | Richard Evans
Proceedings of the International Conference Recent Advances in Natural Language Processing

2014

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An evaluation of syntactic simplification rules for people with autism
Richard Evans | Constantin Orăsan | Iustin Dornescu
Proceedings of the 3rd Workshop on Predicting and Improving Text Readability for Target Reader Populations (PITR)

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Relative clause extraction for syntactic simplification
Iustin Dornescu | Richard Evans | Constantin Orăsan
Proceedings of the Workshop on Automatic Text Simplification - Methods and Applications in the Multilingual Society (ATS-MA 2014)

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Assessing Conformance of Manually Simplified Corpora with User Requirements: the Case of Autistic Readers
Sanja Štajner | Richard Evans | Iustin Dornescu
Proceedings of the Workshop on Automatic Text Simplification - Methods and Applications in the Multilingual Society (ATS-MA 2014)

2013

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A Tagging Approach to Identify Complex Constituents for Text Simplification
Iustin Dornescu | Richard Evans | Constantin Orăsan
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013

2004

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Categorizing Web Pages as a Preprocessing Step for Information Extraction
Viktor Pekar | Richard Evans | Ruslan Mitkov
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

2002

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Bilingual alignment of anaphoric expressions
R. Muñoz | R. Mitkov | M. Palomar | J. Peral | R. Evans | L. Moreno | C. Orasan | M. Saiz-Noeda | A. Ferrández | C. Barbu | P. Martínez-Barco | A. Suárez
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

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A corpus based investigation of morphological disagreement in anaphoric relations
Cătălina Barbu | Richard Evans | Ruslan Mitkov
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

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Assessing the difficulty of finding people in texts
Constantin Orăsan | Richard Evans
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

2001

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Learning to identify animate references
Constantin Orasan | Richard Evans
Proceedings of the ACL 2001 Workshop on Computational Natural Language Learning (ConLL)