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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 Hits 1 – 20 of 43 1 Predicting lexical complexity in English texts: the Complex 2.0 dataset Shardlow, Matthew; Evans, Richard; Zampieri, Marcos. - : Springer, 2022 BASE Show details 2 Predicting Lexical Complexity in English Texts ... Shardlow, Matthew; Evans, Richard; Zampieri, Marcos. - : arXiv, 2021 BASE Show details 3 SemEval-2021 Task 1: Lexical Complexity Prediction ... Shardlow, Matthew; Evans, Richard; Paetzold, Gustavo Henrique. - : arXiv, 2021 BASE Show details 4 SemEval-2021 Task 1: Lexical Complexity Prediction ... Shardlow, Matthew; Evans, Richard; Paetzold, Gustavo. - : Zenodo, 2021 BASE Show details 5 SemEval-2021 Task 1: Lexical Complexity Prediction ... Shardlow, Matthew; Evans, Richard; Paetzold, Gustavo. - : Zenodo, 2021 BASE Show details 6 SemEval-2021 Task 1: Lexical Complexity Prediction ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Evans, Richard; Paetzold, Gustavo. - : Underline Science Inc., 2021 BASE Show details 7 SemEval-2021 task 1: Lexical complexity prediction Evans, Richard; Zampieri, Marcos; Shardlow, Matthew. - : Association for Computational Linguistics, 2021 BASE Show details 8 A sequence labelling approach for automatic analysis of ello: tagging pronouns, antecedents, and connective phrases Parodi, Giovanni; Evans, Richard; Ha, Le An. - : Springer, 2021 BASE Show details 9 Sentence Simplification for Text Processing Evans, Richard. - : University of Wolverhampton, 2020 BASE Show details 10 Identifying Signs of Syntactic Complexity for Rule-Based Sentence Simplification Evans, Richard; Orasan, Constantin In: 25 ; 1 ; 69 ; 119 (2018) BASE Show details 11 Classifying referential and non-referential it using gaze Yaneva, Victoria; Ha, Le An; Evans, Richard... In: 4896 ; 4901 (2018) BASE Show details 12 WLV at SemEval-2018 task 3: Dissecting tweets in search of irony Rohanian, Omid; Taslimipoor, Shiva; Evans, Richard. - : Association for Computational Linguistics, 2018 BASE Show details 13 Intelligent Natural Language Processing: Trends and Applications Orăsan, Constantin; Evans, Richard; Mitkov, Ruslan. - : Springer, 2017 BASE Show details 14 Combining Multiple Corpora for Readability Assessment for People with Cognitive Disabilities Yaneva, Victoria; Orăsan, Constantin; Evans, Richard. - : Association for Computational Linguistics, 2017 BASE Show details 15 Six good predictors of autistic reading comprehension Yaneva, Victoria; Evans, Richard. - : INCOMA Ltd, 2015 BASE Show details 16 An evaluation of syntactic simplification rules for people with autism Dornescu, Iustin; Orasan, Constantin; Evans, Richard. - : Association for Computational Linguistics, 2014 BASE Show details 17 Assessing Conformance of Manually Simplified Corpora with User Requirements: the Case of Autistic Readers Evans, Richard; Stajner, Sanja.; Dornescu, Iustin. - : Association for Computational Linguistics and Dublin City University, 2014 BASE Show details 18 Assessing conformance of manually simplified corpora with user requirements: the case of autistic readers Štajner, Sanja; Evans, Richard; Dornescu, Iustin. - : Association for Computational Linguistics, 2014 BASE Show details 19 Comparing methods for the syntactic simplification of sentences in information extraction Evans, Richard J. In: LLC. - Oxford : Oxford Univ. Press 26 (2011) 4, 371-388 BLLDB OLC Linguistik Show details 20 Comparing methods for the syntactic simplification of sentences in information extraction Evans, Richard J.. - : Oxford University Press, 2011 Abstract: This article describes research aimed at improving the accuracy of an information extraction (IE) system by treating coordinate structures systematically. Commas, coordinating conjunctions, and adjacent comma–conjunction pairs are considered to be potential indicators of coordination in natural language. A recursive algorithm is implemented which converts sentences containing classified potential coordinators into sequences of simple sentences. Several approaches to the classification of potential coordinators are presented, one exploiting memory-based learning, another exploiting the publicly available Stanford parser, and a hybrid approach that classifies commas and conjunctions using the former system and comma–conjunction pairs using the latter. The article describes the initial set of features developed for exploitation by the memory-based classifier and presents optimization of that classifier. A baseline system is also described. The sentence simplification module was exploited by an IE system. With regard to the automatic classifiers that form the basis for simplification, comparative evaluation demonstrated that IE can be performed with greatest accuracy when exploiting the hybrid classifier. It also demonstrated that a simple baseline classifier induces improved accuracy when compared to systems that ignore the presence of coordinate structures in input sentences. The article presents an analysis of the errors made by the different sentence simplification modules and the IE system that exploits them. Directions for future research are suggested. Keyword: Original Articles URL: https://doi.org/10.1093/llc/fqr034 http://llc.oxfordjournals.org/cgi/content/short/26/4/371 BASE Hide details Page: 1 2 3

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