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dc.contributor.authorEvans, Richard
dc.contributor.authorOrasan, Constantin
dc.date.accessioned2018-10-03T14:54:29Z
dc.date.available2018-10-03T14:54:29Z
dc.date.issued2018-10-31
dc.identifier.citationEVANS, R. and ORĂSAN, C. (2019) “Identifying signs of syntactic complexity for rule-based sentence simplification,” Natural Language Engineering. Cambridge University Press, 25(1), pp. 69–119. doi: 10.1017/S1351324918000384.
dc.identifier.issn1351-3249
dc.identifier.doi10.1017/S1351324918000384
dc.identifier.urihttp://hdl.handle.net/2436/621752
dc.description.abstractThis article presents a new method to automatically simplify English sentences. The approach is designed to reduce the number of compound clauses and nominally bound relative clauses in input sentences. The article provides an overview of a corpus annotated with information about various explicit signs of syntactic complexity and describes the two major components of a sentence simplification method that works by exploiting information on the signs occurring in the sentences of a text. The first component is a sign tagger which automatically classifies signs in accordance with the annotation scheme used to annotate the corpus. The second component is an iterative rule-based sentence transformation tool. Exploiting the sign tagger in conjunction with other NLP components, the sentence transformation tool automatically rewrites long sentences containing compound clauses and nominally bound relative clauses as sequences of shorter single-clause sentences. Evaluation of the different components reveals acceptable performance in rewriting sentences containing compound clauses but less accuracy when rewriting sentences containing nominally bound relative clauses. A detailed error analysis revealed that the major sources of error include inaccurate sign tagging, the relatively limited coverage of the rules used to rewrite sentences, and an inability to discriminate between various subtypes of clause coordination. Despite this, the system performed well in comparison with two baselines. This finding was reinforced by automatic estimations of the readability of system output and by surveys of readers’ opinions about the accuracy, accessibility, and meaning of this output.
dc.formatapplication/PDF
dc.language.isoen
dc.publisherCambridge University Press
dc.relation.urlhttps://www.cambridge.org/core/journals/natural-language-engineering
dc.subjectText Simplification
dc.subjectSentence Simplification
dc.subjectSyntactic Analysis
dc.titleIdentifying Signs of Syntactic Complexity for Rule-Based Sentence Simplification
dc.typeJournal article
dc.identifier.eissn1469-8110
dc.identifier.journalNatural Language Engineering
dc.date.accepted2018-09-17
rioxxterms.funderJisc
rioxxterms.identifier.projectUOW25092018RE
rioxxterms.versionAM
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
rioxxterms.licenseref.startdate2019-04-30
dc.source.volume25
dc.source.issue1
dc.source.beginpage69
dc.source.endpage119
refterms.dateFCD2018-09-25T11:28:31Z
refterms.versionFCDAM


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