Text Simplification from Professionally Produced Corpora
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Lexical SimplificationMachine TranslationText SimplificationSimilar Papers 제목 키워드 기반
Lexical Simplification with Neural Ranking
We present a new Lexical Simplification approach that exploits Neural Networks to learn substitutions from the Newsela corpus - a large set of professionally produced simplifications. We extract candidate substitutions b…
Complex Word IdentificationInformation RetrievalLexical Simplificationregression+1Simple is not Enough: Document-level Text Simplification using Readability and Coherence
In this paper, we present the SimDoc system, a simplification model considering simplicity, readability, and discourse aspects, such as coherence. In the past decade, the progress of the Text Simplification (TS) field ha…
SentenceText SimplificationText Simplification of College Admissions Instructions: A Professionally Simplified and Verified Corpus
Access to higher education is critical for minority populations and emergent bilingual students. However, the language used by higher education institutions to communicate with prospective students is often too complex; …
SentenceText SimplificationPatient-friendly Clinical Notes: Towards a new Text Simplification Dataset
Automatic text simplification can help patients to better understand their own clinical notes. A major hurdle for the development of clinical text simplification methods is the lack of high quality resources. We report o…
Text SimplificationDSim, a Danish Parallel Corpus for Text Simplification
We present DSim, a new sentence aligned Danish monolingual parallel corpus extracted from 3701 pairs of news telegrams and corresponding professionally simplified short news articles. The corpus is intended for building …
ArticlesMachine TranslationSentenceText Generation+1