UNT-SimpRank: Systems for Lexical Simplification Ranking
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Lexical SimplificationSimilar Papers 제목 키워드 기반
A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification
Current lexical simplification approaches rely heavily on heuristics and corpus level features that do not always align with human judgment. We create a human-rated word-complexity lexicon of 15,000 English words and pro…
Lexical SimplificationRALS: Resources and Baselines for Romanian Automatic Lexical Simplification
We introduce the first dataset that jointly covers both lexical complexity prediction (LCP) annotations and lexical simplification (LS) for Romanian, along with a comparison of lexical simplification approaches. We propo…
Text SimplificationLexical Simplification with the Deep Structured Similarity Model
We explore the application of a Deep Structured Similarity Model (DSSM) to ranking in lexical simplification. Our results show that the DSSM can effectively capture fine-grained features to perform semantic matching when…
Image CaptioningLearning Word EmbeddingsLexical SimplificationMachine Translation+3Lexical 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+1Personalized Substitution Ranking for Lexical Simplification
A lexical simplification (LS) system substitutes difficult words in a text with simpler ones to make it easier for the user to understand. In the typical LS pipeline, the Substitution Ranking step determines the best sub…
Lexical Simplification