Complex Word Identification as a Sequence Labelling Task

Gooding, Sian; Kochmar, Ekaterina

doi:10.18653/v1/p19-1109

Cited by 37 publications

(37 citation statements)

References 16 publications

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“…We refer to this model as HLR+. The features include word complexity scores estimated by a pre-trained model [6], mean concreteness scores and percent known based on human judgements [2], SUBTLEX word frequencies [18] and user ids.…”

Section: Hlr With Linguistic/psychological Features (Hlr+)mentioning

confidence: 99%

Adaptive Forgetting Curves for Spaced Repetition Language Learning

Zaidi

Caines

Moore

et al. 2020

Lecture Notes in Computer Science

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The forgetting curve has been extensively explored by psychologists, educationalists and cognitive scientists alike. In the context of Intelligent Tutoring Systems, modelling the forgetting curve for each user and knowledge component (e.g. vocabulary word) should enable us to develop optimal revision strategies that counteract memory decay and ensure long-term retention. In this study we explore a variety of forgetting curve models incorporating psychological and linguistic features, and we use these models to predict the probability of word recall by learners of English as a second language. We evaluate the impact of the models and their features using data from an online vocabulary teaching platform and find that word complexity is a highly informative feature which may be successfully learned by a neural network model.

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Section: Hlr With Linguistic/psychological Features (Hlr+)mentioning

confidence: 99%

Adaptive Forgetting Curves for Spaced Repetition Language Learning

Zaidi

Caines

Moore

et al. 2020

Lecture Notes in Computer Science

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“…The strategy of annotated carried out, software with free tools were built, which was called EIL (Language Research Environment), where the texts of the VYTEDU corpus were loaded. The annotated process takes into consideration the research work proposed by [13,9,16,17] on the lexical simplification project for Spanish and the lexical simplification for Czech, respectively.…”

Section: Fig 1 Vytedu Corpus Textsmentioning

confidence: 99%

“…These research papers have attracted attention in recent years, with the advent of deep learning approaches [16] and multilingual challenges [17], which contributes to the evaluation of words labeled as severe, such as specialized words, common lexicon words, slang, English words, acronyms, among others. Given these terms, students had a hard time understanding; in some cases, they ignored its meaning or had some idea or notion of it.…”

Section: B Analysis Of Corpus Vytedu-cw By Carreramentioning

confidence: 99%

Barriers in Reading Comprehension of University Students: Analysis of the Complicated Words Annotated in the VYTEDU-CW Corpus

Ortiz-Zambrano

Montejo-Ráez

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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Students often require a greater understanding of the lexicon that teachers use when dictating an assignment in class or in written texts as supporting material. Identifying and labelling difficult words has allowed us to examine the problem. A sample of students from the University of Guayaquil (Ecuador) was taken to experiment in a corpus of video transcripts that correspond to the different careers. After performing the analysis of the tagged words, the conclusions reached by other research papers in lexical simplification are confirmed and corroborates the recommendations of the Easy Reading guide prepared by Inclusion Europe in 2009. The investigation determined that the words labeled as difficult were specialized words, common lexical words, slang, English words, acronyms, among others. It was difficult for students to understand its meaning; in some cases, they either ignored its definition or just had the wrong idea of the lexicon. This work aims to be a contribution to future research in the area of lexical simplification applied to the development of solutions for detecting difficult words in the university academic field. Also, the type of complex expressions identified in the VYTEDU-CW corpus were characterized by the software, which enriches this resource while opening the possibility to organize a workshop where to promote research in the detection of difficult words to the Spanish. The support to validate these solutions is available to the scientific community.

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“…The proposed SEQ model (Gooding and Kochmar, 2019) has a number of additional advantages: it takes context into account, helps avoid the necessity of extensive feature engineering relying on word embeddings as the only input information at run time, and generalises well across all three datasets. To further assess generalisability of the model, we test it on CEFR-LS, as well as BENCHLS for consistency (see Table 2).…”

Section: Test Setmentioning

confidence: 99%

Recursive Context-Aware Lexical Simplification

Gooding¹,

Kochmar²

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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This paper presents a novel architecture for recursive context-aware lexical simplification, REC-LS, that is capable of (1) making use of the wider context when detecting the words in need of simplification and suggesting alternatives, and (2) taking previous simplification steps into account. We show that our system outputs lexical simplifications that are grammatically correct and semantically appropriate, and outperforms the current state-of-theart systems in lexical simplification.

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Complex Word Identification as a Sequence Labelling Task

Cited by 37 publications

References 16 publications

Adaptive Forgetting Curves for Spaced Repetition Language Learning

Adaptive Forgetting Curves for Spaced Repetition Language Learning

Barriers in Reading Comprehension of University Students: Analysis of the Complicated Words Annotated in the VYTEDU-CW Corpus

Recursive Context-Aware Lexical Simplification

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