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Register a new userWord Complexity is in the Eye of the Beholder
كلمة التعقيد في عين الناظر
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تعد التعقيد المعجمي فكرة ذاتية عالية، ومع ذلك، غالبا ما يتم إهمال هذا العامل في التبسيط المعجمي وأنظمة قابلية القراءة التي تستخدم نهج "كل واحد يناسب الجميع".في هذه الورقة، يمكننا التحقيق في الجوانب التي تساهم في فكرة التعقيد المعجمي في مجموعات مختلفة من القراء، مع التركيز على المتحدثين الأصليين وغير الأصليين في اللغة الإنجليزية، وكيف تغير مفهوم التعقيد اعتمادا على مستوى الكفاءة للقارئ غير الأصليينوبعدلتسهيل استنساخ نهجنا وتعزيز المزيد من البحث في هذه الجوانب، نطلق سرد بيانات من الكلمات المعقدة المشروح من قبل القراء مع خلفيات مختلفة.
Lexical complexity is a highly subjective notion, yet this factor is often neglected in lexical simplification and readability systems which use a ''one-size-fits-all'' approach. In this paper, we investigate which aspects contribute to the notion of lexical complexity in various groups of readers, focusing on native and non-native speakers of English, and how the notion of complexity changes depending on the proficiency level of a non-native reader. To facilitate reproducibility of our approach and foster further research into these aspects, we release a dataset of complex words annotated by readers with different backgrounds.
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Objective: Health care providers in emergency settings encounter emotional issues
in treating traumatic dental injuries TDIs which may have a significant negative impact on
the quality of life. Assessing OHRQoL of children with TDIs, in emergency,
promote a
shift from traditional dental management to a more supportive care that focuses on a
person’s social and emotional status and physical functioning in which appropriate health
care and optimal outcomes can be obtained. The objective of this study is to investigate the
impact of TDIs on the quality of life of 7to16-year-old Syrian school children and to test
whether treatment of TDI will improve their OHRQoL.
Basic research design:A case-control study was carried out involving 147children
aged 7 to 16 years attending the Department of Pediatric Dentistry in Damascus
University. Andreasen classification was adopted for TDI diagnosis. CPQ11-14 instrument
was utilized to assess OHRQoL.
Results: Findings of this study revealed significant differences in OHRQoL items
before and after treatment of participating children. There were no statistically significant
differences between healthy children and those who were treated with TDI in regard to the
overall OHRQoL (P > 0.05).
Conclusions: OHRQoL was significantly improved in children with TDIs after
treatment. Oral symptoms, functional limitations, emotional and social well-being were
similar to healthy children. Health professionals in Syria should put efforts to make war
less damaging for children and their parents. Dentistry during the crisis goes beyond filling
and drilling of injured teeth to a moral commitment within a supportive and caring context.
This paper revisits feature engineering approaches for predicting the complexity level of English words in a particular context using regression techniques. Our best submission to the Lexical Complexity Prediction (LCP) shared task was ranked 3rd out
of 48 systems for sub-task 1 and achieved Pearson correlation coefficients of 0.779 and 0.809 for single words and multi-word expressions respectively. The conclusion is that a combination of lexical, contextual and semantic features can still produce strong baselines when compared against human judgement.
The adoption of Transformer-based models in natural language processing (NLP) has led to great success using a massive number of parameters. However, due to deployment constraints in edge devices, there has been a rising interest in the compression o
f these models to improve their inference time and memory footprint. This paper presents a novel loss objective to compress token embeddings in the Transformer-based models by leveraging an AutoEncoder architecture. More specifically, we emphasize the importance of the direction of compressed embeddings with respect to original uncompressed embeddings. The proposed method is task-agnostic and does not require further language modeling pre-training. Our method significantly outperforms the commonly used SVD-based matrix-factorization approach in terms of initial language model Perplexity. Moreover, we evaluate our proposed approach over SQuAD v1.1 dataset and several downstream tasks from the GLUE benchmark, where we also outperform the baseline in most scenarios. Our code is public.
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process or
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laser beam in visible range, which requires to apply the special goggles, as well
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In this paper, we propose a method of fusing sentence information and word frequency information for the SemEval 2021 Task 1-Lexical Complexity Prediction (LCP) shared task. In our system, the sentence information comes from the RoBERTa model, and th
e word frequency information comes from the Tf-Idf algorithm. Use Inception block as a shared layer to learn sentence and word frequency information We described the implementation of our best system and discussed our methods and experiments in the task. The shared task is divided into two sub-tasks. The goal of the two sub-tasks is to predict the complexity of a predetermined word. The shared task is divided into two subtasks. The goal of the two subtasks is to predict the complexity of a predetermined word. The evaluation index of the task is the Pearson correlation coefficient. Our best performance system has Pearson correlation coefficients of 0.7434 and 0.8000 in the single-token subtask test set and the multi-token subtask test set, respectively.
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