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- Biomedical named entity recognition (1)
- Computer Science Education; K-12; Outreach; Computing CampsIn-clusive; Female Student; Perceptions (1)
- Contextualized word embeddings (1)
- Deep learning (1)
- Introductory Programming; CS1; Intervention; Growth Mindset; Clustering; K-Means; Self-Efficacy; Replication Study (1)
Articles 1 - 4 of 4
Full-Text Articles in Computer Engineering
Developing An Inclusive K-12 Outreach Model, Karen Nolan, Roisin Faherty, Keith Quille, Brett A. Becker, Susan Bergin
Developing An Inclusive K-12 Outreach Model, Karen Nolan, Roisin Faherty, Keith Quille, Brett A. Becker, Susan Bergin
Conference Papers
This paper outlines the longitudinal development of a K-12 outreachmodel, to promote Computer Science in Ireland. Over a three-yearperiod, it has been piloted to just under 9700 K-12 students fromalmost every county in Ireland. The model consists of a two-hourcamp that introduces students to a range of Computer Sciencetopics: addressing computing perceptions, introduction to codingand exploration of computational thinking. The model incorporateson-site school delivery and is available at no cost to any interestedschool across Ireland. The pilot study so far collected over 3400surveys (pre- and post-outreach delivery).Schools from all over Ireland self-selected to participate, includ-ing male only, female only and …
Named Entity Recognition In Spanish Biomedical Literature: Short Review And Bert Model, Liliya Akhtyamova
Named Entity Recognition In Spanish Biomedical Literature: Short Review And Bert Model, Liliya Akhtyamova
Conference Papers
Named Entity Recognition (NER) is the rst step for knowledge acquisition when we deal with an unknown corpus of texts. Having received these entities, we have an opportunity to form parameters space and to solve problems of text mining as concept normalization, speech recognition, etc. The recent advances in NER are related to the technology of word embeddings, which transforms text to the form being effective for Deep Learning. In the paper, we show how NER detects pharmacological substances, compounds, and proteins in the dataset obtained from the Spanish Clinical Case Corpus (SPACCC). To achieve this goal, we use contextualized …
Lm-Based Word Embeddings Improve Biomedical Named Entity Recognition: A Detailed Analysis, Liliya Akhtyamova, John Cardiff
Lm-Based Word Embeddings Improve Biomedical Named Entity Recognition: A Detailed Analysis, Liliya Akhtyamova, John Cardiff
Conference Papers
Recent studies have shown that contextualized word embeddings outperform other types of embeddings on a variety of tasks. However, there is little research done to evaluate their effectiveness in the biomedical domain under multi-task settings. We derive the contextualized word embeddings from the Flair framework and apply them to the task of biomedical NER on 5 benchmark datasets, yielding major improvements over the baseline and achieving competitive results over the current best systems. We analyze the sources of these improvements, reporting model performances over different combinations of word embeddings, and fine-tuning and casing modes.
Promoting A Growth Mindset In Cs1: Does One Size Fit All? A Pilot Study, Keith Quille, Susan Bergin
Promoting A Growth Mindset In Cs1: Does One Size Fit All? A Pilot Study, Keith Quille, Susan Bergin
Conference Papers
This paper describes a pilot intervention conducted in CS1, in the
academic year of 2016-2017. The intervention was based on the
work of Dweck, promoting a growth Mindset in an effort to in-
crease performance in introductory programming. The study also
examined data from a previous year (as a control group) to compare
and contrast the results. Multiple factors related to programming
performance were recorded with the control and treatment group,
which were measured at multiple intervals throughout the course,
to monitor changes as the pilot intervention was implemented.
This study found a significant increase in programming perfor-
mance when …