Teaching NLP/CL through games

Halteren, H. van

doi:10.3115/1118108.1118109

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…After splitting the data, the ensemble classifiers (RF + GBM) were trained on the train data. In this experiment, (RF + GBM) were implemented using NLTK and Scikit-learn library [53]. (RF + GBM) were tuned with different hyper-parameters as shown in Tab.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Decision Level Fusion Using Hybrid Classifier for Mental Disease Classification

Ahmad¹,

Wahid²,

Hamid³

et al. 2022

Computers, Materials &Amp; Continua

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Mental health signifies the emotional, social, and psychological well-being of a person. It also affects the way of thinking, feeling, and situation handling of a person. Stable mental health helps in working with full potential in all stages of life from childhood to adulthood therefore it is of significant importance to find out the onset of the mental disease in order to maintain balance in life. Mental health problems are rising globally and constituting a burden on healthcare systems. Early diagnosis can help the professionals in the treatment that may lead to complications if they remain untreated. The machine learning models are highly prevalent for medical data analysis, disease diagnosis, and psychiatric nosology. This research addresses the challenge of detecting six major psychological disorders, namely, Anxiety, Bipolar Disorder, Conversion Disorder, Depression, Mental Retardation and Schizophrenia. These challenges are mined by applying decision level fusion of supervised machine learning algorithms. A dataset was collected from a clinical psychologist consisting of 1771 observations that we used for training and testing the models. Furthermore, to reduce the impact of a conflicting decision, a voting scheme Shrewd Probing Prediction Model (SPPM) is introduced to get output from ensemble model of Random Forest and Gradient Boosting Machine (RF + GBM). This research provides an intuitive solution for mental disorder analysis among different target class labels or groups. A framework is proposed for determining the mental health problem of patients using observations of medical experts. The framework consists of an ensemble model based on RF and GBM with a novel SPPM technique. This proposed decision level fusion approach by combining RF + GBM with SPPM-MIN significantly improves the performance in terms of Accuracy, Precision, Recall, and F1-score with 71\%, 73\%, 71\% and 71\% respectively. This framework seems suitable in the case of huge and more diverse multiclass datasets. Furthermore, three vector spaces based on TF-IDF (unigram, 5042 CMC, 2022, vol.72, no.3 bi-gram, and tri-gram) are also tested on the machine learning models and the proposed model.

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Section: Experiments and Resultsmentioning

confidence: 99%

Decision Level Fusion Using Hybrid Classifier for Mental Disease Classification

Ahmad¹,

Wahid²,

Hamid³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

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“…Since the workshop here presented is the first activity of this kind in the Italian context, we took inspiration from games and problems such as those outlined in Radev and Pustejovsky (2013) and used for the North American Computational Linguistics Olympiads, similar to the ones described in Van Halteren (2002) and Iomdin et al (2013). Particularly, we were inspired by the institution of (Computational) Linguistic Olympiads in making our workshop a problem-solving game with different activities, each related to a different aspect of computational language processing.…”

Section: Genesis and Goalsmentioning

confidence: 99%

Teaching NLP with Bracelets and Restaurant Menus: An Interactive Workshop for Italian Students

Pannitto

Busso

Combei

et al. 2021

Proceedings of the Fifth Workshop on Teaching NLP

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Although Natural Language Processing (NLP) is at the core of many tools young people use in their everyday life, high school curricula (in Italy) do not include any computational linguistics education. This lack of exposure makes the use of such tools less responsible than it could be and makes choosing computational linguistics as a university degree unlikely. To raise awareness, curiosity, and longer-term interest in young people, we have developed an interactive workshop designed to illustrate the basic principles of NLP and computational linguistics to high school Italian students aged between 13 and 18 years. The workshop takes the form of a game in which participants play the role of machines needing to solve some of the most common problems a computer faces in understanding language: from voice recognition to Markov chains to syntactic parsing. Participants are guided through the workshop with the help of instructors, who present the activities and explain core concepts from computational linguistics. The workshop was presented at numerous outlets in Italy between 2019 and 2021, both face-to-face and online.

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“…Since the workshop here presented is the first activity of this kind in the Italian context, we took inspiration from games and problems such as those outlined in and used for the North American Computational Linguistics Olympiads, similar to the ones described in Van Halteren (2002) and Iomdin et al (2013). Particularly, we were inspired by the institution of (Computational) Linguistic Olympiads in making our workshop a problem-solving game with different activities, each related to a different aspect of computational language processing.…”

Section: Genesis and Goalsmentioning

confidence: 99%

Proceedings of the Fifth Workshop on Teaching NLP

2021

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The ongoing COVID-19 pandemic has brought online education to the forefront of pedagogical discussions. To make this increased interest sustainable in a postpandemic era, online courses must be built on strong pedagogical foundations. With a long history of pedagogic research, there are many principles, frameworks, and models available to help teachers in doing so. These models cover different teaching perspectives, such as constructive alignment, feedback, and the learning environment. In this paper, we discuss how we designed and implemented our online Natural Language Processing (NLP) course following constructive alignment and adhering to the pedagogical principles of LTU. By examining our course and analyzing student evaluation forms, we show that we have met our goal and successfully delivered the course. Furthermore, we discuss the additional benefits resulting from the current mode of delivery, including the increased reusability of course content and increased potential for collaboration between universities. Lastly, we also discuss where we can and will further improve the current course design.

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Teaching NLP/CL through games

Cited by 4 publications

References 6 publications

Decision Level Fusion Using Hybrid Classifier for Mental Disease Classification

Decision Level Fusion Using Hybrid Classifier for Mental Disease Classification

Teaching NLP with Bracelets and Restaurant Menus: An Interactive Workshop for Italian Students

Proceedings of the Fifth Workshop on Teaching NLP

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