Efficient Estimation of Nepali Word Representations in Vector Space

Bhatta, Janardan; Shrestha, Dipesh; Nepal, Santosh; Pandey, Saurav; Koirala, S

doi:10.3126/jiee.v3i1.34327

Cited by 62 publications

(12 citation statements)

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“…We trained the neural network on a corpus of 1.1 million words sourced from 22 individual blogs and online forums ( Multimedia Appendix 1 ). We used the skip-gram negative sampling variant of the word2vec neural network algorithm described by Mikolov et al [ 9 ] to discover community words and phrases for disease symptoms. Briefly, the neural network model was trained to predict context words that appear in close proximity with symptom keywords in the corpus text.…”

Section: Methodsmentioning

confidence: 99%

“…We address this limitation with a novel approach based on a neural network, specifically a word embedding [ 9 ], to identify words and phrases that patients with chronic obstructive pulmonary disease (COPD) use to describe their experiences of living with the disease. Unlike traditional neural network approaches, a word embedding is not trained on any specific set of scientific keywords [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

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A Neural Network Approach for Understanding Patient Experiences of Chronic Obstructive Pulmonary Disease (COPD): Retrospective, Cross-sectional Study of Social Media Content

Freeman¹,

Rodriguez‐Esteban²,

Gottowik³

et al. 2021

JMIR Med Inform

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Background The abundance of online content contributed by patients is a rich source of insight about the lived experience of disease. Patients share disease experiences with other members of the patient and caregiver community and do so using their own lexicon of words and phrases. This lexicon and the topics that are communicated using words and phrases belonging to the lexicon help us better understand disease burden. Insights from social media may ultimately guide clinical development in ways that ensure that future treatments are fit for purpose from the patient’s perspective. Objective We sought insights into the patient experience of chronic obstructive pulmonary disease (COPD) by analyzing a substantial corpus of social media content. The corpus was sufficiently large to make manual review and manual coding all but impossible to perform in a consistent and systematic fashion. Advanced analytics were applied to the corpus content in the search for associations between symptoms and impacts across the entire text corpus. Methods We conducted a retrospective, cross-sectional study of 5663 posts sourced from open blogs and online forum posts published by COPD patients between February 2016 and August 2019. We applied a novel neural network approach to identify a lexicon of community words and phrases used by patients to describe their symptoms. We used this lexicon to explore the relationship between COPD symptoms and disease-related impacts. Results We identified a diverse lexicon of community words and phrases for COPD symptoms, including gasping, wheezy, mucus-y, and muck. These symptoms were mentioned in association with specific words and phrases for disease impact such as frightening, breathing discomfort, and difficulty exercising. Furthermore, we found an association between mucus hypersecretion and moderate disease severity, which distinguished mucus from the other main COPD symptoms, namely breathlessness and cough. Conclusions We demonstrated the potential of neural networks and advanced analytics to gain patient-focused insights about how each distinct COPD symptom contributes to the burden of chronic and acute respiratory illness. Using a neural network approach, we identified words and phrases for COPD symptoms that were specific to the patient community. Identifying patterns in the association between symptoms and impacts deepened our understanding of the patient experience of COPD. This approach can be readily applied to other disease areas.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Neural Network Approach for Understanding Patient Experiences of Chronic Obstructive Pulmonary Disease (COPD): Retrospective, Cross-sectional Study of Social Media Content

Freeman¹,

Rodriguez‐Esteban²,

Gottowik³

et al. 2021

JMIR Med Inform

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“…Embedding technique has been developed and immediately obtained a considerable attention and success in ML community in general, and in natural language processing [58] and recommendation systems [59] in particular. This technique allows condensing the dimensions of the input features, thereby it contributes to stabilising the learning process.…”

Section: Stream Representationmentioning

confidence: 99%

An intelligent radar signal classification and deinterleaving method with unified residual recurrent neural network

Al‐Malahi

Farhan

Feng

et al. 2023

IET Radar Sonar & Navi

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The accuracy of radar emitter signal sorting nowadays deteriorates due to the high flexibility and complexity of modern radar pulse streams and the density of crowded electromagnetic environment. In modern radar signal sorting based on pulse repetition interval, conventional methods usually fail to achieve acceptable accuracy and lack stable performance for two main reasons: (1) Conventional methods require a large number of pulses in the stream, which is not practical in many applications. (2) These methods are sensitive to pulse loss and random noise pulses. These two reasons are the main problem that is addressed in this paper. Our proposed model is a machine learning architecture called Unified Residual Recurrent Neural Network (URRNN). In this architecture, residual neural network and recurrent neural network are combined and modified to alleviate the forementioned shortcomings of traditional approaches and enhance the model performance in both classification and deinterleaving tasks. This aim is achieved due to the fact that URRNN extracts both spatial and temporal features, which means more information about processed stream that is exploited to enhance model performance. Three different architectural combinations of URRNN, which show high accuracy and reasonable processing time, are built and trained. The structural and functional description are provided for each architecture. Simulation demonstrates high accuracy and reliable performance of the proposed methods in different circumstances. The results are compared with the results obtained by other conventional machine learning techniques.

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“…With the emergence of word vector technology that converts words into numerical vectors, word meaning measurement becomes possible. The main derived word vector generation models include Word2vec [15], G1oVe [16], ELMo [17], and BERT [18]. The most commonly used are the Word2vec model and the BERT model.…”

Section: The Research Statusmentioning

confidence: 99%

Hot Keyword Extraction of Sci-tech Periodicals Based on the Improved BERT Model

2022

KSII TIIS

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With the development of the economy and the improvement of living standards, the hot issues in the subject area have become the main research direction, and the mining of the hot issues in the subject currently has problems such as a large amount of data and a complex algorithm structure. Therefore, in response to this problem, this study proposes a method for extracting hot keywords in scientific journals based on the improved BERT model.It can also provide reference for researchers,and the research method improves the overall similarity measure of the ensemble,introducing compound keyword word density, combining word segmentation, word sense set distance, and density clustering to construct an improved BERT framework, establish a composite keyword heat analysis model based on I-BERT framework.Taking the 14420 articles published in 21 kinds of social science management periodicals collected by CNKI(China National Knowledge Infrastructure) in 2017-2019 as the experimental data, the superiority of the proposed method is verified by the data of word spacing, class spacing, extraction accuracy and recall of hot keywords. In the experimental process of this research, it can be found that the method proposed in this paper has a higher accuracy than other methods in extracting hot keywords, which can ensure the timeliness and accuracy of scientific journals in capturing hot topics in the discipline, and finally pass Use information technology to master popular key words.

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Efficient Estimation of Nepali Word Representations in Vector Space

Cited by 62 publications

References 1 publication

A Neural Network Approach for Understanding Patient Experiences of Chronic Obstructive Pulmonary Disease (COPD): Retrospective, Cross-sectional Study of Social Media Content

A Neural Network Approach for Understanding Patient Experiences of Chronic Obstructive Pulmonary Disease (COPD): Retrospective, Cross-sectional Study of Social Media Content

An intelligent radar signal classification and deinterleaving method with unified residual recurrent neural network

Hot Keyword Extraction of Sci-tech Periodicals Based on the Improved BERT Model

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