Significance of Global Vectors Representation in Protein Sequences Analysis

George, Anon; Ganesh, H B Barathi; Kumar, M. Anand; Soman, K P

doi:10.1007/978-3-030-04061-1_27

Cited by 8 publications

(5 citation statements)

References 11 publications

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“…This is done by constructing a large matrix of co-occurrence information, with the content of information on how frequent each “word” stored in rows appear in the column. It is a type of unsupervised technique used to obtain a meaningful vector that corresponds to individual words in a corpus [ 65 ]. In this model, different words repel against each other, where similar words cluster together.…”

Section: Methodsmentioning

confidence: 99%

Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach

2021

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Sarcasm is the main reason behind the faulty classification of tweets. It brings a challenge in natural language processing (NLP) as it hampers the method of finding people’s actual sentiment. Various feature engineering techniques are being investigated for the automatic detection of sarcasm. However, most related techniques have always concentrated only on the content-based features in sarcastic expression, leaving the contextual information in isolation. This leads to a loss of the semantics of words in the sarcastic expression. Another drawback is the sparsity of the training data. Due to the word limit of microblog, the feature vector’s values for each sample constructed by BoW produces null features. To address the above-named problems, a Multi-feature Fusion Framework is proposed using two classification stages. The first stage classification is constructed with the lexical feature only, extracted using the BoW technique, and trained using five standard classifiers, including SVM, DT, KNN, LR, and RF, to predict the sarcastic tendency. In stage two, the constructed lexical sarcastic tendency feature is fused with eight other proposed features for modelling a context to obtain a final prediction. The effectiveness of the developed framework is tested with various experimental analysis to obtain classifiers’ performance. The evaluation shows that our constructed classification models based on the developed novel feature fusion obtained results with a precision of 0.947 using a Random Forest classifier. Finally, the obtained results were compared with the results of three baseline approaches. The comparison outcome shows the significance of the proposed framework.

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Section: Methodsmentioning

confidence: 99%

Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach

2021

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“…Glove word embedding features [17] are extracted from the pre-processed reviews after removing all the emoticons. Glove is a powerful word embedding algorithm.…”

Section: Content Featuresmentioning

confidence: 99%

Cross Domain Hybrid Feature Fusion based Sarcastic Opinion Recognition Over E-Commerce Reviews Using Adversarial Transfer Learning

2023

IJIES

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Sarcasm detection is a challenge in review based e-commerce product recommendation systems. Failure to detect sarcasm causes assignment of directionally opposite ratings to reviews and faults the recommendation systems. This work proposes a cross domain hybrid feature fusion based sarcastic opinion recognition system. Hybrid deep feature integrating content and context is formed and feature vector volume is expanded using cross domain adversarial learning. The hybrid features are used for sarcasm detection in two combinations. First combination is hybrid features with bi-directional long short term memory (LSTM)and second is hybrid features with traditional machine learning classifiers. Hybrid feature with bi-directional LSTM is able to capture temporal relation between sentences of the review for better identification of sarcasm. Traditional machine learning classifiers trained with these hybrid features are also able to provide higher accuracy of sarcastic detection in reviews. The effectiveness of the proposed cross domain hybrid feature fusion features is tested against various experimental setups with Bi-directional LSTM and traditional machine learning classifiers. The proposed solution is able to achieve peak accuracy of 95% in classifying e-commerce reviews.

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“…This algorithm has many applications in different fields such as text similarity ( Kenter & De Rijke, 2015 ), node representations ( Brochier, Guille & Velcin, 2019 ), emotion detection ( George, Barathi Ganesh & Soman, 2018 ) and many others. This algorithm found its way in many biomedicine such as finding semantic similarity ( Muneeb, Sahu & Anand, 2015 ), extracting Adverse Drug Reactions (ADR) ( Lin et al, 2015 ), and analyzing protein sequences ( George et al, 2019 ).…”

Section: Related Workmentioning

confidence: 99%

An automated method to enrich consumer health vocabularies using GloVe word embeddings and an auxiliary lexical resource

Ibrahim¹,

Gauch²,

Salman³

et al. 2021

PeerJ Computer Science

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Background Clear language makes communication easier between any two parties. A layman may have difficulty communicating with a professional due to not understanding the specialized terms common to the domain. In healthcare, it is rare to find a layman knowledgeable in medical terminology which can lead to poor understanding of their condition and/or treatment. To bridge this gap, several professional vocabularies and ontologies have been created to map laymen medical terms to professional medical terms and vice versa. Objective Many of the presented vocabularies are built manually or semi-automatically requiring large investments of time and human effort and consequently the slow growth of these vocabularies. In this paper, we present an automatic method to enrich laymen’s vocabularies that has the benefit of being able to be applied to vocabularies in any domain. Methods Our entirely automatic approach uses machine learning, specifically Global Vectors for Word Embeddings (GloVe), on a corpus collected from a social media healthcare platform to extend and enhance consumer health vocabularies. Our approach further improves the consumer health vocabularies by incorporating synonyms and hyponyms from the WordNet ontology. The basic GloVe and our novel algorithms incorporating WordNet were evaluated using two laymen datasets from the National Library of Medicine (NLM), Open-Access Consumer Health Vocabulary (OAC CHV) and MedlinePlus Healthcare Vocabulary. Results The results show that GloVe was able to find new laymen terms with an F-score of 48.44%. Furthermore, our enhanced GloVe approach outperformed basic GloVe with an average F-score of 61%, a relative improvement of 25%. Furthermore, the enhanced GloVe showed a statistical significance over the two ground truth datasets with P < 0.001. Conclusions This paper presents an automatic approach to enrich consumer health vocabularies using the GloVe word embeddings and an auxiliary lexical source, WordNet. Our approach was evaluated used healthcare text downloaded from MedHelp.org, a healthcare social media platform using two standard laymen vocabularies, OAC CHV, and MedlinePlus. We used the WordNet ontology to expand the healthcare corpus by including synonyms, hyponyms, and hypernyms for each layman term occurrence in the corpus. Given a seed term selected from a concept in the ontology, we measured our algorithms’ ability to automatically extract synonyms for those terms that appeared in the ground truth concept. We found that enhanced GloVe outperformed GloVe with a relative improvement of 25% in the F-score.

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Significance of Global Vectors Representation in Protein Sequences Analysis

Cited by 8 publications

References 11 publications

Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach

Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach

Cross Domain Hybrid Feature Fusion based Sarcastic Opinion Recognition Over E-Commerce Reviews Using Adversarial Transfer Learning

An automated method to enrich consumer health vocabularies using GloVe word embeddings and an auxiliary lexical resource

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