Word sense disambiguation using hybrid swarm intelligence approach

Al-Saiagh, Wafaa; Tiun, Sabrina; Al-Saffar, Ahmed; Awang, Suryanti; Al-Khaleefa, Ahmed Salih

doi:10.1371/journal.pone.0208695

Cited by 20 publications

(6 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The filter approach evaluates feature importance using feature relevance metrics such as Chi-Square, Mutual Information, and Odds Ratio. The wrapper, on the other hand, is claimed to be a classifierdependent approach that selects features based on the ML's predictive performance of a classifier on a given subset [3]. It takes time because of the learning algorithm process in feature selection.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Hybrid Feature Selection Technique Using Term Frequency-Inverse Document Frequency and Support Vector Machine-Recursive Feature Elimination for Sentiment Classification

Nafis

Awang

2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

Sentiment classification is increasingly used to automatically identify a positive or negative sentiment in a text review. In classification, feature selection had always been a critical and challenging problem. Most of the related feature selection for sentiment classification techniques unable to overcome problems of evaluating the significant features that will reduce the classification performance. This paper proposes an enhanced hybrid feature selection technique to improve the sentiment classification based on machine learning approaches. First, two customer review datasets namely Sentiment Labelled and large IMDB are retrieved and pre-processed. Next, the proposed feature selection technique which is the hybridization of Term Frequency-Inverse Document Frequency (TF-IDF) and Supports Vector Machine (SVM-RFE) is developed and tested on these two datasets. TF-IDF aims to measure features importance. The SVM-RFE iteratively evaluates and ranks the features. For sentiment classification, only the ktop features from the ranked features will be used. Finally, the Support Vector Machine (SVM) classifier is deployed to observe the performance of the proposed technique. The performance is measured using accuracy, precision, recall, and F-measure. The experimental results show promising performances with 84.54% to 89.56% in the measurements especially from the large IMDB dataset. The results also outperformed other related techniques in certain datasets. Consequently, the proposed technique able to reduce from 19.25% to 70.5% of the features to be classified. This reduction rate is significant in optimally utilizing the computational resources while maintaining the efficiency of the classification performance.

show abstract

Section: Introductionmentioning

confidence: 99%

An Enhanced Hybrid Feature Selection Technique Using Term Frequency-Inverse Document Frequency and Support Vector Machine-Recursive Feature Elimination for Sentiment Classification

Nafis

Awang

2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…Applications to speech recognition and analysis, data gathering, and some other NLP tasks are mostly found in the POS tagged (annotated) language corpora. Including machine translation, natural language processing requires the disambiguation of word sense [18]. The linguist differentiates linguistic ambiguity from structural.…”

Section:  Issn: 2502-4752mentioning

confidence: 99%

Neural machine translation for Sanskrit to Malayalam using morphology and evolutionary word sense disambiguation

Chingamtotattil

Gopikakumar

2022

IJEECS

View full text Add to dashboard Cite

Neural machine translation (NMT) is a fast-evolving MT paradigm and showed good results, particularly in large training data circumstances, for several language pairs. In this paper, we have utilized Sanskrit to Malayalam language pair neural machines translation. The attention-based mechanism for the development of the machine translation system was particularly exploited. Word sense disambiguation (WSD) is a phenomenon for disambiguating the text to let the machine infer the proper definition of the particular word. Sequential deep learning approaches such as a recurrent neural network (RNN), a gated recurrent unit (GRU), a long short term memory (LSTM), and a bi-directional LSTM (BLSTM) were used to analyze the tagged data. By adding morphological elements and evolutionary word sense disambiguation, the suggested common character-word embedding-based NMT model gives a BLEU score of 38.58 which was higher than the others.

show abstract

“…Moreover, no policy or classification packet was applied to deploy the QoS technologies to obtain a comprehensive comparison of network performances under different queuing scenarios. Applying multiple techniques, such as first-in, first-out (FIFO), CBWFQ, low-latency queuing, class-based weighted random early detection, explicit congestion notification, and link fragmentation and interleaving (LFI), can result in high network performance levels [17]. The more advanced the method, the better the quality of the transmission [18].…”

Section: Online Sequential Extreme Learning Machinementioning

confidence: 99%

Quality of Service (QoS) Management for Local Area Network (LAN) Using Traffic Policy Technique to Secure Congestion

et al. 2020

View full text Add to dashboard Cite

This study presents the proposed testbed implementation for the Advanced Technology Training Center (ADTEC) Batu Pahat, one of Malaysia’s industrial training institutes. The objectives of this study are to discover the issues regarding network congestion, propose a suitable method to overcome such issues, and generate output data for the comparison of the results before and after the proposed implementation. The internet is directly connected to internet service providers (ISPs), which neither impose any rule nor filter the traffic components; all connections comply on the basis of the base effort services provided by the ISP. The congestion problem has been raised several times and the information technology (IT) department has been receiving complaints about poor and sometimes intermittent internet connection. Such issues provide some ideas for a possible solution because the end client is a human resource core business. In addition, budget constraints contribute to this problem. After a comprehensive review of related literature and discussion with experts, the implementation of quality of service through add-on rules, such as traffic policing on network traffic, was proposed. The proposed testbed also classified the traffic. Results show that the proposed testbed is stable. After the implementation of the generated solution, the IT department no longer receives any complaints, and thus fulfills the goal of having zero internet connection issues.

show abstract

Word sense disambiguation using hybrid swarm intelligence approach

Cited by 20 publications

References 50 publications

An Enhanced Hybrid Feature Selection Technique Using Term Frequency-Inverse Document Frequency and Support Vector Machine-Recursive Feature Elimination for Sentiment Classification

An Enhanced Hybrid Feature Selection Technique Using Term Frequency-Inverse Document Frequency and Support Vector Machine-Recursive Feature Elimination for Sentiment Classification

Neural machine translation for Sanskrit to Malayalam using morphology and evolutionary word sense disambiguation

Quality of Service (QoS) Management for Local Area Network (LAN) Using Traffic Policy Technique to Secure Congestion

Contact Info

Product

Resources

About