Using WordNet Similarity and Translations to Create Synsets for Ontology-Based Vietnamese WordNet

Nguyen, Lap Trung; Huynh, Khai Minh

doi:10.1109/iiai-aai.2016.217

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…A study based on semantic method has been introduced by Bhagat et al [188] using Wordnet ontology [189] as well as some 'Similarity Measures' to reduce the high number of extracted features. 'Path Length Measure' has been chosen as the most suitable algorithm for determining the similarity measures.…”

Section: K: Supervised Systems Discussing Performance Of Different Almentioning

confidence: 99%

A Comprehensive Survey for Intelligent Spam Email Detection

et al. 2019

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The tremendously growing problem of phishing e-mail, also known as spam including spear phishing or spam borne malware, has demanded a need for reliable intelligent anti-spam e-mail filters. This survey paper describes a focused literature survey of Artificial Intelligence (AI) and Machine Learning (ML) methods for intelligent spam email detection, which we believe can help in developing appropriate countermeasures. In this paper, we considered 4 parts in the email's structure that can be used for intelligent analysis: (A) Headers Provide Routing Information, contain mail transfer agents (MTA) that provide information like email and IP address of each sender and recipient of where the email originated and what stopovers, and final destination. (B) The SMTP Envelope, containing mail exchangers' identification, originating source and destination domains\users. (C) First part of SMTP Data, containing information like from, to, date, subject-appearing in most email clients (D) Second part of SMTP Data, containing email body including text content, and attachment. Based on the number the relevance of an emerging intelligent method, papers representing each method were identified, read, and summarized. Insightful findings, challenges and research problems are disclosed in this paper. This comprehensive survey paves the way for future research endeavors addressing theoretical and empirical aspects related to intelligent spam email detection. INDEX TERMS Machine learning, phishing attack, spear phishing, spam detection, spam email, spam filtering.

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Section: K: Supervised Systems Discussing Performance Of Different Almentioning

confidence: 99%

A Comprehensive Survey for Intelligent Spam Email Detection

et al. 2019

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“…Nouns, verbs, adjectives, and adverbs are divided into different groups named [10]. The process of computing the ontology and the WordNet similarity uses a library Java API for WordNet Similarity [11]. This process supports the semantic similarity between keyword and present similarity score with a percentage [12].…”

Section: Word Similarity On Wordnetmentioning

confidence: 99%

Classification of Social Media Messages Posted at the Time of Disaster

Kemavuthanon

Uchida

2020

IFIP Advances in Information and Communication Technology

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Nowadays, social media is one of the essential sharing of information and proliferation tools because it spreads text messages, news, pictures, or videos in real-time. During the disaster, Japanese people use social media to exchange real-time information for their social interaction. Twitter is the most popular tool that has been used for disaster response in Japan. Even though many disaster systems have been created and used for disaster mitigation in Japan, most of them are assumed to be used by the Japanese in the Japanese language. From this problem, this study focuses on the way to create a disaster response system and community service to help, collect, and extract information on social media to help disaster mitigation becomes more important. This paper aims to investigate the tweets by focusing on noun keywords during the Osaka North Earthquake on 18 June 2018 with a data set of more than 9,000,000 tweets. The process presented classify social media messages by using ontology, word similarity, frequency of keyword, and evaluate results of natural language processing. We organize the messages into 15 categories and used as the classification algorithms with machine learning features of the count of each category word in the sentences. The result tweets were statistically compared with the keyword in each category to classify the content and collecting disaster information and using the result to build the analysis system.

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Using WordNet Similarity and Translations to Create Synsets for Ontology-Based Vietnamese WordNet

Cited by 2 publications

References 13 publications

A Comprehensive Survey for Intelligent Spam Email Detection

A Comprehensive Survey for Intelligent Spam Email Detection

Classification of Social Media Messages Posted at the Time of Disaster

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