Exposing Students to New Terminologies While Collecting Browsing Search Data (Best Technical Paper)

Zammit, Omar; Smith, Serengul; Windridge, David; Raffaele, Clifford De

doi:10.1007/978-3-030-63799-6_1

Cited by 2 publications

(20 citation statements)

References 26 publications

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“…Although the Keyphrase assignment process suggested in Zammit et al (2020) can retrieve effective keyphrases, the experiments in this research showed that it can be improved by taking into consideration the part-of-speech tags of the keyphrase words while performing keyphrase ranking. Having higher accuracy will lead to more effective keyphrases that novice students can use to find relevant information about a topic.…”

Section: Introductionmentioning

confidence: 88%

“…This is because it exposes students to new terminologies pertinent to a topic being searched by the student thus reducing the dependency on background knowledge. Since the function generates various keyphrases, to avoid the Curse of dimensionality (Fan & Fan, 2008) and not end up with a large amount of keyphrases, in Zammit et al (2020) a Ranking mechanism was suggested. This mechanism selects the top 10 most pertinent keyphrases generated from the keyphrases extracted by the function.…”

Section: Framework Overviewmentioning

confidence: 99%

“…The main focus of this research is to reduce the dependency of having prior domain knowledge about a topic by suggesting better results than the framework proposed by Zammit et al (2020). To achieve this, we improved the Ranking mechanism accuracy by taking into consideration the part-of-speech tags within a keyphrase and included a Reporting server that among other functionality it allows keyphrase collaboration between students.…”

Section: Framework Overviewmentioning

confidence: 99%

“…To address this issue, in this research we implemented an existing framework that was proposed in Zammit et al (2020) that among other components, has a Keyphrase assignment mechanism that generates keyphrases pertinent to the domain being searched by a student. This framework aims to expose students to new terminologies and, therefore, assist students who lack background knowledge about the domain being researched.…”

Section: Introductionmentioning

confidence: 99%

“…A framework that collects URLs visited by a student and using various similarity analysis finds previously searched keyphrases by the student was proposed in Zammit et al (2019). This study was extended in Zammit et al (2020) with a framework that amongst others includes Keyphrase assignment functionality and can suggest new terminologies pertinent to the domain being researched by the student. Keyphrase assignment is done using a Keyphrase extraction function that can generate keyphrases of various lengths and a Ranking mechanism that is based on the keyphrases occurrence.…”

Section: Introductionmentioning

confidence: 99%

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Reducing the dependency of having prior domain knowledge for effective online information retrieval

et al. 2022

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Sometimes Internet users struggle to find what they are looking for on the Internet due to information overload. Search engines intend to identify documents related to a given keyphrase on the Internet and provide suggestions. Having some background knowledge about a topic or a domain will help in building effective search keyphrases that will lead to accurate results in information retrieval. This is further pronounced among students that rely on the internet to learn about a new topic. Students might not have the required background knowledge to build effective keyphrases and find what they are looking for. In this research, we are addressing this problem, and aim to help students find relevant information online. This research furthers existing literature by enhancing information retrieval frameworks through keyphrase assignment, aiming to expose students to new terminologies, therefore reducing the dependency of having background knowledge about the domain under study. We evaluated this framework and identified how it can be enhanced to suggest more effective search keyphrases. Our proposed suggestion is to introduce a keyphrase Ranking Mechanism that will improve the keyphrase assignment part of the framework by taking into consideration the part‐of‐speech of the generated keyphrases. To evaluate the proposed approach, various data sets were downloaded and processed. The results obtained showed that our proposed approach produces more effective keyphrases than the existing framework.

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Section: Introductionmentioning

confidence: 88%

Section: Framework Overviewmentioning

confidence: 99%

Section: Framework Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reducing the dependency of having prior domain knowledge for effective online information retrieval

et al. 2022

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A Novel Hybrid Deep Learning IChOA-CNN-LSTM Model for Modality-Enriched and Multilingual Emotion Recognition in Social Media A Novel Hybrid Deep Learning IChOA-CNN-LSTM Model

Geethanjali,

VALARMATHI

2024

Preprint

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In the rapidly evolving field of artificial intelligence, the importance of multimodal sentiment analysis has never been more evident, especially amid the ongoing COVID-19 pandemic. Our research addresses the critical imperative to comprehend public sentiment across the multifaceted dimensions of this crisis by integrating data from various modalities such as text, images, audio, and videos sourced from platforms like Twitter. Conventional methods, which primarily focus on text analysis, often fall short in capturing the nuanced intricacies of emotional states, necessitating a more comprehensive approach. To tackle this challenge, our proposed framework introduces a novel hybrid model, IChOA-CNN-LSTM, which leverages Convolutional Neural Networks (CNNs) for precise image feature extraction, Long Short-Term Memory (LSTM) networks for sequential data analysis, and an Improved Chimp Optimization Algorithm (IChOA) for effective feature fusion. Remarkably, our model achieves an impressive accuracy rate of 97.8\%, outperforming existing approaches in the field. Furthermore, by integrating the GeoCoV19 dataset, we enable a comprehensive analysis spanning linguistic and geographical boundaries, thereby enriching our understanding of global pandemic discourse and furnishing indispensable insights for informed decision-making in public health crises. Through this holistic approach and innovative techniques, our research contributes significantly to advancing multimodal sentiment analysis, providing a robust framework for deciphering the complex interplay of emotions amidst unprecedented global challenges like the COVID-19 pandemic

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Exposing Students to New Terminologies While Collecting Browsing Search Data (Best Technical Paper)

Cited by 2 publications

References 26 publications

Reducing the dependency of having prior domain knowledge for effective online information retrieval

Reducing the dependency of having prior domain knowledge for effective online information retrieval

A Novel Hybrid Deep Learning IChOA-CNN-LSTM Model for Modality-Enriched and Multilingual Emotion Recognition in Social Media A Novel Hybrid Deep Learning IChOA-CNN-LSTM Model

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