Pub Date : 1900-01-01DOI: 10.1007/978-3-031-26438-2_4
Mohamed O. Ibrahim, Shagufta Henna, Garry Cullen
{"title":"Multi-Graph Convolutional Neural Network for Breast Cancer Multi-task Classification","authors":"Mohamed O. Ibrahim, Shagufta Henna, Garry Cullen","doi":"10.1007/978-3-031-26438-2_4","DOIUrl":"https://doi.org/10.1007/978-3-031-26438-2_4","url":null,"abstract":"","PeriodicalId":286718,"journal":{"name":"Irish Conference on Artificial Intelligence and Cognitive Science","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127803248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1007/978-3-031-26438-2_16
Joyce Mahon, Brett A. Becker, Brian Mac Namee
{"title":"AI and ML in School Level Computing Education: Who, What and Where?","authors":"Joyce Mahon, Brett A. Becker, Brian Mac Namee","doi":"10.1007/978-3-031-26438-2_16","DOIUrl":"https://doi.org/10.1007/978-3-031-26438-2_16","url":null,"abstract":"","PeriodicalId":286718,"journal":{"name":"Irish Conference on Artificial Intelligence and Cognitive Science","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114901178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent increases in the use and availability of short messages have created opportunities to harvest vast amounts of information through machine-based classification. However, traditional classification methods have failed to yield accuracies comparable to classification accuracies on longer texts. Several approaches have previously been employed to extend traditional methods to overcome this problem, including the enhancement of the original texts through the construction of associations with external data supplementation sources. Existing literature does not precisely describe the impact of text length on classification performance. This work quantitatively examines the changes in accuracy of a small selection of classifiers using a variety of enhancement methods, as text length progressively decreases. Findings, based on ANOVA testing at a 95% confidence interval, suggest that the performance of classifiers using simple enhancements decreases with decreasing text length, but that the use of more sophisticated enhancements risks over-supplementation of the text and consequent concept drift and classification performance decrease as text length increases.
{"title":"How Short is a Piece of String? : The Impact of Text Length and Text Augmentation on Short-text Classification","authors":"Austin Mccartney, Svetlana Hensman, L. Longo","doi":"10.21427/D7151M","DOIUrl":"https://doi.org/10.21427/D7151M","url":null,"abstract":"Recent increases in the use and availability of short messages have created opportunities to harvest vast amounts of information through machine-based classification. However, traditional classification methods have failed to yield accuracies comparable to classification accuracies on longer texts. Several approaches have previously been employed to extend traditional methods to overcome this problem, including the enhancement of the original texts through the construction of associations with external data supplementation sources. Existing literature does not precisely describe the impact of text length on classification performance. This work quantitatively examines the changes in accuracy of a small selection of classifiers using a variety of enhancement methods, as text length progressively decreases. Findings, based on ANOVA testing at a 95% confidence interval, suggest that the performance of classifiers using simple enhancements decreases with decreasing text length, but that the use of more sophisticated enhancements risks over-supplementation of the text and consequent concept drift and classification performance decrease as text length increases.","PeriodicalId":286718,"journal":{"name":"Irish Conference on Artificial Intelligence and Cognitive Science","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121449769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the increasing amounts of textual data being collected online, automated text classification techniques are becoming increasingly important. However, a lot of this data is in the form of short-text with just a handful of terms per document (e.g. Text messages, tweets or Facebook posts). This data is generally too sparse and noisy to obtain satisfactory classification. Two techniques which aim to alleviate this problem are Latent Dirichlet Allocation (LDA) and Formal Concept Analysis (FCA). Both techniques have been shown to improve the performance of short-text classification by reducing the sparsity of the input data. The relative performance of classifiers that have been enhanced using each technique has not been directly compared so, to address this issue, this work presents an experiment to compare them, using supervised models. It has shown that FCA leads to a much higher degree of correlation among terms than LDA and initially gives lower classification accuracy. However, once a subset of features is selected for training, the FCA models can outperform those trained on LDA expanded data.
{"title":"A Comparison on the Classification of Short-text Documents Using Latent Dirichlet Allocation and Formal Concept Analysis","authors":"Noel Rogers, L. Longo","doi":"10.21427/D7XR39","DOIUrl":"https://doi.org/10.21427/D7XR39","url":null,"abstract":"With the increasing amounts of textual data being collected online, automated text classification techniques are becoming increasingly important. However, a lot of this data is in the form of short-text with just a handful of terms per document (e.g. Text messages, tweets or Facebook posts). This data is generally too sparse and noisy to obtain satisfactory classification. Two techniques which aim to alleviate this problem are Latent Dirichlet Allocation (LDA) and Formal Concept Analysis (FCA). Both techniques have been shown to improve the performance of short-text classification by reducing the sparsity of the input data. The relative performance of classifiers that have been enhanced using each technique has not been directly compared so, to address this issue, this work presents an experiment to compare them, using supervised models. It has shown that FCA leads to a much higher degree of correlation among terms than LDA and initially gives lower classification accuracy. However, once a subset of features is selected for training, the FCA models can outperform those trained on LDA expanded data.","PeriodicalId":286718,"journal":{"name":"Irish Conference on Artificial Intelligence and Cognitive Science","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115603347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1007/978-3-031-26438-2_41
Lalu Prasad Lenka, Mélanie Bouroche
{"title":"Safe Lane-Changing in CAVs Using External Safety Supervisors: A Review","authors":"Lalu Prasad Lenka, Mélanie Bouroche","doi":"10.1007/978-3-031-26438-2_41","DOIUrl":"https://doi.org/10.1007/978-3-031-26438-2_41","url":null,"abstract":"","PeriodicalId":286718,"journal":{"name":"Irish Conference on Artificial Intelligence and Cognitive Science","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115346517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1007/978-3-031-26438-2_29
AhmedSalah Jouda
{"title":"Exploring Abstractive vs. Extractive Summarisation Techniques for Sports News","authors":"AhmedSalah Jouda","doi":"10.1007/978-3-031-26438-2_29","DOIUrl":"https://doi.org/10.1007/978-3-031-26438-2_29","url":null,"abstract":"","PeriodicalId":286718,"journal":{"name":"Irish Conference on Artificial Intelligence and Cognitive Science","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130008232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1007/978-3-031-26438-2_36
Maha Riad, Saeedeh Ghanadbashi, F. Golpayegani
{"title":"Run-Time Norms Synthesis in Dynamic Environments with Changing Objectives","authors":"Maha Riad, Saeedeh Ghanadbashi, F. Golpayegani","doi":"10.1007/978-3-031-26438-2_36","DOIUrl":"https://doi.org/10.1007/978-3-031-26438-2_36","url":null,"abstract":"","PeriodicalId":286718,"journal":{"name":"Irish Conference on Artificial Intelligence and Cognitive Science","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126966968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1007/978-3-031-26438-2_24
Diego Carraro, Kenneth N. Brown
{"title":"CouRGe: Counterfactual Reviews Generator for Sentiment Analysis","authors":"Diego Carraro, Kenneth N. Brown","doi":"10.1007/978-3-031-26438-2_24","DOIUrl":"https://doi.org/10.1007/978-3-031-26438-2_24","url":null,"abstract":"","PeriodicalId":286718,"journal":{"name":"Irish Conference on Artificial Intelligence and Cognitive Science","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124364955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1007/978-3-031-26438-2_14
M. Alfano, J. Kellett, B. Lenzitti, M. Helfert
{"title":"An Intelligent Empowering Agent (IEA) to Provide Easily Understood and Trusted Health Information Appropriate to the User Needs","authors":"M. Alfano, J. Kellett, B. Lenzitti, M. Helfert","doi":"10.1007/978-3-031-26438-2_14","DOIUrl":"https://doi.org/10.1007/978-3-031-26438-2_14","url":null,"abstract":"","PeriodicalId":286718,"journal":{"name":"Irish Conference on Artificial Intelligence and Cognitive Science","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125193075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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