Automated email answering by text‐pattern matching: Performance and error analysis

Sneiders, Eriks; Sjöbergh, Jonas; Alfalahi, Alyaa

doi:10.1111/exsy.12251

Cited by 7 publications

(4 citation statements)

References 35 publications

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“…The advantages of Weka are therefore the analysis and application of the entire process of machine learning, from data pre-processing, creating a model by choosing one of many realized machine learning algorithms, and making predictions based on input data. Many researchers have used this tool because of its intuitive user interface and easy way to get results from a trained model [33][34][35][36][37][38].…”

Section: Analysis Of Existing Solutionsmentioning

confidence: 99%

Visual Simulator for Mastering Fundamental Concepts of Machine Learning

Milakovic¹,

Draskovic²,

Nikolić³

2022

Applied Sciences

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Machine learning (ML) has become an increasingly popular choice of scientific research for many students due to its application in various fields. However, students often have difficulty starting with machine learning concepts due to too much focus on programming. Therefore, they are deprived of a more profound knowledge of machine learning concepts. The purpose of this research study was the analysis of introductory courses in machine learning at some of the best-ranked universities in the world and existing software tools used in those courses and designed to assist in learning machine learning concepts. Most university courses are based on the Python programming language and tools realized in this language. Other tools with less focus on programming are quite difficult to master. The research further led to the proposal of a new practical tool that users can use to learn without needing to know any programming language or programming skills. The simulator includes three methods: linear regression, decision trees, and k-nearest neighbors. In the research, several case studies are presented with applications of all realized ML methods based on real problems.

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Section: Analysis Of Existing Solutionsmentioning

confidence: 99%

Visual Simulator for Mastering Fundamental Concepts of Machine Learning

Milakovic¹,

Draskovic²,

Nikolić³

2022

Applied Sciences

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“…The weighted attributes are then related using probabilistic models to fill the available templates for email replying. Sneiders et al modelled automatic reply of email messages as text categorization problem (Sneiders et al, 2017). They evaluated performance of text-pattern matching technique by analyzing multiword expressions.…”

Section: Related Workmentioning

confidence: 99%

Automatic email response suggestion for support departments within a university

Parameswaran

Mishra

Bansal

et al. 2018

Preprint

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Background. Office of Academic Affairs (OAA), Office of Student Life (OSL) and Information Technology Helpdesk (ITD) are support functions within a university which receives hundreds of email messages on the daily basis. A large percentage of emails received by these departments are frequent and commonly used queries or request for information. Responding to every query by manually typing is a tedious and time consuming task and an automated approach for email response suggestion can save lot of time. Methods.We propose an application and solution approach for automatically generating and suggesting short email responses to support queries in a university environment. Our proposed solution can be used as one tap or one click solution for responding to various types of queries raised by faculty members and students in a university. We create a dataset for the application domain and make it publicly available. We apply a machine learning framework for classifying emails into categories such as office of academic affairs or information technology department. We apply a machine learning based classification approach for sub-category level classification also. We apply text pre-processing techniques, feature selection, support vector machine and naïve naive classifiers. We present an approach to overcome various natural language processing based challenges in the text. Results.We conduct a series of experiments and evaluate the approach using confusion matrix and accuracy based metrics. We study the discriminatory power of features and compare their relevance for the classification task. Our experimental results reveal that the proposed approach is effective. We conclude from our experiments that discriminatory features can be extracted from the text within our specific domain and automatic email response suggestion can be accurately created using machine learning algorithms and framework. We experiment with two different learning algorithms and observe that SVM outperforms Naïve Bayes. We achieve a classification accuracy of above $85\%$ for all the classes and sub-classes. Discussion. Our experiments on email response suggestion are conducted on a corpus consists of short and frequent emails by a university function but the proposed approach and techniques can be generalized to other domains also. We observe that different classifiers give different results and there is a significant difference in the predictive power of features. Background. Office of Academic Affairs (OAA), Office of Student Life (OSL) and Information Technology Helpdesk (ITD) are support functions within a university which receives hundreds of email messages on the daily basis. A large percentage of emails received by these departments are frequent and commonly used queries or request for information. Responding to every query by manually typing is a tedious and time consuming task and an automated approach for email response suggestion can save lot of time. Methods. We propose an application and solution approach for automatically generating an...

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“…Sneiders, Sjöbergh, and Alyaa ()(Automated email answering by text pattern matching: Performance and error analysis) present an experiment for measuring the performance of a text‐pattern matching system for automated email answering and subsequent error analysis. Furthermore, the authors introduce “context description” and “response trigger” as two essential components of an email inquiry sent to a contact centre.…”

Section: Guest Editorialmentioning

confidence: 99%