Term Based Semantic Clusters for Very Short Text Classification

Paalman, Jasper; Mullick, Shantanu; Zervanou, Kalliopi; Zhang, Yingqian

doi:10.26615/978-954-452-056-4_102

Cited by 7 publications

(3 citation statements)

References 35 publications

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“…In such cases, recent NLP methods, e.g. (Paalman et al, 2019), can be used to extract information from these texts, which can subsequently be incorporated as additional features in the model.…”

Section: Resultsmentioning

confidence: 99%

Lost and Found: Predicting Airline Baggage At-risk of Being Mishandled

Leeuwen¹,

Zhang

Zervanou

et al. 2020

Proceedings of the 12th International Conference on Agents and Artificial Intelligence

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Section: Resultsmentioning

confidence: 99%

Lost and Found: Predicting Airline Baggage At-risk of Being Mishandled

Leeuwen¹,

Zhang

Zervanou

et al. 2020

Proceedings of the 12th International Conference on Agents and Artificial Intelligence

Self Cite

View full text Add to dashboard Cite

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“…The Word2Vec model considers the context and the semantic meaning of the words. So if two words are close in meaning they will be located close in the space (Mikolov, 2013). With the transformation of words to vectors, the machine-learning algorithms can perform algebra operations on them.…”

Section: Modelingmentioning

confidence: 99%

Clustering of Relevant Documents Based on Findability Effort in Information Retrieval

Rajagopal

Aghris

Fettah

et al. 2023

International Journal of Information Retrieval Research

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A user expresses their information need in the form of a query on an information retrieval (IR) system that retrieves a set of articles related to the query. The performance of the retrieval system is measured based on the retrieved content to the query, judged by expert topic assessors who are trained to find this relevant information. However, real users do not always succeed in finding relevant information in the retrieved list due to the amount of time and effort needed. This paper aims 1) to utilize the findability features to determine the amount of effort needed to find information from relevant documents using the machine learning approach and 2) to demonstrate changes in IR systems' performance when the effort is included in the evaluation. This study uses a natural language processing technique and unsupervised clustering approach to group documents by the amount of effort needed. The results show that relevant documents can be clustered using the k-means clustering approach, and the retrieval system performance varies by 23%, on average.

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“…Recent research in topic estimation in the context of open-ended questions (OEQ) has revealed that short texts covering not only responses to OEQ, but also huge volumes of text involved in interactions with social networks and the language of business stressed the need to develop specific algorithms for this class of texts (Burrows et al, 2015;Galhardi & Brancher, 2018;Paalman et al, 2019;Poulimenou et al, 2016;Zhang et al, 2019) [29][30][31][32][33].…”

Section: Related Workmentioning

confidence: 99%

Framework for Classroom Student Grading with Open-Ended Questions: A Text-Mining Approach

et al. 2022

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The purpose of this paper is to present a framework based on text-mining techniques to support teachers in their tasks of grading texts, compositions, or essays, which form the answers to open-ended questions (OEQ). The approach assumes that OEQ must be used as a learning and evaluation instrument with increasing frequency. Given the time-consuming grading process for those questions, their large-scale use is only possible when computational tools can help the teacher. This work assumes that the grading decision is entirely a teacher’s task responsibility, not the result of an automatic grading process. In this context, the teacher is the author of questions to be included in the tests, administration and results assessment, the entire cycle for this process being noticeably short: a few days at most. An attempt is made to address this problem. The method is entirely exploratory, descriptive and data-driven, the only data assumed as inputs being the texts of essays and compositions created by the students when answering OEQ for a single test on a specific occasion. Typically, the process involves exceedingly small data volumes measured by the power of current home computers, but big data when compared with human capabilities. The general idea is to use software to extract useful features from texts, perform lengthy and complex statistical analyses and present the results to the teacher, who, it is believed, will combine this information with his or her knowledge and experience to make decisions on mark allocation. A generic path model is formulated to represent that specific context and the kind of decisions and tasks a teacher should perform, the estimated results being synthesised using graphic displays. The method is illustrated by analysing three corpora of 126 texts originating in three different real learning contexts, time periods, educational levels and disciplines.

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Term Based Semantic Clusters for Very Short Text Classification

Cited by 7 publications

References 35 publications

Lost and Found: Predicting Airline Baggage At-risk of Being Mishandled

Lost and Found: Predicting Airline Baggage At-risk of Being Mishandled

Clustering of Relevant Documents Based on Findability Effort in Information Retrieval

Framework for Classroom Student Grading with Open-Ended Questions: A Text-Mining Approach

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