A Comparative Study on Part-of-Speech Taggers’ Performance on Examination Questions Classification According to Bloom’s Taxonomy

Goh, Thing Thing; Jamaludin, Nor Azliana Akmal; Mohamed, Hassan; Ismail, Mohd Nazri; Chua, H. S.

doi:10.1088/1742-6596/2224/1/012001

Cited by 7 publications

(5 citation statements)

References 5 publications

(9 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This paper presents a continuous study of the framework proposed by Goh et al [17] for the examination question classifications according to Bloom's taxonomy. We aimed to study the framework of question classifications in order to improve its accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…There are many POS taggers, such as the TnT POS tagger, ClassifiedBasedPOS tagger, Perceptron POS tagger, and Stanford POS tagger, etc. The Stanford POS tagger was adopted in this module because it generally outperformed other taggers, according to Goh et al [17], Tian and Lo [18] and Go and Nocon [19]. The "English-left3wrods-distsim" and "English-bidirectional-distsim" are two commonly used models for the Stanford POS tagger.…”

Section: Question Preprocessingmentioning

confidence: 99%

See 1 more Smart Citation

Semantic Similarity Analysis for Examination Questions Classification Using WordNet

et al. 2023

Self Cite

View full text Add to dashboard Cite

Question classification based on Bloom’s Taxonomy (BT) has been widely accepted and used as a guideline in designing examination questions in many institutions of higher learning. The misclassification of questions may happen when the classification task is conducted manually due to a discrepancy in the understanding of BT by academics. Hence, several automated examination question classification systems have been proposed by researchers to perform question classification accurately. Most of this research has focused on specific subject areas only or single-sentence type questions. There has been a lack of research on question classification for multi-sentence type and multi-subject questions. This paper proposes a question classification system (QCS) to perform the examination of question classification using a semantic and synthetic approach. The questions were taken from various subjects of an engineering diploma course, and the questions were either single- or multiple-sentence types. The QCS was developed using a natural language toolkit (NLTK), Stanford POS tagger (SPOS), Stanford parser’s universal dependencies (UD), and WordNet similarity approaches. The QCS used the NLTK to process the questions into sentences and then word tokens, such as SPOS, to tag the word tokens and UD to identify the important word tokens, which were the verbs of the examination questions. The identified verbs were then compared with the BT’s verbs list in terms of word sense using the WordNet similarity approach before finally classifying the questions according to BT. The developed QCS achieved an overall 83% accuracy in the classification of a set of 200 examination questions, according to BT.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Question Preprocessingmentioning

confidence: 99%

Semantic Similarity Analysis for Examination Questions Classification Using WordNet

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…Research categorizes questions based on the amount of language information required for classification. Comparison experiments demonstrate the impact of various DL models on question categorization [66][67][68][69][70]. Deep transfer learning is employed to enhance question categorization, even in new domains.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of Questions from Text Data Using LSTM-Deep Learning Model

Nadir Hussain,

Dr. Sheikh Muhammad Saqib,

Hamza Arif

et al. 2024

VAWKUM trans. comput. sci.

View full text Add to dashboard Cite

This paper discusses the importance of detecting questions in textual data for various applications in natural language processing (NLP), such as question answering and chatbot creation. The proposed approach employs long short-term memory (LSTM) models to accurately identify questions by leveraging the sequential nature of language.The paper highlights that LSTM models address challenges like ambiguous language and varying sentence structures. They allow the model to learn from sequential patterns, crucial for understanding the intent behind the text. The preprocessing steps, including tokenization, embedding, and padding, are detailed to prepare the data for training and testing. The study investigates the impact of hyperparameters like hidden layers, hidden states, and optimizer choice on the LSTM algorithm’s performance. In experiments on benchmark datasets, the proposed LSTM-based approach consistently outperforms conventional machine learning models, achieving a remarkable accuracy of 99.25% on the test dataset. The paper concludes by suggesting future directions, including applyingthe approach to other NLP tasks like named entity recognition, sentiment analysis, and text classification. Further optimization for specific datasets or domains is also encouraged. Overall, this research contributes to robust question detection models in NLP, with potential applications in various fields.

show abstract

“…NLTK's default Perceptron tagger is employed due to its high accuracy compared to TNT and CRF taggers [21]. State-of-the-art POS tagging with the Penn Treebank dataset achieves an F1 Score of 98.3 [3].…”

Section: Part-of-speech Taggingmentioning

confidence: 99%

Leveraging Semantic Text Analysis to Improve the Performance of Transformer-Based Relation Extraction

Evans,

Latifi,

Ahsan

et al. 2024

Information

View full text Add to dashboard Cite

Keyword extraction from Knowledge Bases underpins the definition of relevancy in Digital Library search systems. However, it is the pertinent task of Joint Relation Extraction, which populates the Knowledge Bases from which results are retrieved. Recent work focuses on fine-tuned, Pre-trained Transformers. Yet, F1 scores for scientific literature achieve just 53.2, versus 69 in the general domain. The research demonstrates the failure of existing work to evidence the rationale for optimisations to finetuned classifiers. In contrast, emerging research subjectively adopts the common belief that Natural Language Processing techniques fail to derive context and shared knowledge. In fact, global context and shared knowledge account for just 10.4% and 11.2% of total relation misclassifications, respectively. In this work, the novel employment of semantic text analysis presents objective challenges for the Transformer-based classification of Joint Relation Extraction. This is the first known work to quantify that pipelined error propagation accounts for 45.3% of total relation misclassifications, the most poignant challenge in this domain. More specifically, Part-of-Speech tagging highlights the misclassification of complex noun phrases, accounting for 25.47% of relation misclassifications. Furthermore, this study identifies two limitations in the purported bidirectionality of the Bidirectional Encoder Representations from Transformers (BERT) Pre-trained Language Model. Firstly, there is a notable imbalance in the misclassification of right-to-left relations, which occurs at a rate double that of left-to-right relations. Additionally, a failure to recognise local context through determiners and prepositions contributes to 16.04% of misclassifications. Furthermore, it is highlighted that the annotation scheme of the singular dataset utilised in existing research, Scientific Entities, Relations and Coreferences (SciERC), is marred by ambiguity. Notably, two asymmetric relations within this dataset achieve recall rates of only 10% and 29%.

show abstract

A Comparative Study on Part-of-Speech Taggers’ Performance on Examination Questions Classification According to Bloom’s Taxonomy

Cited by 7 publications

References 5 publications

Semantic Similarity Analysis for Examination Questions Classification Using WordNet

Semantic Similarity Analysis for Examination Questions Classification Using WordNet

Detection of Questions from Text Data Using LSTM-Deep Learning Model

Leveraging Semantic Text Analysis to Improve the Performance of Transformer-Based Relation Extraction

Contact Info

Product

Resources

About