Question categorization and classification using grammar based approach

Mohasseb, Alaa; Bader-El-Den, Mohamed; Cocea, Mihaela

doi:10.1016/j.ipm.2018.05.001

Cited by 59 publications

(31 citation statements)

References 34 publications

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“…Questions used in this experiment are labelled to six different categories,namely; causal, choice, confirmation (Yes-No Questions), factoid (Wh-Questions), hypothetical and list. These classifications were proposed by [25]. Table 2 presents classification performance details (Precision, Recall and F-Measure) of the NB classifier and the performance details of the NB classifier with the use of the SMOTE algorithm.…”

Section: Experimental Study and Resultsmentioning

confidence: 99%

Improving Imbalanced Question Classification Using Structured Smote Based Approach

Mohasseb

Bader-El-Den

Cocea

et al. 2018

2018 International Conference on Machine Learning and Cybernetics (ICMLC)

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Questions Classification (QC) is one of the most popular text classification applications. QC plays an important role in question-answering systems. However, as in many real-world classification problems, QC may suffer from the problem of class imbalance. The classification of imbalanced data has been a key problem in machine learning and data mining. In this paper, we propose a framework that deals with the class imbalance using a hierarchical SMOTE algorithm for balancing different types of questions. The proposed framework is grammar-based, which involves using the grammatical pattern for each question and using machine learning algorithms to classify them. Experimental results imply that the proposed framework demonstrates a good level of accuracy in identifying different question types and handling class imbalance.

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Section: Experimental Study and Resultsmentioning

confidence: 99%

Improving Imbalanced Question Classification Using Structured Smote Based Approach

Mohasseb

Bader-El-Den

Cocea

et al. 2018

2018 International Conference on Machine Learning and Cybernetics (ICMLC)

Self Cite

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“…Over the recent years, many research studies have been conducted to solve different challenging problems on CQA networks [1,2,5]. In CQA networks, documents are posted by registered users in the forms of questions and answers.…”

Section: Expert Finding In Cqa Networkmentioning

confidence: 99%

Mining shape of expertise: A novel approach based on convolutional neural network

Dehghan

Rahmani

Abin

et al. 2020

Information Processing & Management

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Expert finding addresses the task of retrieving and ranking talented people on the subject of user query. It is a practical issue in the Community Question Answering networks. Recruiters looking for knowledgeable people for their job positions are the most important clients of expert finding systems. In addition to employee expertise, the cost of hiring new staff is another significant concern for organizations. An efficient solution to cope with this concern is to hire T-shaped experts that are cost-effective. In this study, we have proposed a new deep model for T-shaped experts finding based on Convolutional Neural Networks. The proposed model tries to match queries and users by extracting local and position-invariant features from their corresponding documents. In other words, it detects users' shape of expertise by learning patterns from documents of users and queries simultaneously. The proposed model contains two parallel CNN's that extract latent vectors of users and queries based on their corresponding documents and join them together in the last layer to match queries with users. Experiments on a large subset of Stack Overflow documents indicate the effectiveness of the proposed method against baselines in terms of NDCG, MRR, and ERR evaluation metrics.

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“…The SMOTE 39 was used to handle the issue of class imbalance. SMOTE is one of the most effective and widely used oversampling techniques that was used by several works in literature [41][42][43][44][45][46] to effectively handle the class imbalance problem. SMOTE increases the number of patient records who die inside the hospital (minority class) by inserting synthetic patient records.…”

Section: A Framework For Early Icu Mortality Predictionmentioning

confidence: 99%

Predicting hospital mortality for intensive care unit patients: Time-series analysis

Awad

Bader-El-Den

McNicholas

et al. 2019

Health Informatics J

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Current mortality prediction models and scoring systems for intensive care unit patients are generally usable only after at least 24 or 48 h of admission, as some parameters are unclear at admission. However, some of the most relevant measurements are available shortly following admission. It is hypothesized that outcome prediction may be made using information available in the earliest phase of intensive care unit admission. This study aims to investigate how early hospital mortality can be predicted for intensive care unit patients. We conducted a thorough time-series analysis on the performance of different data mining methods during the first 48 h of intensive care unit admission. The results showed that the discrimination power of the machine-learning classification methods after 6 h of admission outperformed the main scoring systems used in intensive care medicine (Acute Physiology and Chronic Health Evaluation, Simplified Acute Physiology Score and Sequential Organ Failure Assessment) after 48 h of admission.

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Question categorization and classification using grammar based approach

Cited by 59 publications

References 34 publications

Improving Imbalanced Question Classification Using Structured Smote Based Approach

Improving Imbalanced Question Classification Using Structured Smote Based Approach

Mining shape of expertise: A novel approach based on convolutional neural network

Predicting hospital mortality for intensive care unit patients: Time-series analysis

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