Enhanced answer selection in CQA using multi-dimensional features combination

Fan, Hongjie; Ma, Zhiyi; Li, Hongqiang; Wang, Dongsheng; Liu, Junfei

doi:10.26599/tst.2018.9010050

Cited by 14 publications

(5 citation statements)

References 27 publications

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“…K decision trees are generated based on K diverse training data extracted from the main dataset. Decision trees build the final RF model ( 36 ). In such combined methods as RF, a “‘strong learner” is constructed by consuming numerous “weak learners” ( 37 ).…”

Section: Methodsmentioning

confidence: 99%

Applications of different machine learning approaches in prediction of breast cancer diagnosis delay

et al. 2023

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BackgroundThe increasing rate of breast cancer (BC) incidence and mortality in Iran has turned this disease into a challenge. A delay in diagnosis leads to more advanced stages of BC and a lower chance of survival, which makes this cancer even more fatal.ObjectivesThe present study was aimed at identifying the predicting factors for delayed BC diagnosis in women in Iran.MethodsIn this study, four machine learning methods, including extreme gradient boosting (XGBoost), random forest (RF), neural networks (NNs), and logistic regression (LR), were applied to analyze the data of 630 women with confirmed BC. Also, different statistical methods, including chi-square, p-value, sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC), were utilized in different steps of the survey.ResultsThirty percent of patients had a delayed BC diagnosis. Of all the patients with delayed diagnoses, 88.5% were married, 72.1% had an urban residency, and 84.8% had health insurance. The top three important factors in the RF model were urban residency (12.04), breast disease history (11.58), and other comorbidities (10.72). In the XGBoost, urban residency (17.54), having other comorbidities (17.14), and age at first childbirth (>30) (13.13) were the top factors; in the LR model, having other comorbidities (49.41), older age at first childbirth (82.57), and being nulliparous (44.19) were the top factors. Finally, in the NN, it was found that being married (50.05), having a marriage age above 30 (18.03), and having other breast disease history (15.83) were the main predicting factors for a delayed BC diagnosis.ConclusionMachine learning techniques suggest that women with an urban residency who got married or had their first child at an age older than 30 and those without children are at a higher risk of diagnosis delay. It is necessary to educate them about BC risk factors, symptoms, and self-breast examination to shorten the delay in diagnosis.

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Section: Methodsmentioning

confidence: 99%

Applications of different machine learning approaches in prediction of breast cancer diagnosis delay

et al. 2023

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“…The extracted answers were then sorted by leveraging the offline trained model to judge the preference orders. Fan et al (2019) proposed to enhance answer selection in CQA using multidimensional feature combination and similarity order. They made full use of the information in answers to questions to determine the similarity between questions and answers, and use the text-based description of the answer to determine its sensibility.…”

Section: Related Workmentioning

confidence: 99%

Development of Answer Validation System Using Responders’ Attributes and Crowd Ranking

Adebisi¹,

Ojokoh²,

Adebayo³

et al. 2021

JSSM

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Crowdsourcing has found a wide range of application in Community Question Answering (CQA). However, one of its biggest challenges is the need to address the quality of crowd answers contributions. Therefore, this work proposed a system that seeks to validate answers to questions provided by respondents using responders' attributes and crowd ranking technique. Weights were assigned to respondent answers based on their academic records, experience and understanding of the question to obtain valid answers. Thereafter, valid answers were ranked by the crowd using Borda Count algorithm. The proposed system was evaluated using Usability and User experience (UX) measurement. The result obtained demonstrated the effectiveness of the applied technique.

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“…The frequently used features can be divided into content-related features (answer features) and answerer-related features (answerer features; Srba and Bielikova, 2016). Most of the answer features are related to the statistics of question and answer content, such as the lengths, the number of words or the number of sentences in answers or the ratio of the length of the question to the length of the answer (Fan et al, 2019;Liu et al, 2015). Other studies used the proportion of the number of overlapping words between the answer and the question and obtained reasonable results (Liu et al, 2015;Toba et al, 2014).…”

Section: Answerquality Factorsmentioning

confidence: 99%

Spam detection and high-quality features to analyse question –answer pairs

Wang

Chiang

Lin

2020

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Purpose In community question and answer (CQA) services, because of user subjectivity and the limits of knowledge, the distribution of answer quality can vary drastically – from highly related to irrelevant or even spam answers. Previous studies of CQA portals have faced two important issues: answer quality analysis and spam answer filtering. Therefore, the purposes of this study are to filter spam answers in advance using two-phase identification methods and then automatically classify the different types of question and answer (QA) pairs by deep learning. Finally, this study proposes a comprehensive study of answer quality prediction for different types of QA pairs. Design/methodology/approach This study proposes an integrated model with a two-phase identification method that filters spam answers in advance and uses a deep learning method [recurrent convolutional neural network (R-CNN)] to automatically classify various types of questions. Logistic regression (LR) is further applied to examine which answer quality features significantly indicate high-quality answers to different types of questions. Findings There are four prominent findings. (1) This study confirms that conducting spam filtering before an answer quality analysis can reduce the proportion of high-quality answers that are misjudged as spam answers. (2) The experimental results show that answer quality is better when question types are included. (3) The analysis results for different classifiers show that the R-CNN achieves the best macro-F1 scores (74.8%) in the question type classification module. (4) Finally, the experimental results by LR show that author ranking, answer length and common words could significantly impact answer quality for different types of questions. Originality/value The proposed system is simultaneously able to detect spam answers and provide users with quick and efficient retrieval mechanisms for high-quality answers to different types of questions in CQA. Moreover, this study further validates that crucial features exist among the different types of questions that can impact answer quality. Overall, an identification system automatically summarises high-quality answers for each different type of questions from the pool of messy answers in CQA, which can be very useful in helping users make decisions.

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Enhanced answer selection in CQA using multi-dimensional features combination

Cited by 14 publications

References 27 publications

Applications of different machine learning approaches in prediction of breast cancer diagnosis delay

Applications of different machine learning approaches in prediction of breast cancer diagnosis delay

Development of Answer Validation System Using Responders’ Attributes and Crowd Ranking

Spam detection and high-quality features to analyse question –answer pairs

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