Content-Driven Answer Selection in Community Question Answering Using Clustering and Fuzzy Ranking
Tóm tắt
The main task of question answering system is retrieval of high-quality answer; to achieve that, we are utilizing string similarity approach with clustering model to retrieve similar or related questions from the dataset. But ranking similar type of question answer will not solve the problem; we also considered expert ratings of the answer and treating it as a user satisfactory index or rating; to incorporate this rating in our framework, we are using triangular fuzzy number (TFN) and average of TFN and similarity index of question which will give the precise satisfactory answer. For experimental results, we are using dataset of health domain where dataset contains collection of question asked by user related to their health. Experimental results on health Community Question Answering (CQA) datasets indicate that the proposed method can give the appropriate response for new questions with higher accuracy than other advanced methods. Evaluated results are compared with existing CQA methods like CNN and LSTM. Proposed framework will be extended with machine learning approach using appropriate natural language processing (NLP) model.
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