In this work, a best answer recommendation model is proposed for a Question Answering (QA) system. A Community Question Answering System was subsequently developed based on the model. The system applies Brouwer Fixed ...In this work, a best answer recommendation model is proposed for a Question Answering (QA) system. A Community Question Answering System was subsequently developed based on the model. The system applies Brouwer Fixed Point Theorem to prove the existence of the desired voter scoring function and Normalized Google Distance (NGD) to show closeness between words before an answer is suggested to users. Answers are ranked according to their Fixed-Point Score (FPS) for each question. Thereafter, the highest scored answer is chosen as the FPS Best Answer (BA). For each question asked by user, the system applies NGD to check if similar or related questions with the best answer had been asked and stored in the database. When similar or related questions with the best answer are not found in the database, Brouwer Fixed point is used to calculate the best answer from the pool of answers on a question then the best answer is stored in the NGD data-table for recommendation purpose. The system was implemented using PHP scripting language, MySQL for database management, JQuery, and Apache. The system was evaluated using standard metrics: Reciprocal Rank, Mean Reciprocal Rank (MRR) and Discounted Cumulative Gain (DCG). The system eliminated longer waiting time faced by askers in a community question answering system. The developed system can be used for research and learning purposes.展开更多
随着人工智能技术的快速发展,自然语言处理(Natural Language Processing,NLP)技术在各个领域得到了广泛应用。文章提出一种基于NLP技术的智能培训系统中知识点与题库关联方法,该方法利用NLP技术对培训材料进行文本分析,自动提取知识点...随着人工智能技术的快速发展,自然语言处理(Natural Language Processing,NLP)技术在各个领域得到了广泛应用。文章提出一种基于NLP技术的智能培训系统中知识点与题库关联方法,该方法利用NLP技术对培训材料进行文本分析,自动提取知识点,并基于知识点和题库之间建立关联模型,实现试卷题目的自动分配。该方法能够有效提高培训系统的智能化水平,提高培训效率和质量。展开更多
文摘In this work, a best answer recommendation model is proposed for a Question Answering (QA) system. A Community Question Answering System was subsequently developed based on the model. The system applies Brouwer Fixed Point Theorem to prove the existence of the desired voter scoring function and Normalized Google Distance (NGD) to show closeness between words before an answer is suggested to users. Answers are ranked according to their Fixed-Point Score (FPS) for each question. Thereafter, the highest scored answer is chosen as the FPS Best Answer (BA). For each question asked by user, the system applies NGD to check if similar or related questions with the best answer had been asked and stored in the database. When similar or related questions with the best answer are not found in the database, Brouwer Fixed point is used to calculate the best answer from the pool of answers on a question then the best answer is stored in the NGD data-table for recommendation purpose. The system was implemented using PHP scripting language, MySQL for database management, JQuery, and Apache. The system was evaluated using standard metrics: Reciprocal Rank, Mean Reciprocal Rank (MRR) and Discounted Cumulative Gain (DCG). The system eliminated longer waiting time faced by askers in a community question answering system. The developed system can be used for research and learning purposes.
文摘随着人工智能技术的快速发展,自然语言处理(Natural Language Processing,NLP)技术在各个领域得到了广泛应用。文章提出一种基于NLP技术的智能培训系统中知识点与题库关联方法,该方法利用NLP技术对培训材料进行文本分析,自动提取知识点,并基于知识点和题库之间建立关联模型,实现试卷题目的自动分配。该方法能够有效提高培训系统的智能化水平,提高培训效率和质量。
