User-interactive Question Answering System Research Articles

A π-Calculus-Based Complaint Model for a User-Interactive Question Answering System

User-interactive question answering systems are catching more and more attention, however users complaints arise within it from such problems as unfair compensation. This paper focuses on a π-calculus-based formal model for the complaint process of a user-interactive question answering system. The formal model is verified by employing MWB tools so as to demonstrate the deadlock-free and other properties. The research results are of special significance to the comprehensive formal analysis of the user-interactive question answering systems.

User interest modeling and its application for question recommendation in user-interactive question answering systems

In this paper, we propose a generative model, the Topic-based User Interest (TUI) model, to capture the user interest in the User-Interactive Question Answering (UIQA) systems. Specifically, our method aims to model the user interest in the UIQA systems with latent topic method, and extract interests for users by mining the questions they asked, the categories they participated in and relevant answer providers. We apply the TUI model to the application of question recommendation, which automatically recommends to certain user appropriate questions he might be interested in. Data collection from Yahoo! Answers is used to evaluate the performance of the proposed model in question recommendation, and the experimental results show the effectiveness of our proposed model.

Toward Automatic Answers in User-Interactive Question Answering Systems

A strategy of automatic answer retrieval for repeated or similar questions in user-interactive systems by employing semantic question patterns is proposed in this paper. The used semantic question pattern is a generalized representation of a group of questions with both similar structure and relevant semantics. Specifically, it consists of semantic annotations (or constraints) for the variable components in the pattern and hence enhances the semantic representation and greatly reduces the ambiguity of a question instance when asked by a user using such pattern. The proposed method consists of four major steps: structure processing, similar pattern matching and filtering, automatic pattern generation, question similarity evaluation and answer retrieval. Preliminary experiments in a real question answering system show a precision of more than 90% of the method.

Automatic categorization of questions for user-interactive question answering

Question categorization, which suggests one of a set of predefined categories to a user’s question according to the question’s topic or content, is a useful technique in user-interactive question answering systems. In this paper, we propose an automatic method for question categorization in a user-interactive question answering system. This method includes four steps: feature space construction, topic-wise words identification and weighting, semantic mapping, and similarity calculation. We firstly construct the feature space based on all accumulated questions and calculate the feature vector of each predefined category which contains certain accumulated questions. When a new question is posted, the semantic pattern of the question is used to identify and weigh the important words of the question. After that, the question is semantically mapped into the constructed feature space to enrich its representation. Finally, the similarity between the question and each category is calculated based on their feature vectors. The category with the highest similarity is assigned to the question. The experimental results show that our proposed method achieves good categorization precision and outperforms the traditional categorization methods on the selected test questions.

Using a User-Interactive QA System for Personalized E-Learning

A personalized e-learning framework based on a user-interactive question-answering (QA) system is proposed, in which a user-modeling approach is used to capture personal information of students and a personalized answer extraction algorithm is proposed for personalized automatic answering. In our approach, a topic ontology (or concept hierarchy) of course content defined by an instructor is used for the system to generate the corresponding structure of boards for holding relevant questions. Students can interactively post questions, and also browse, select, and answer others’ questions in their interested boards. A knowledge base is accumulated using historical question/answer (Q/A) pairs for knowledge reuse. The students’ log data are used to build an association space to compute the interest and authority of the students for each board and each topic. The personal information of students can help instructors design suitable teaching materials to enhance instruction efficiency, be used to implement the personalized automatic answering and distribute unsolved questions to relevant students to enhance the learning efficiency. The experiment results show the efficacy of our user-modeling approach.