Tianyong Hao

Semantic Pattern for User-Interactive Question Answering

A new semantic pattern is proposed in this paper, which can be used by users to post questions and answers in user-interactive question answering (QA) system. The necessary procedures of using semantic pattern in a QA system are also presented, which include question structure analysis, pattern matching, pattern generation, pattern classification and answer extraction. A user interface of using semantic pattern is also implemented in our QA system, which allows users to effectively post and answer questions. It gains good overall user satisfaction.

A Web-Based Platform for User-Interactive Question-Answering

A user-interactive question-answering (QA) platform named BuyAns (at www.buyans.com) is presented. The platform is a special kind of online community and mainly features a rewarding scheme for answering questions among all users, a pattern-based user interface (UI) for questioning and answering, and a pattern-based representation and storage scheme for accumulated question-answer pairs. The system actually proposes and promotes a C2C business model for exchanging and commercializing knowledge from ordinary people. It can also be used as an incentive and collaborative approach to knowledge acquisition. Driven by the business model, prompt and quality answers are quickly accumulated. Due to the patterns used, accurate answers can be provided automatically for repeated questions. Facilitating features and technologies, including user modeling, reputation management, and answer clustering and fusion, are also developed and briefly described. Preliminary user studies show the potential attraction of the system to its users as well as reasonable usability and user-satisfaction. We anticipate hot applications of such a system in the Web 2.0 era.

Automatic question generation for learning evaluation in medicine

An approach of automatic question generation from given learning material of medical text is presented in this paper. The main idea is to generate the questions automatically based on question templates which are created by training on many medical articles. In order to provide interesting questions, our research focuses on medical related concepts. This method can be used for evaluation of learner’s comprehension after he/she finished a reading material. Different from traditional learning system the articles and questions are all prepared beforehand; participants can learn whatever new input medical articles with the help of automatic question generation.

Adaptation Rule Learning for Case-Based Reasoning

A method of learning adaptation rules for case- based reasoning (CBR) is proposed in this paper. Adaptation rules are generated from the case-base with the guidance of domain knowledge which is also extracted from the case-base. The adaptation rules are refined before they are applied in the revision process. After solving each new problem, the adaptation rule set is updated by an evolution module in the retention process. The results of preliminary experiment show that the adaptation rules obtained could improve the performance of the CBR system compared to a retrieval-only CBR system.

Finding similar questions in collaborative question answering archives: toward bootstrapping-based equivalent pattern learning

Many questions submitted to Collaborative Question Answering (CQA) sites have similar questions answered before. We propose a precise approach of automatically finding an answer to such questions by automatically identifying “equivalent” questions submitted and answered, in the past. Our method is based on automatically generating equivalent question patterns by grouping together questions that have previously obtained the same answers. The generated patterns are used as seed patterns to match more questions to extract large number of equivalent patterns by a new bootstrapping-based learning method. The resulting patterns can be applied to match a new question to an equivalent one that has already been answered, and thus suggest potential answers automatically. We experimented with this approach over a large collection of more than 200,000 real questions drawn from the Yahoo! Answers archive, automatically acquiring over 16,991 groups of equivalent question patterns. These patterns allow our method to obtain over 57% recall and over 54% precision on suggesting an answer automatically to new questions, significantly improving over baseline methods.

Online role mining for context-aware mobile service recommendation

Finding and recommending suitable services for mobile devices are increasingly important due to the popularity of mobile Internet. While recent research has attempted to use role-based approaches to recommend services, role discovery is still an ongoing research topic. Using role-based approaches, popular mobile services can be recommended to other members in the same role group in a context- dependent manner. This paper proposes several role mining algorithms, to suit different application requirements, that automatically group users according to their interests and habits dynamically. Most importantly, we propose an online role mining algorithm that can discover role patterns efficiently and incrementally. Finally, we present a complete, question-based framework that can efficiently perform role mining for context-aware service recommendation in a mobile environment—where a device may not be always connected to the server and/or scalability of the role mining algorithm running on the server is critical.

Automatic categorization of questions for user-interactive question answering

Question categorization, which suggests one of a set of predefined categories to a users question according to the questions 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.

Context-Aware Service Recommendation for Moving Connected Devices

Finding and recommending suitable services for moving, connected devices are increasingly important due to the popularity of mobile Internet. While recent research has attempted to use role-based approaches to recommend services, role discovery is still an ongoing research topic. This paper proposes a simple and yet efficient, matrix-based, context-aware role mining method to automatically group users according to their interests and habits. Using a question-based approach, we also allow popular mobile services to be recommended to other members in the same group in a context dependent.

Categorizing and ranking search engine's results by semantic similarity

An automatic method for text categorizing and ranking search engines results by semantic similarity is proposed in this paper. We first obtain nouns and verbs from snippets obtained from search engine using Name Entity Recognition and part-of speech. A semantic similarity algorithm based on WordNet is proposed to calculate the similarity of each snippet to each of the pre-defined categories. A balanced similarity ranking method combined with Googles rank and timeliness of the pages is proposed to rank these snippets. Preliminary experiments with 500 labeled questions from TREC03 show that 72.7% are correctly categorized.

Automatic generation of semantic patterns for user-interactive question answering

An automatic method for generation of semantic patterns from free-text questions is proposed in this paper. An evaluation method is also proposed to estimate the suitability of the generated patterns and implemented in our user-interactive question answering (QA) system. Experiments with 5500 questions show that 63.9% generated patterns are satisfactory in the average.