Min-Yuh Day

Reference metadata extraction using a hierarchical knowledge representation framework

The integration of bibliographical information on scholarly publications available on the Internet is an important task in the academic community. Accurate reference metadata extraction from such publications is essential for the integration of metadata from heterogeneous reference sources. In this paper, we propose a hierarchical template-based reference metadata extraction method for scholarly publications. We adopt a hierarchical knowledge representation framework called INFOMAP, which automatically extracts metadata. The experimental results show that, by using INFOMAP, we can extract author, title, journal, volume, number (issue), year, and page information from different kinds of reference styles with a high degree of precision. The overall average accuracy is 92.39% for the six major reference styles compared in this study.

The measurement of user satisfaction with question answering systems

Question Answering Systems (QAS) are receiving increasing attention from IS researchers, particularly those in the information retrieval and natural language processing communities. Evaluation of an ISs success and user satisfaction are important issues, especially for emerging online service systems using the Internet. Although many QAS have been implemented, little work has been done on the development of an evaluation model for them. Our purpose was to develop a validated instrument to measure user satisfaction with QAS (USQAS). The proposed validated instrument was intended as a reference for the design of QAS from a users perspective.

A knowledge-based approach to citation extraction

Integration of the bibliographical information of scholarly publications available on the Internet is an important task in academic research. To accomplish this task, accurate reference metadata extraction for scholarly publications is essential for the integration of information from heterogeneous reference sources. In this paper, we propose a knowledge-based approach to literature mining and focus on reference metadata extraction methods for scholarly publications. We adopt an ontological knowledge representation framework called INFOMAP to automatically extract the reference metadata. The experimental results show that, by using INFOMAP, we can extract author, title, journal, volume, number (issue), year, and page information from different reference styles with a high degree of accuracy. The overall average field accuracy of citation extraction for a bioinformatics dataset is 97.87% for six reference styles.

Question Classification in English-Chinese Cross-Language Question Answering: An Integrated Genetic Algorithm and Machine Learning Approach

Question classification plays an important role in cross-language question answering (CLQA) systems, while question Informer plays a key role in enhancing question classification for factual question answering. In this paper, we propose an integrated genetic algorithm (GA) and machine learning (ML) approach for question classification in English-Chinese cross-language question answering. To enhance question informer prediction, we use a hybrid method that integrates GA and conditional random fields (CRF) to optimize feature subset selection in a CRF-based question informer prediction model. The proposed approach extends cross-language question classification by using the GA-CRF question informer feature with support vector machines (SVM). The results of evaluations on the NTCIR-6 CLQA question sets demonstrate the efficacy of the approach in improving the accuracy of question classification in English-Chinese cross-language question answering.

An integrated knowledge-based and machine learning approach for Chinese question classification

Question classification plays an important role in question-answering systems. Chinese question classification is the process that analyzes a question and labels it based on its question type and expected answer type. In this paper, we propose an integrated knowledge-based and machine learning approach for Chinese question classification that focuses on factoid question answering. We develop a Chinese question classification scheme for CLQA C-C (cross language question answering Chinese to Chinese) factoid question answering, and define a coarse-grained and fine-grained classification taxonomy for a Chinese question-answering system. We adopt INFOMAP inference engine to support the knowledge-based approach for Chinese questions, which can be formulated as templates and use SVM (support vector machines) as the machine learning approach for large collections of labeled Chinese questions. Our experimental results show that the accuracy of Chinese question classification using INFOMAP alone is 88% and 73.5% with SVM alone. In contrast, classification based on a hybrid approach that incorporates SVM and INFOMAP yields an accuracy rate of 92%.

FAQ-Centered Organizational Memory

The value of a piece of information in an organization is related to its retrieval (or requested) frequency. Therefore, collecting the answers to the frequently asked questions (FAQs) and constructing a good retrieval mechanism is a useful way to maintain organizational memory (OM). Since natural language is the easiest way for people to communicate, we have designed a natural language dialogue system for sharing the valuable knowledge of an organization. The system receives a natural language query from the user and matches it with a FAQ. Either an appropriate answer will be returned according to the user profile or the system will ask-back another question to the user so that a more detailed query can be formed. This dialogue will continue until the user is satisfied or a detailed answer is obtained, hi this paper, we apply natural language processing techniques to build a computer system that can help achieve the goal of OM.

An integrated evaluation model of user satisfaction with social media services

Social media services (SMSs) have been growing rapidly in recent years, and have therefore attracted increasing attention from practitioner and researchers. Social media services refer to the online services provide users with social media applications like Youtube, Facebook, and Wikipedia. Satisfaction is an important construct and user satisfaction is critical to the successful information systems. This study integrated the expectation-confirmation theory (ECT) by introducing perceived social influence and perceived enjoyment in the development of an integrated evaluation model for studying user satisfaction and continuance intention in the context of social media services. Structural equation modeling (SEM) is used to analyze the measurement and structural model. Empirical results show that the proposed model has a good fit in terms of theoretical robustness and practical application. Our findings suggest that the key determinants of user satisfaction with social media service are confirmation, perceived social influence, and perceived enjoyment, while the outcome of user satisfaction is enhanced continuance intention.

A supervised learning approach to biological question answering

Biologists rely on keyword-based search engines to retrieve superficially relevant papers, from which they must filter out the irrelevant information manually. Question answering (QA) systems can offer more efficient and user-friendly ways of retrieving such information. Two contributions are provided in this paper. First, a factoid QA system is developed to employ a named entity recognition module to extract answer candidates and a linear model to rank them. The linear model uses various semantic features, such as named entity types and semantic roles. To tune the weights of features used by the model, a novel supervised learning algorithm, which only needs small amounts of training data, is provided. Second, a QA system may assign several answers with the same score, making evaluation unfair. To solve this problem, an efficient formula for a mean average reciprocal rank (MARR) is proposed to reduce the complexity of its computation. After employing all effective semantic features, our system achieves a top-1 MARR of 74.11% and top-5 MARR of 76.68%. In comparison of the baseline system, the top-1 and top-5 MARR increase by 9.5% and 7.1%. In addition, the experiment result on test set shows our ranking method, which achieves 55.58% top-1 MARR and 66.99% top-5 MARR, significantly surpasses traditional BM25 and simple voting in performance by averagely 35.23% and 36.64%, respectively.

Biological question answering with syntactic and semantic feature matching and an improved mean reciprocal ranking measurement

Specific information on biomolecular events such as protein-protein and gene-protein interactions is essential for molecular biology researchers. However, the results derived by current keyword-based information retrieval engine contain a great deal of noisy information, which forces biologists to use a combination of several keywords to locate information. To resolve this problem, we propose a question answering (QA) system that offers more efficient and user-friendly ways to retrieve desired information. In addition, QA system measurements may suffer from the same score problem, so the evaluation of a QA system may be unfair. An improved mean reciprocal rank (MRR) measurement, mean average reciprocal rank (MARR), and an efficient formula to reduce the computational complexity of the MARR are proposed to address the same score problem. With our syntactic and semantic features, our system achieves a Top-1 MARR of 74.11% and Top-5 MARR of 76.68%. Compared to the baseline system, Top-1 MARR and Top-5 MARR increase by 16.17% and 18.61% respectively.

Boosting Chinese Question Answering with Two Lightweight Methods: ABSPs and SCO-QAT

Question Answering (QA) research has been conducted in many languages. Nearly all the top performing systems use heavy methods that require sophisticated techniques, such as parsers or logic provers. However, such techniques are usually unavailable or unaffordable for under-resourced languages or in resource-limited situations. In this article, we describe how a top-performing Chinese QA system can be designed by using lightweight methods effectively. We propose two lightweight methods, namely the Sum of Co-occurrences of Question and Answer Terms (SCO-QAT) and Alignment-based Surface Patterns (ABSPs). SCO-QAT is a co-occurrence-based answer-ranking method that does not need extra knowledge, word-ignoring heuristic rules, or tools. It calculates co-occurrence scores based on the passage retrieval results. ABSPs are syntactic patterns trained from question-answer pairs with a multiple alignment algorithm. They are used to capture the relations between terms and then use the relations to filter answers. We attribute the success of the ABSPs and SCO-QAT methods to the effective use of local syntactic information and global co-occurrence information. By using SCO-QAT and ABSPs, we improved the RU-Accuracy of our testbed QA system, ASQA, from 0.445 to 0.535 on the NTCIR-5 dataset. It also achieved the top 0.5 RU-Accuracy on the NTCIR-6 dataset. The result shows that lightweight methods are not only cheaper to implement, but also have the potential to achieve state-of-the-art performances.