The Experts below are selected from a list of 374049 Experts worldwide ranked by ideXlab platform
Lin Jia - One of the best experts on this subject based on the ideXlab platform.
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Uncertain Temporal Information Representation and the Extensions of Temporal Operation
Computer Science, 2005Co-Authors: Lin JiaAbstract:Temporal Information representation and query are the main research topics in Temporal database. It still re- mains difficulties in handing uncertain Temporal Information in Temporal database while in real life many events happen with uncertain Temporal Information. A new model for representing uncertain Temporal Information is proposed and the extensions of various Temporal operations are also presented in this paper. The model and the extensions enrich the flexibility of Temporal database.
Zheng Qi-lun - One of the best experts on this subject based on the ideXlab platform.
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Unified model for uncertain Temporal Information representation
Computer Engineering, 2005Co-Authors: Zheng Qi-lunAbstract:Temporal representation and reasoning is a main research topic in artificial intelligence. Most common models can only represent certain Temporal Information, but many events happen with uncertain Temporal Information in real life. A new model for representing uncertain and certain Temporal Information was proposed to describe events and facts with time indeterminacy. This model firstly defined some Temporal objects (such as time point and time period), then defined several relations among Temporal objects and discussed the transitivity between the relations. Finally, two examples were analyzed, using this model to solve the uncertain Temporal reasoning problem.
Chunfa Yuan - One of the best experts on this subject based on the ideXlab platform.
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An Overview of Temporal Information Extraction
International Journal of Computer Processing of Languages, 2005Co-Authors: Kam-fai Wong, Yunqing Xia, Chunfa YuanAbstract:Research of Temporal Information Extraction was regarded as a subtask of named entity recognition in 1990's. To date, the scope of this research is broadened, ranging from Temporal expression extraction and annotation to Temporal reasoning and understanding. This area of research is now a hot NLP topic and the results are applicable to question answering, Information extraction, text summarization, etc. This paper presents the past, present and future research development in Temporal Information extraction.
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An Overview of Temporal Information Extraction
International Journal of Computer Processing of Languages, 2005Co-Authors: Kam-fai Wong, Yunqing Xia, Chunfa YuanAbstract:Research of Temporal Information Extraction was regarded as a subtask of named entity recognition in 1990's. To date, the scope of this research is broadened, ranging from Temporal expression extraction and annotation to Temporal reasoning and understanding. This area of research is now a hot NLP topic and the results are applicable to question answering, Information extraction, text summarization, etc. This paper presents the past, present and future research development in Temporal Information extraction.Department of Computin
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Toward automatic Chinese Temporal Information extraction
Journal of the American Society for Information Science and Technology, 2001Co-Authors: Kam-fai Wong, Chunfa YuanAbstract:Over the past few years, Temporal Information processing and Temporal database management have increasingly become hot topics. Nevertheless, only a few researchers have investigated these areas in the Chinese language. This lays down the objective of our research: to exploit Chinese language processing techniques for Temporal Information extraction and concept reasoning. In this article, we first study the mechanism for expressing time in Chinese. On the basis of the study, we then design a general frame structure for maintaining the extracted Temporal concepts and propose a system for extracting time-dependent Information from Hong Kong financial news. In the system, Temporal knowledge is represented by different types of Temporal concepts (TTC) and different Temporal relations, including absolute and relative relations, which are used to correlate between action times and reference times. In analyzing a sentence, the algorithm first determines the situation related to the verb. This in turn will identify the type of Temporal concept associated with the verb. After that, the relevant Temporal Information is extracted and the Temporal relations are derived. These relations link relevant concept frames together in chronological order, which in turn provide the knowledge to fulfill users' queries, e.g., for question-answering (i.e., Q&A) applications.
Etienne Kerre - One of the best experts on this subject based on the ideXlab platform.
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Acquiring Vague Temporal Information from the Web
2008 IEEE WIC ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2008Co-Authors: Steven Schockaert, M. De Cock, Etienne KerreAbstract:Many real-world Information needs are naturally formulated as queries with Temporal constraints. However, the structured Temporal background Information needed to support such constraints is usually not available to Information retrieval systems. As an alternative, we automatically compile Temporal knowledge bases from web documents, combining whatever quantitative and qualitative Temporal Information we can find about events of interest. By using simple heuristic techniques for Temporal Information extraction, we initially focus more on recall than on precision, relying on the subsequent application of a fuzzy Temporal reasoner to improve the reliability of the extracted Information.
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Question answering with imperfect Temporal Information
Lecture Notes in Computer Science, 2006Co-Authors: Steven Schockaert, David Ahn, Martine De Cock, Etienne KerreAbstract:A Temporal question answering system must be able to deduce which qualitative Temporal relation holds between two events, a reasoning task that is complicated by the fact that historical events tend to have a gradual beginning and ending. In this paper, we introduce an algebra of Temporal relations that is well--suited to represent the qualitative Temporal Information we have at our disposal. We provide a practical algorithm for deducing new Temporal knowledge, and show how this can be used to answer questions that require several pieces of qualitative and quantitative Temporal Information to be combined. Finally. We propose a heuristic technique to cope with inconsistencies that may arise when integrating qualitative and quantitative Information.
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FQAS - Question answering with imperfect Temporal Information
Flexible Query Answering Systems, 2006Co-Authors: Steven Schockaert, David Ahn, Martine De Cock, Etienne KerreAbstract:A Temporal question answering system must be able to deduce which qualitative Temporal relation holds between two events, a reasoning task that is complicated by the fact that historical events tend to have a gradual beginning and ending. In this paper, we introduce an algebra of Temporal relations that is well–suited to represent the qualitative Temporal Information we have at our disposal. We provide a practical algorithm for deducing new Temporal knowledge, and show how this can be used to answer questions that require several pieces of qualitative and quantitative Temporal Information to be combined. Finally, we propose a heuristic technique to cope with inconsistencies that may arise when integrating qualitative and quantitative Information.
Adam Jatowt - One of the best experts on this subject based on the ideXlab platform.
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Survey of Temporal Information Retrieval and Related Applications
ACM Computing Surveys, 2015Co-Authors: Ricardo Campos, Gaël Dias, Alípio Mário Jorge, Adam JatowtAbstract:Temporal Information retrieval has been a topic of great interest in recent years. Its purpose is to improve the effectiveness of Information retrieval methods by exploiting Temporal Information in documents and queries. In this article, we present a survey of the existing literature on Temporal Information retrieval. In addition to giving an overview of the field, we categorize the relevant research, describe the main contributions, and compare different approaches. We organize existing research to provide a coherent view, discuss several open issues, and point out some possible future research directions in this area. Despite significant advances, the area lacks a systematic arrangement of prior efforts and an overview of state-of-the-art approaches. Moreover, an effective end-to-end Temporal retrieval system that exploits Temporal Information to improve the quality of the presented results remains undeveloped.
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Temporal Information searching behaviour and strategies
Information Processing & Management, 2015Co-Authors: Hideo Joho, Adam Jatowt, Roi BlancoAbstract:Temporal Information searching behaviour and strategies were investigated.Searching patterns were identified for past, recency and future search tasks.Implications for the development of Temporal IR systems are discussed. Temporal aspects have been receiving a great deal of interest in Information Retrieval and related fields. Although previous studies have proposed, designed and implemented Temporal-aware systems and solutions, understanding of people's Temporal Information searching behaviour is still limited. This paper reports the findings of a user study that explored Temporal Information searching behaviour and strategies in a laboratory setting. Information needs were grouped into three Temporal classes (Past, Recency, and Future) to systematically study their characteristics. The main findings of our experiment are as follows. (1) It is intuitive for people to augment topical keywords with Temporal expressions such as history, recent, or future as a tactic of Temporal search. (2) However, such queries produce mixed results and the success of query reformulations appears to depend on topics to a large extent. (3) Search engine interfaces should detect Temporal Information needs to trigger the display of Temporal search options. (4) Finding a relevant Wikipedia page or similar summary page is a popular starting point of past Information needs. (5) Current search engines do a good job for Information needs related to recent events, but more work is needed for past and future tasks. (6) Participants found it most difficult to find future Information. Searching for domain experts was a key tactic in Future search, and file types of relevant documents are different from other Temporal classes. Overall, the comparison of search across Temporal classes indicated that Future search was the most difficult and the least successful followed by the search for the Past and then for Recency Information. This paper discusses the implications of these findings on the design of future Temporal IR systems.
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overview of ntcir 11 Temporal Information access Temporalia task
NTCIR, 2014Co-Authors: Hideo Joho, Adam Jatowt, Roi Blanco, Hajime Naka, Shuhei YamamotoAbstract:This paper describes the overview of NTCIR-11 Temporal Information Access (Temporalia) task. This pilot task aims to foster research in Temporal aspects of Information retrieval and search. Temporalia is composed of two subtasks: Temporal Query Intent Classication (TQIC) and Temporal Information Retrieval (TIR) subtask. TQIC attracted 6 teams which submitted a total of 17 runs, while 6 teams took part in TIR proposing 18 runs. In this paper we describe both subtasks, datasets, evaluation methods and results of meta analyses.
