Diese Datenbank enthält über 40.000 Dokumente zu Themen aus den Bereichen Formalerschließung – Inhaltserschließung – Information Retrieval.
© 2015 W. Gödert, TH Köln, Institut für Informationswissenschaft / Powered by litecat, BIS Oldenburg (Stand: 04. Juni 2021)
1Aker, A. ; Gaizauskas, R.: Generating descriptive multi-document summaries of geo-located entities using entity type models.
In: Journal of the Association for Information Science and Technology. 66(2015) no.4, S.721-738.
Abstract: In this article, we investigate the application of entity type models in extractive multi-document summarization using automatic caption generation for images of geo-located entities (e.g., Westminster Abbey) as an application scenario. Entity type models contain sets of patterns aiming to capture the ways geo-located entities are described in natural language. They are automatically derived from texts about geo-located entities of the same type (e.g., churches, lakes). We integrate entity type models into a multi-document summarizer and use them to address the 2 major tasks in extractive multi-document summarization: sentence scoring and summary composition. We experiment with 3 different representation methods for entity type models: signature words, n-gram language models, and dependency patterns. We evaluate the summarizer with integrated entity type models relative to (a) a summarizer using standard text-related features commonly used in text summarization and (b) the Wikipedia location descriptions. Our results show that entity type models significantly improve the quality of output summaries over that of summaries generated using standard summarization features and Wikipedia summaries. The representation of entity type models using dependency patterns is superior to the representations using signature words and n-gram language models.
Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23211/abstract.
2Aker, A. ; Plaza, L. ; Lloret, E. ; Gaizauskas, R.: Do humans have conceptual models about geographic objects? : a user study.
In: Journal of the American Society for Information Science and Technology. 64(2013) no.4, S.689-700.
Abstract: In this article, we investigate what sorts of information humans request about geographical objects of the same type. For example, Edinburgh Castle and Bodiam Castle are two objects of the same type: "castle." The question is whether specific information is requested for the object type "castle" and how this information differs for objects of other types (e.g., church, museum, or lake). We aim to answer this question using an online survey. In the survey, we showed 184 participants 200 images pertaining to urban and rural objects and asked them to write questions for which they would like to know the answers when seeing those objects. Our analysis of the 6,169 questions collected in the survey shows that humans have shared ideas of what to ask about geographical objects. When the object types resemble each other (e.g., church and temple), the requested information is similar for the objects of these types. Otherwise, the information is specific to an object type. Our results may be very useful in guiding Natural Language Processing tasks involving automatic generation of templates for image descriptions and their assessment, as well as image indexing and organization.
Behandelte Form: Bilder
4Gaizauskas, R. ; Wilks, Y.: Information extraction : beyond document retrieval.
In: Journal of documentation. 54(1998) no.1, S.70-105.
Abstract: In this paper we give a synoptic view of the growth of the text processing technology of informatione xtraction (IE) whose function is to extract information about a pre-specified set of entities, relations or events from natural language texts and to record this information in structured representations called templates. Here we describe the nature of the IE task, review the history of the area from its origins in AI work in the 1960s and 70s till the present, discuss the techniques being used to carry out the task, describe application areas where IE systems are or are about to be at work, and conclude with a discussion of the challenges facing the area. What emerges is a picture of an exciting new text processing technology with a host of new applications, both on its own and in conjunction with other technologies, such as information retrieval, machine translation and data mining
Inhalt: Vgl. auch unter: http://www.emeraldinsight.com/10.1108/EUM0000000007162.
Themenfeld: Data Mining