Abstract
Information Retrieval systems spend a great effort on determining the significant terms in a document. When, instead, a user is looking at a document he cannot benefit from such information. He has to read the text to understand which words are important. In this paper we take a look at the idea of enhancing the perception of web documents with visualisation techniques borrowed from the tag clouds of Web 2.0. Highlighting the important words in a document by using a larger font size allows to get a quick impression of the relevant concepts in a text. As this process does not depend on a user query it can also be used for explorative search. A user study showed, that already simple TF-IDF values used as notion of word importance helped the users to decide quicker, whether or not a document is relevant to a topic.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Krug, S.: Don’t make me think – Web Usability, 2nd edn. mitp, Heidelberg (2006)
Lindgaard, G., Fernandes, G., Dudek, C., Browñ, J.: Attention web designers: You have 50 milliseconds to make a good first impression! Behaviour & Information Technology 25(2), 115–126 (2005)
Tombros, A., Ruthven, I., Jose, J.M.: How users assess web pages for information seeking. J. Am. Soc. Inf. Sci. Technol. 56(4), 327–344 (2005)
Ogden, W.C., Davis, M.W., Rice, S.: Document thumbnail visualization for rapid relevance judgments: When do they pay off? In: TREC, pp. 528–534 (1998)
Dziadosz, S., Chandrasekar, R.: Do thumbnail previews help users make better relevance decisions about web search results? In: SIGIR 2002: Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 365–366. ACM, New York (2002)
Noll, M.G., Meinel, C.: Exploring social annotations for web document classification. In: SAC 2008: Proceedings of the 2008 ACM symposium on Applied computing, pp. 2315–2320. ACM, New York (2008)
Bateman, S., Gutwin, C., Nacenta, M.: Seeing things in the clouds: the effect of visual features on tag cloud selections. In: HT 2008: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia, pp. 193–202. ACM, New York (2008)
Viégas, F.B., Wattenberg, M.: Tag clouds and the case for vernacular visualization. Interactions 15(4), 49–52 (2008)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)
Schönhofen, P.: Identifying document topics using the wikipedia category network. In: WI 2006: Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 456–462. IEEE Computer Society Press, Los Alamitos (2006)
Gottron, T.: Content code blurring: A new approach to content extraction. In: Bhowmick, S.S., Küng, J., Wagner, R. (eds.) DEXA 2008. LNCS, vol. 5181, pp. 29–33. Springer, Heidelberg (2008)
Gottron, T.: An evolutionary approach to automatically optimise web content extraction. In: IIS 2009: Proceedings of the 17th International Conference Intelligent Information Systems (in preparation, 2009)
Porter, M.F.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gottron, T. (2009). Document Word Clouds: Visualising Web Documents as Tag Clouds to Aid Users in Relevance Decisions. In: Agosti, M., Borbinha, J., Kapidakis, S., Papatheodorou, C., Tsakonas, G. (eds) Research and Advanced Technology for Digital Libraries. ECDL 2009. Lecture Notes in Computer Science, vol 5714. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04346-8_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-04346-8_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04345-1
Online ISBN: 978-3-642-04346-8
eBook Packages: Computer ScienceComputer Science (R0)