Aidan Finn, Nicholas Kushmerick, and Barry Smyth. Genre classification and domain transfer for information filtering. In Fabio Crestani, Mark Girolami, and Cornelis J. van Rijsbergen, editors, Proceedings of ECIR-02, 24th European Colloquium on Information Retrieval Research, Glasgow, UK. Springer Verlag, Heidelberg, DE. Home/Search Document Details and Download
Summary Related Articles Check |
This paper is cited in the following contexts:
Mining the Peanut Gallery: Opinion Extraction and.. - Dave, Lawrence, Pennock (2003) (4 citations) (Correct)
....in other applications. Both of these tasks draw on work done finding the semantic orientation of words. 2.1 Objectivity classification The task of separating reviews from other types of content is a genre or style classification problem. It involves identifying subjectivity, which Finn et al. [3] attempted to do on a set of articles spidered from the web. A classifier based on the relative frequency of each part of speech in a document outperformed bag of words and custom built features. But determining subjectivity can be, well, subjective. Wiebe et al. 25] studied manual annotation of ....
Aidan Finn, Nicholas Kushmerick, and Barry Smyth. Genre classification and domain transfer for information filtering. In Fabio Crestani, Mark Girolami, and Cornelis J. van Rijsbergen, editors, Proceedings of ECIR-02, 24th European Colloquium on Information Retrieval Research, Glasgow, UK. Springer Verlag, Heidelberg, DE.
User Assessment of a Visual Web Genre Classifier - Dimitrova, Kushmerick.. (2003) Self-citation (Kushmerick) (Correct)
No context found.
. A. Finn, N. Kushmerick, & B. Smyth, Genre classification and domain transfer for information filtering, Proc. European Colloquium on Information Retrieval Research, Glasgow, 2002, 353-362.
Gleaning Answers From the Web - Kushmerick Self-citation (Kushmerick) (Correct)
....whether a product review is positive or negative , whether an article is detailed or superficial , the amount of technical knowledge assumed by a document s author, etc. Existing text classification algorithms exploit words that reliably indicate the correct class. Our investigation [5, 6, 4] suggests that such term based techniques are inaccurate for our subjective tasks. Instead, we use shallow natural language processing techniques such as part of speech tagging and estimates of term topicality to derive document features that yield accurate classification. Our experiments in the ....
A. Finn, N. Kushmerick, and B. Smyth. Genre classification and domain transfer for information filtering. In Proc. European Colloqium on Information Retrieval Research, 2002.
Mining the Peanut Gallery: Opinion Extraction and.. - Dave, Lawrence, Pennock (2003) (4 citations) (Correct)
No context found.
Aidan Finn, Nicholas Kushmerick, and Barry Smyth. Genre classification and domain transfer for information filtering. In Fabio Crestani, Mark Girolami, and Cornelis J. van Rijsbergen, editors, Proceedings of ECIR-02, 24th European Colloquium on Information Retrieval Research, Glasgow, UK. Springer Verlag, Heidelberg, DE.
Text Categorization - Sebastiani (2005) (2 citations) (Correct)
No context found.
Finn, A., Kushmerick, N. & Smyth, B., Genre classification and domain transfer for information filtering. Proceedings of ECIR-02, 24th European Colloquium on Information Retrieval Research, eds. F. Crestani, M. Girolami & C.J.V. Rijsbergen, Springer Verlag, Heidelberg, DE: Glasgow, UK, pp. 353--362, 2002. Published in the "Lecture Notes in Computer Science" series, number 2291.
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC