Difference between revisions of "Long tail"
m (Text replace - "<h2> Datasets </h2> There is no datasets for this keyword. <h2> Tools </h2> There is no tools for this keyword. <br clear="all" /> <h2> Publications </h2> There is no publications with this keyword." to "")
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Latest revision as of 17:48, March 10, 2013
| Long tail|
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Long tail is included as keyword or extra keyword in 0 datasets, 0 tools and 2 publications.
There is no datasets for this keyword.
There is no tools for this keyword.
|Title||Author(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|No noun phrase left behind: Detecting and typing unlinkable entities||Lin T.
|EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference||English||2012||Entity linking systems link noun-phrase mentions in text to their corresponding Wikipedia articles. However, NLP applications would gain from the ability to detect and type all entities mentioned in text, including the long tail of entities not prominent enough to have their own Wikipedia articles. In this paper we show that once the Wikipedia entities mentioned in a corpus of textual assertions are linked, this can further enable the detection and fine-grained typing of the unlinkable entities. Our proposed method for detecting un-linkable entities achieves 24% greater accuracy than a Named Entity Recognition baseline, and our method for fine-grained typing is able to propagate over 1,000 types from linked Wikipedia entities to unlinkable entities. Detection and typing of unlinkable entities can increase yield for NLP applications such as typed question answering.||0||0|
|Lurking? Cyclopaths? A quantitative lifecycle analysis of user behavior in a geowiki||Katherine Panciera
|Conference on Human Factors in Computing Systems - Proceedings||English||2010||Online communities produce rich behavioral datasets, e.g., Usenet news conversations, Wikipedia edits, and Facebook friend networks. Analysis of such datasets yields important insights (like the "long tail" of user participation) and suggests novel design interventions (like targeting users with personalized opportunities and work requests). However, certain key user data typically are unavailable, specifically viewing, pre-registration, and non-logged-in activity. The absence of data makes some questions hard to answer; ac- cess to it can strengthen, extend, or cast doubt on previous results. We report on analysis of user behavior in Cyclopath, a geographic wiki and route-finder for bicyclists. With access to viewing and non-logged-in activity data, we were able to: (a) replicate and extend prior work on user lifecycles in Wikipedia, (b) bring to light some pre-registration activity, thus testing for the presence of "educational lurking," and (c) demonstrate the locality of geographic activity and how editing and viewing are geographically correlated.||0||0|