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| Content Provider | PubMed Central |
|---|---|
| Author | Williams, Jake Ryland Lessard, Paul R. Desu, Suma Clark, Eric M. Bagrow, James P. Danforth, Christopher M. Dodds, Peter Sheridan |
| Copyright Year | 2015 |
| Abstract | With Zipf’s law being originally and most famously observed for word frequency, it is surprisingly limited in its applicability to human language, holding over no more than three to four orders of magnitude before hitting a clear break in scaling. Here, building on the simple observation that phrases of one or more words comprise the most coherent units of meaning in language, we show empirically that Zipf’s law for phrases extends over as many as nine orders of rank magnitude. In doing so, we develop a principled and scalable statistical mechanical method of random text partitioning, which opens up a rich frontier of rigorous text analysis via a rank ordering of mixed length phrases. |
| Related Links | http://dx.doi.org/10.1038/srep12209 |
| Starting Page | 12209 |
| File Format | |
| ISSN | 20452322 |
| e-ISSN | 20452322 |
| Journal | Scientific Reports |
| Volume Number | 5 |
| Language | English |
| Publisher | Nature Publishing Group |
| Publisher Date | 2015-08-01 |
| Access Restriction | Open |
| Rights Holder | Nature Publishing Group |
| Subject Keyword | Science and technology Research in Higher Education |
| Content Type | Text |
| Resource Type | Article |
| Subject | Multidisciplinary |
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