%0 Report %1 citeulike:771872 %A Ratnaparkhi, A. %D 1997 %K nlp statistics %T A simple introduction to maximum entropy models for natural language processing %U http://citeseer.ist.psu.edu/128751.html %X Many problems in natural language processing can be viewed as linguistic classification problems, in which linguistic contexts are used to predict linguistic classes. Maximum entropy models offer a clean way to combine diverse pieces of contextual evidence in order to estimate the probability of a certain linguistic class occurring with a certain linguistic context. This report demonstrates the use of a particular maximum entropy model on an example problem, and then proves some relevant...

@techreport{citeulike:771872, abstract = {Many problems in natural language processing can be viewed as linguistic classification problems, in which linguistic contexts are used to predict linguistic classes. Maximum entropy models offer a clean way to combine diverse pieces of contextual evidence in order to estimate the probability of a certain linguistic class occurring with a certain linguistic context. This report demonstrates the use of a particular maximum entropy model on an example problem, and then proves some relevant...}, added-at = {2008-02-10T02:19:38.000+0100}, author = {Ratnaparkhi, A.}, biburl = {https://www.bibsonomy.org/bibtex/261d3b2424b33e970c306318df9f1ccb6/brightbyte}, citeulike-article-id = {2162753}, description = {stuff from citeyoulike}, institution = {nstitute for Research in Cognitive Science, University of Pennsylvania}, interhash = {7e5db81c85cba9fbe5e2087db667613a}, intrahash = {61d3b2424b33e970c306318df9f1ccb6}, keywords = {nlp statistics}, priority = {2}, timestamp = {2009-01-23T09:58:50.000+0100}, title = {A simple introduction to maximum entropy models for natural language processing}, url = {http://citeseer.ist.psu.edu/128751.html}, year = 1997 }