Markov model), ME (maximum entropy), and CRF (conditional random fields), and feature sets. And some researchers recently proposed graph-based semi-supervised learning model for language specific NER tasks. A recently emerging task of identifying "important expressions" in text and cross-linking them to Wikipedia can be seen as an instance of extremely fine-grained named-entity recognition, where the types are the actual Wikipedia pages describing the (potentially ambiguous) concepts. Below is an example output of a Wikification system: <ENTITY url="https://en.wikipedia.org/wiki/Michael_I._Jordan"> Michael Jordan </ENTITY> is a professor at <ENTITY url="https://en.wikipedia.org/wiki/University_of_California,_Berkeley"> Berkeley </ENTITY> Another field that has seen progress but remains challenging is the application of NER to Twitter and other microblogs, considered "noisy" due to non-standard orthography, shortness and informality of texts. NER challenges in English Tweets have been organized by research communities to compare performances of various approaches, such as bidirectional LSTMs, Learning-to-Search, or CRFs. See also Controlled vocabulary Coreference resolution Entity linking (aka named entity normalization, entity disambiguation) Information extraction Knowledge extraction Onomastics Record linkage Smart tag (Microsoft) References Computational linguistics Tasks of natural language processing