Named entity recognition (NER) (also known as entity identification (EI) and entity extraction) is a subtask of information extraction that seeks to locate and classify atomic elements in text into predefined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc.
Most research on NER systems has been structured as taking an unanotated block of text, such as this one:
Jim bought 300 shares of Acme Corp. in 2006.
and producing an annotated block of text, such as this one:
<ENAMEX TYPE="PERSON">Jim</ENAMEX> bought
<NUMEX TYPE="QUANTITY">300</NUMEX> shares of
<ENAMEX TYPE="ORGANIZATION">Acme Corp.</ENAMEX> in
Does anyone reading this work on this or other natural language processing (NLP) and, more generally, Human Language Technology (HLT) tasks such as machine translation, information extraction (IE) including gist summarization, modeling of second language acquisition or foreign language acquisition, and textual entailment?