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Named Entity Recognition
IA161 Advanced NLP Course, Course Guarantee: Aleš Horák
Prepared by: Zuzana Nevěřilová
State of the Art
NER aims to recognize and classify names of people, locations, organizations, products, artworks, sometimes dates, money, measurements (numbers with units), law or patent numbers etc. Known issues are ambiguity of words (e.g. May can be a month, a verb, or a name), ambiguity of classes (e.g. HMS Queen Elisabeth can be a ship), and the inherent incompleteness of lists of NEs.
Named entity recognition (NER) is used mainly in information extraction (IE) but it can significantly improve other NLP tasks such as syntactic parsing.
Example from IE
In 2003, Hannibal Lecter (as portrayed by Hopkins) was chosen by the American Film Institute as the #1 movie villain.
Hannibal Lecter <-> Hopkins
Example concerning syntactic parsing
Wish You Were Here is the ninth studio album by the English progressive rock group Pink Floyd.
vs.
Wish_You_Were_Here is the ninth studio album by the English progressive rock group Pink Floyd.
References
- David Nadeau, Satoshi Sekine: A survey of named entity recognition and classification. In Satoshi Sekine and Elisabete Ranchhod (eds.) Named Entities: Recognition, classification and use. Lingvisticæ Investigationes 30:1. 2007. pp. 3–26 http://brown.cl.uni-heidelberg.de/~sourjiko/NER_Literatur/survey.pdf
- Charles Sutton and Andrew McCallum: An Introduction to Conditional Random Fields. Foundations and Trends in Machine Learning 4 (4). 2012. http://homepages.inf.ed.ac.uk/csutton/publications/crftut-fnt.pdf
Practical Session
Try naive gazetteer method (implement substring search) on prepared data. Observe the results:
- what happens to every string present in the gazetteer?
- what happens to NE not present in the gazetteer?
Try machine learning approach (use the Stanford NER) with prepared data. Observe the results:
- measure precision, recall, and F1-score on the test data
- find NEs not present in the train data
- find NEs that were not recognized
- discuss what types of NE are easy/difficult to recognize
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- cnec.prop (351 bytes) - added by 8 years ago.
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