Changes between Version 2 and Version 3 of private/NlpInPracticeCourse/NamedEntityRecognition

Timestamp:

Jul 23, 2015, 4:30:29 PM (9 years ago)

Author:

Zuzana Nevěřilová

Comment:

--

private/NlpInPracticeCourse/NamedEntityRecognition

@@ -12 +12 @@
 == 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 ===
 
@@ -20 +38 @@
 
 Try naive gazetteer method (implement substring search) on prepared data. 
-Observe the recognition: 
+Observe the results: 
   1. what happens to every string present in the gazetteer?
   1. what happens to NE not present in the gazetteer?
 
 Try machine learning approach (use the Stanford NER) with prepared data.
-Observe the recognition:
+Observe the results:
   1. measure precision, recall, and F1-score on the test data
   1. find NEs not present in the train data