Changes between Version 26 and Version 27 of private/NlpInPracticeCourse/NamedEntityRecognition

Timestamp:

Sep 26, 2023, 11:23:11 AM (7 months ago)

Author:

Zuzana Nevěřilová

Comment:

--

private/NlpInPracticeCourse/NamedEntityRecognition

@@ -7 +7 @@
 == 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.
+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 the ambiguity of words (e.g., ''May'' can be a month, a verb, or a name), the 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.
+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 ===
@@ -34 +34 @@
 === Multilingual Named Entity Recognition ===
 
-In this workshop, we train a NER model for any of the languages supported by WikiAnn. We work with the huggingface library, its BERT model for multilingual token classification, and the WikiAnn training data.
+In this workshop, we train a NER model for any languages !WikiAnn supports. We work with the huggingface library, its BERT model for multilingual token classification, and the !WikiAnn training data.
 
 1. Create `<YOUR_FILE>`, a text file named `ia161-UCO-04.txt` where ''UCO'' is your university ID.