1. LING 388: Computers and Language
Lecture 10

2. Class exercises with TA last week
Some feedback: how did it go?

3. Named Entity Recognition (NER)
Jurafsky & Martin (JM) textbook on Speech and Language Processing
Used in LING 438/538 course in Fall
See JM Chapter 22: Information Extraction
22.1 Named Entity Recognition
22.2 Relation Detection and Classification
also Chapter 21 for Anaphora Resolution

4. Named Entity Recognition (NER)
(also Identification and Extraction)
tries 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.
[paraphrased from entity_recognition]

5. Illinois NER System
Website:

6. Example
WSJ9_002.txt

7. Illinois NER System NLP systems might also compute: anaphora reference

8. JM Chapter 22

9. JM Chapter 22

10. JM Chapter 22
Ambiguity: sometimes systematic, sometimes not

11. Illinois NER system
On the ambiguous examples, so-so performance:

12. JM Chapter 22
Word by word labeling (IOB “inside outside beginning”)

13. JM Chapter 22
POS information
Shape
Syntactic
chunking

14. JM Chapter 22

15. JM Chapter 22
What features to use in making a decision (used also for Machine Learning)?