The document is an agenda for a crash course on natural language processing (NLP), outlining its definition, importance, and various task areas. It details major applications like speech recognition, named entity recognition, and machine translation, as well as key techniques and resources in the field. Additionally, it provides pro tips for practitioners and lists relevant resources including toolkits and literature.

1. NLP “Crash Course”
Charlie Greenbacker
dcnlp.org

2. Agenda
• Introduction & Motivation
• Famous Examples
• Basics
• Major Task Areas
• Protips
• Resources

3. Introduction
& Motivation
By “NLP” we mean...
Natural Language Processing
(#NLProc)
aka Computational Linguistics,Text Analytics, etc.
not Neuro-linguistic Programming! (#NLP)

4. Introduction
& Motivation
Natural Language Processing is...
Using computers to process (i.e., analyze,
understand, generate, etc.) natural human
languages (e.g., English, Chinese, Klingon).
Hello, world! 你好，世界！

5. That sounds hard... why should I care?
• Most of the knowledge created by humans
is unstructured text (information overload)
• Need some way to make sense of it all
• Enable quantitative analysis of text data
Introduction
& Motivation

6. Famous Examples
Siri (Apple, SRI, Nuance)
Speech Recognition/Generation
IBM Watson
Question Answering
Google Translate
MachineTranslation

7. Basics
• Segmentation
• Part-of-speech tagging
• Noun phrase (NP) chunking
• Parsing
• Word sense disambiguation

8. Basics
• Stop words, stemming/lemmatization
• Frequency analysis
(terms, ngrams,TF-IDF)
• Machine learning (classification,
clustering, recommendation)

9. Major Task Areas
Question Answering
• Match query with knowledge base
• Closed domain vs open domain
• Reasoning about intent of question

10. Major Task Areas
Speech Recognition
• Speech to text
• Trained/untrained user models
• Voice-based interfaces

11. Major Task Areas
Named Entity Recognition
• Entity extraction
• Persons, organizations, location
• Grammar, syntax, phrasing

12. Major Task Areas
Entity Resolution
• Linking names to ground truth
• Disambiguating similar names

13. Major Task Areas
Co-reference Resolution
• Finding antecedents for pronouns
• Name resolution

14. Major Task Areas
Relationship Extraction
• Attribute values
• SVO triples
• Populating ontologies

15. Major Task Areas
Information Retrieval
• Query expansion
• Relevancy of results
• “More like this”

16. Major Task Areas
Assistive Technologies
• Text simplification
• Predictive text input
• Alternative interfaces

17. Major Task Areas
NLG + Automatic Summarization
• Generating text from data
• Extractive summarization
• Abstractive summarization

18. Major Task Areas
Machine Translation
• From source to target, and back!
• Single terms work... sometimes
• Idioms, metaphors, cultural references

19. Major Task Areas
Sentiment Analysis
• Polarity, intensity, direction
• "Easy" for movie/product reviews
• "Impossible" for nearly anything else

20. Protips
• Domain adaptation
(retrain your models, social media != news)
• Assume everything is in beta
(error rates compound, translate last,
consult the research literature)
• Evaluation is essential
(human judges,“gold standard” data,
cross-validation, appropriate metrics)

21. Resources
(toolkits)
Stanford CoreNLP
Java, GPL
Apache OpenNLP
Java,Apache License
NLTK
Python,Apache License

22. Resources
(books)
Natural Language
Processing with Python
Bird, Klein, and Loper
Speech and Language______________
Processing______________
Jurafsky and Martin______________
Foundations of Statistical
Natural Language Processing
Manning and Schütze

23. Resources
(groups)
ACL (Association for
Computational Linguistics)
Conferences,Workshops, Journals, SIGs
DC NLP
NLP Meetups
Data Community DC
NLPWorkshops

24. Questions?
Charlie Greenbacker
dcnlp.org
@greenbacker