Natural Language Processing Crash Course
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Charlie Greenbacker, founder and co-organizer of the DC NLP meetup group, provides a "crash course" in Natural Language Processing techniques and applications.
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Natural Language Processing Crash Course
- 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