SAS Global 2021 Introduction to Natural Language Processing
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The document introduces natural language processing (NLP) and its applications in various fields, including legal, clinical, and social media contexts. It discusses tools and techniques for understanding and analyzing text data, including sentiment analysis and machine learning features. The document also mentions popular NLP software and recent literature related to the field.
SAS Global 2021 Introduction to Natural Language Processing
- 2. Natural Language Processing—An Introduction Colleen M. Farrelly, Staticlysm Brief bio – Colleen M. Farrelly is a machine learning scientist whose expertise includes supervised learning, unsupervised learning, psychometrics, topological data analysis, and natural language processing. She has an analytics book in review that touches upon the analysis of text data with topological data analysis tools.
- 3. Introduction
- 4. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. Text Data and Applications • What do all of these have in common? • Clinical case notes • Chatbot conversations • Client email interactions • Court case summaries/transcripts • Published research articles • Tweets • Voice recordings
- 5. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. Text Data and Applications • Commonalities • Text data • Contain potentially- informative features for predicting an outcome or categorizing data • May contain information not available in structured datasets • Linguistic insight on the speaker/writer
- 6. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. Example Legal • Imagine both the witness and the robber in these two examples. • How might these observations impact the outcome of a police investigation? • Statement 1: • She pulled the gun, took the money, and ran. • Statement 2: • The petite blonde pulled a shotgun on the clerk at station 2, filled a bag with cash from the register, and absconded with the money and a handful of pens. • How many suspects might the police have to stop to find Bonnie and Clyde? Which witness statement might have more impact on a jury? • How might differences in clinical case notes by clinicians inform health outcome models? How might they reflect on the individual clinician?
- 7. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. Making Sense of Text Data • Natural language processing (NLP) • Collection of tools to parse human language into something understandable by algorithms • What is said • Computational linguistics • Deriving insight about human behavior or traits based on text data • How it’s said
- 8. Common NLP Tools An Overview
- 9. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. Parsing Documents/Sentences An Example • Tokens (words or punctuation) • Punctuation (non-word tokens) • Stop words (less important words) • Root words (stemming/lemmatizing) Bonnie hopped into Clyde’s new car.
- 10. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. Tagging Features • Parts of speech • Clauses • Grammatical relations • Entity recognition Bonnie hopped into Clyde’s new car.
- 11. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. Deriving Sentiment • Language-dependent • Sentiment dictionaries • Positive/negative/neutral (afinn, for instance) • Emotion groups from psychological models Bonnie hopped into Clyde’s new car.
- 12. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. Vectorizing/Summarizing Results • Many options for turning NLP results into usable data in machine learning and statistical tools: • Vectorization • Word frequency matrices • Summary tables Bonnie hopped into Clyde’s new car.
- 13. Using Statistical Tools to Understand NLP An Overview
- 14. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. Summary Statistics • Common summary statistic uses 1. Conversation length (example: engagement metric) 2. Swear count (example: escalation marker) 3. Conversation sentiment over time (example: engagement and satisfaction) 4. Key word frequency (example: products with most issues)
- 15. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. Use as Machine Learning Features • Examples combining NLP data with data from structured databases 1. Clustering (example: types of churn from client feedback and account data) 2. Predictive modeling (example: patient outcomes from case notes and medical records)
- 16. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. Psychometric Applications • Some published papers: 1. Personality trait identification in industrial psychology research 2. Author identification in plagiarism software 3. Quantification of release risk in justice systems 4. Quantification of relapse risk in mental health applications
- 17. Other Uses of NLP
- 18. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. Other Common NLP Applications • Chatbots • Personal assistants • Translation services • Sentence completion
- 19. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. In General
- 21. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. Main NLP Software Options • NLTK (Python) • spaCy (Python) • Stanford CoreNLP (Java) • John Snow Labs/Spark NLP (Spark)
- 22. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. Some NLP Literature • Dunnmon, J. A., Ratner, A. J., Saab, K., Khandwala, N., Markert, M., Sagreiya, H., ... & Ré, C. (2020). Cross-modal data programming enables rapid medical machine learning. Patterns, 100019. • Maas, A., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., & Potts, C. (2011, June). Learning word vectors for sentiment analysis. In Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies (pp. 142-150). • Pennebaker, J. W. (2011). The secret life of pronouns. New Scientist, 211(2828), 42-45. • Polsley, S., Jhunjhunwala, P., & Huang, R. (2016, December). Casesummarizer: a system for automated summarization of legal texts. In Proceedings of COLING 2016, the 26th international conference on Computational Linguistics: System Demonstrations (pp. 258-262). • Velupillai, S., Suominen, H., Liakata, M., Roberts, A., Shah, A. D., Morley, K., ... & Chapman, W. (2018). Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances. Journal of biomedical informatics, 88, 11-19.