How computers understand text content - by Anna Divoli
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The document is a presentation by Anna Divoli on how computers understand text, focusing on various aspects of natural language processing (NLP) and its applications. It covers topics such as named entity recognition, sentiment analysis, and challenges posed by ambiguities in human language. The presentation emphasizes the importance of machine learning and hybrid systems for effective information extraction and highlights the interaction between human language and technology.
How computers understand text content - by Anna Divoli
- 1. How computers understand text content a presentation for the Auckland content strategy meetup by Anna Divoli @annadivoli . Ph.D. in Biomedical Text Mining | Text Analytics Researcher | Head of R&D at Pingar
- 2. Who am I? • 14 years in academia + 4 years in industry • academically exposed to different disciplines: biomedicine, bioinformatics, computational linguistics, information retrieval, information extraction, semantic technologies, human-computer interaction, search user interface usability, knowledge acquisition, visualizations • lived in different countries: Greece, UK, US, NZ • learned English as a second language (hint: I empathize with computer systems) Anna Divoli Auckland content strategy meetup Aug 2015
- 3. Who are you? • Marketing? • Digital content? • Information Architecture? • Journalists? • UX? • Business Analysis? • Software Development? • CS research (incl. “text” people)? • Other? Anna Divoli Auckland content strategy meetup Aug 2015
- 4. What is “text”? Where is it? www.nailingit.com/images/websites.jpg www.bu.edu/today/files/2012/10/t_journals1.jpgweb.clarku.edu/offices/its/images/filepile.jpg www.flickr.com/photos/jlconfor/14191286471
- 5. Human – Text Content Interaction Humans: Slow, Inconsistent, Expensive Text content: Overwhelmingly fast growing, Disseminated across multiple sources Anna Divoli Auckland content strategy meetup Aug 2015
- 6. NLP ∈ Artificial Intelligence Machine Learning NLP Computational Linguistics Applied Text Analytics Storage Memory Security Friendly UIs Visualizations Anna Divoli Auckland content strategy meetup Aug 2015
- 7. So, what’s in the text? • Entities • Facts • Relations • Themes/topics • Opinions & sentiment • … + Time/Location dimensions: • Trends & paradigm shifts • Networks • … Anna Divoli Auckland content strategy meetup Aug 2015
- 8. Named Entity Recognition Find and classify names… S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd offices last month. John Smith went to Washington to see the Smithsonian and also met up with Virginia for a coffee. Anna Divoli Auckland content strategy meetup Aug 2015
- 9. Named Entity Recognition Find and classify names… S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd offices last month. John Smith went to Washington to see the Smithsonian and also met up with Virginia for a coffee. People Locations Organizations Methods: lexicon-based (gazeteers) grammar-based (rule-based) ✓ statistical models (machine learning: algorithms + features) ✓ hybrids Anna Divoli Auckland content strategy meetup Aug 2015
- 10. Named Entity Recognition Find and classify names… S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd offices last month. John Smith went to Washington to see the Smithsonian and also met up with Virginia for a coffee. People Dates Locations Organizations Who? Where? When? Anna Divoli Auckland content strategy meetup Aug 2015
- 11. Disambiguation & Normalization: Word Sense Disambiguation & Text Normalization S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd offices last month. John Smith went to Washington to see the Smithsonian and also met up with Virginia for a coffee. Word Sense Disambiguation: identifying which sense/meaning of a word is used in a sentence, when the word has multiple meanings. Synonyms & homonyms. Use context!! Text normalization: transforming text into a single canonical form that it might not have had before. Anna Divoli Auckland content strategy meetup Aug 2015
- 12. Word Sense Disambiguation & Text Normalization S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd offices last month. Sam Arlington initiated partnership discussions during his visit to Eureka offices in July. John Smith went to Washington to see the Smithsonian and also met up with Virginia for a coffee. J. Smith went to Washington DC to see the Smithsonian Institute and also met up with Virginia Peterson for a coffee. Anna Divoli Auckland content strategy meetup Aug 2015
- 13. S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd offices last month. Sam Arlington initiated partnership discussions during his visit to Eureka office in July. John Smith went to Washington to see the Smithsonian and also met up with Virginia for a coffee. J. Smith went to Washington DC to see the Smithsonian Institute and also met up with Virginia Peterson for a coffee. Word Sense Disambiguation & Text Normalization Anna Divoli Auckland content strategy meetup Aug 2015
- 14. Fact & Relationship extraction S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd offices last month. John Smith went to Washington to see the Smithsonian and also met up with Virginia for a coffee. What? Anna Divoli Auckland content strategy meetup Aug 2015
- 15. Deeper knowledge & Sentiment S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd offices last month. John Smith went to Washington to see the Smithsonian and also met up with Virginia for a coffee. How? Why? How do we feel about it? S. Arlington visited the Eureka’s Ltd offices last month to initiate partnership discussions. John Smith was delighted to go to Washington to see the Smithsonian and also met up with Virginia for a coffee. Anna Divoli Auckland content strategy meetup Aug 2015
- 16. Sentiment analysis & opinion mining • Dictionary-based (e.g. LIWC) • Statistical • Hybrid • Polarity & strength • Feelings • Mood • Aspects • Who has this sentiment (source) • What is the target of the sentiment Pos | Neu | Neg & score Angry, sad… Happy, depressed… Location, cleanliness… Employees, customers… Product, event, person… Anna Divoli Auckland content strategy meetup Aug 2015
- 17. So, what’s in the text? Anna Divoli Auckland content strategy meetup Aug 2015 • Entities • Facts • Relations • Themes/topics no training or ontologies need! can utilize web resources (e.g., Wikipedia) • Opinions & sentiment • … + Time/Location dimensions: • Trends & paradigm shifts • Networks • …
- 18. So, what ELSE is in the text? • Ambiguity • Metaphors • Sarcasm • Colloquialism/Slang • Negation • Hedging • Conditional statements • Inconsistencies/Bad grammar • Text speak • Anaphora • Humor I want an apple. He drowned in a sea of grief. George W Bush. Love him! I slept like crap last night. I am not sure I want to go to NYC. The results indicate this. When it rains I feel sad. I think your smart. C u l8r @Jacks John met with Nick. He was upset. Did you take a bath today? No. Is one missing? Anna Divoli Auckland content strategy meetup Aug 2015
- 19. So, what ELSE is in the text? • Ambiguity • Metaphors • Sarcasm • Colloquialism/Slang • Negation • Hedging • Conditional statements • Inconsistencies/Bad grammar • Text speak • Anaphora • Humor I want an apple. He drowned in a sea of grief. George W Bush. Love him! I slept like crap last night. I am not sure I want to go to NYC. The results indicate this. When it rains I feel sad. I think your smart. C u l8r @Jacks John met with Nick. He was upset. Did you take a bath today? No. Is one missing? Consider: distributed information (dialogue), technical/scientific text, legal text, creative/poetry… Anna Divoli Auckland content strategy meetup Aug 2015
- 20. Human language! Eye drops off shelf. Include your children when baking cookies. Turn right here. John saw the man on the mountain with a telescope. He gave her cat food. They are hunting dogs. Anna Divoli Auckland content strategy meetup Aug 2015
- 21. Examples: Biology… Looking for: interactions between SAF and viral LTR elements (SAF is a transcription factor, LTR stands for ‘long terminal repeat’) (Also: SAF = single and free, LTR = long term relationship) Gene names: tinman, lilliputian, dreadlocks, lush, cheap date, methuselah, Van Gogh, maggie, brainiac, grim, reaper, cleopatra, swiss cheese, fucK, out cold, ken and barbie, kenny, lava lamp, hamlet, sonic hedgehog, werewolf, half pint, drop dead, chardonnay, agnostic, I’m not dead yet… Anna Divoli Auckland content strategy meetup Aug 2015
- 22. Current State of NLP • Rule-based systems for high precision results • Hybrid systems for more robust performance (rules + dictionaries/ontologies + statistical models) • Limitation: specialized systems perform better (much like humans!) • Workflows offer work-around for more generic systems e.g., check language check category choose model Anna Divoli Auckland content strategy meetup Aug 2015
- 23. Examples of applications (some are very specialized!) Anna Divoli Auckland content strategy meetup Aug 2015
- 30. Content Enrichment Content Inventory Content Intelligence
- 34. Take home messages • Machines can do a lot of consistent, fast information extraction • Specialization is needed in several fields but systems can have internal workflows • Big data + statistics = magic! • Always room for improvement • Information management AND decisions AND predictions
- 35. Time for questions and discussion! https://xkcd.com/1263/ Anna Divoli Auckland content strategy meetup Aug 2015 @annadivoli .
Editor's Notes
- #6: We create and consume text!