Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

David Israel (SRI Intl) on "Natural Language Processing"

902 views

Published on

David Israel (SRI Intl) on "Natural Language Processing" at a LASER http://www.scaruffi.com/leonardo/aug2013.html

Published in: Technology, Spiritual
  • Be the first to comment

  • Be the first to like this

David Israel (SRI Intl) on "Natural Language Processing"

  1. 1. David Israel Artificial Intelligence Center SRI International August 8, 2013
  2. 2.  AI as Cognitive Science by Other Means  How do people do what they do  AI as focused on emulating intelligence artificially… by whatever means necessary  AI as a design and engineering discipline, not an empirical science  AI as Applied Logic (in weird disguise?)  Central focus: Representation & Reasoning  AI as Applied Probability Theory/Statistics  Central focus: (Machine) Learning from data
  3. 3.  Distinctively Human (?) Cognitive Achievement  In any case, a central human cognitive achievement  We (not I!) know something about how we do it – about underlying processes and mechanisms of language use  And we know something about how we (our infant selves) come to be able to do it – how we learn our first language(s) BUT I DON’T CARE !!* And more important, neither does DARPA * Beyond finding “inspiration” in theories of actual cognitive mechanisms/processes
  4. 4.  Goal: To make the knowledge expressed in (English) texts accessible by formal (artificial) reasoning systems  Translation(?): To make the (information) content expressed, e.g., in news stories available as input to “downstream” AI-systems  For, e.g., Intelligence Analysts, trying to put together an analytic picture of what was going on in some region during some time period.
  5. 5.  Applied Linguistics & Logic vs. (versus???)  Machine Learning: Applied Probability Theory and Statistics  What does this really come to, in our case (Machine Reading)?
  6. 6.  Hand-built grammars: sets of rules governing the ways in which sentences could be constructed out of sub-sentential elements (ultimately, of words/morphemes)  Often quite directly inspired by work in linguistics  Rules linking syntactic elements and structures with structures of symbols from formal languages  Often directly inspired by the languages developed and studied by logicians, typically for representing mathematical structures
  7. 7.  Availability of large annotated data-sets and of huge quantities of “raw” (unlabeled) text data  Growth of the practice of community-wide open evaluations and of  A metrics-focused research community  Moore’s Law; huge advances in processing speeds, memory capacity, etc., etc.  Resulted in moving toward a-theoretical, statistically- trained, ML-induced NLP modules (e.g., POS-taggers, NamedEntityExtractors, SemanticRoleLabelers, Parsers)  Tilrecently: sentence-/clause-level semantics was ignored
  8. 8. A New Synthesis:  Probabilistic Representation of Non-linguistic information + state-of-the-art Statistically-based ML-induced NLProcessing Modules  Analogous developments in Computer Vision  How to operationalize that `+’ ??  Many different possibilities to be explored  So little time … and nowhere near enough $$

×