FrontPage

  • Barriere's (2009) TerminoWeb?
  • Normallized MI --> frequency divided by the number of senses in wordnet
  • Greenstone/FLAX Web Phrases

Keynote

  • Magnetoencephalography (MEG)
  • word recognition
    • visual cortices, Occipital Lobe --> abstract sign recognition
    • lemma/lexical access
    • recomposition of stem and suffix

Keynote2: Prof. Wen-Lian Hsu (Academia Sinica)

  • The archaelogist is in trouble ([people: [be]: [in a situation])
  • The old archaelogist named John Doe is in a very serious trouble with his academia reputation.
  • phrases --> classes (NP, PP, AdjP)
  • Simple phrase: word, word combination
  • Complex phrase: named entity frame
  • Scenerio frame:
  • Frame elements
    • exaple??
  • How to score an Insertion?
  • Principle Pattern Analysis
    • 60 patterns out of all the sentence structures will cover approximately 98%.
    • Using that framework, you can do the insertion/deletion operations over the pattern.

Keynote by Tsujii Junichi (Microsoft Research, Beijing)

  • Language acquisition --> skimming huge amount of data --> wrong?
  • Semantic trigger (Pinker) --> certain contexts
    • text <--> knowledge (bootstrapping)
  • Innate ability (Chomsky)
    • grammar (linear sequence --> structure --> meaning)
  • rationalism vs. empiricism
  • Ken Church (2011): A pendulum swung too far toward empiricism/data
  • Integrating [grammar, rule] into [data]
  • Knowledge mining
    • Triggering: Big knowledge to enhance NLP abilities
      • entity linking
      • relation extraction
  • Entity linking
    • MS --> Microsoft or Multiple sclerosis
    • entity relation in knowledge domain (human knows all these relations)
    • categories in knowledge
  • How to use those structural knowledge to disambiguate the meaning of words in a certain context
  • Build classifiers, looking at the context and tell which a certain word belongs to a particular entity
  • Inference relationship among different cues with different strength.
  • Semantic parsing
    • Identify grammatical relationships
    • Extract entity relationships
  • Parsing: Current state of the arts
    • F-value too high
    • Most relations are very simple (DET-N, ADJ-N) and trivial
    • Non-trivial one: PP attachment
      • accuracy is around 80-85%
  • semantically crucial problems are not properly solved by current parsing techniques
  • They use HPSG
  • Fragments of parsing are compared to each other and compared in terms of similarities.
  • inferences involved --> difficult
  • statistical models of inferences have become scalable and feasible.
  • integration of NLP with knowledge-based inferences is becoming a practical framework of natural language understanding.

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Last-modified: 2013-11-25 (月) 01:16:18 (1429d)