Paclic2013Memo

FrontPage

• Barriere's (2009) TerminoWeb?

• TechCollo (Wible, and others)

• Normallized MI --> frequency divided by the number of senses in wordnet

• LTTCELC-Learner-Corpus

• Greenstone/FLAX Web Phrases

• Linggle

• Netspeak.org

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.