[[FrontPage]]

-Barriere's (2009) TerminoWeb

-TechCollo (Wible, and others) 
-[[TechCollo (Wible, and others)>http://techcollo.stringnet.org/]] 

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

-[[LTTCELC-Learner-Corpus>http://lttcelc.org/]]

-Greenstone/FLAX Web Phrases

-[[Linggle>http://linggle.com/##CMD]]

-[[Netspeak.org>http://www.netspeak.org/]]

**Keynote [#m6aff977]

-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) [#f848ead7]

-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) [#vf5279a0]

-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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