[[FrontPage]] -Barriere's (2009) TerminoWeb -[[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 [#vf5279a0] **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.