CS 378 Lecture 8 Today - - Bias in embeddings - Part - of - speech intro - sequence tagging - Tagging with classifiers Announcements - [ prams in Zoom ] - Al - AZ - Survey : Good : re watchable lectures , exercises / breakouts , notes Not so good : ① Working in pairs on Hw - please do ! ② More exercises ③ Bigger breakouts / more engagement ④ More context / big picture ⑤ Reading guidance - holding
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sequence Tagging - University of Texas at Austingdurrett/courses/fa2020/... · 2020. 9. 22. · -sequence tagging-Tagging with classifiers Announcements-[prams in Zoom]-Al-AZ-Survey:
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CS 378 Lecture 8
Today-
- Bias in embeddings- Part- of -speech intro- sequence tagging- Tagging with classifiers
Announcements-
[ prams in Zoom]- Al- AZ- Survey :
Good : rewatchable lectures,exercises / breakouts
,notes
Not so good : ① Working in pairs on Hw - please do!② More exercises
③ Bigger breakouts/more engagement④ More context/big picture⑤ Reading guidance - holding
Recap Skip-gram
Input : corpus of textCorpus ⇒ (x, y ) pairs which are c- k
words apart ( K= window size )
P( context -_yl word =×) =e%{ CITY ,
y'EV
Maximize log likelihood of data ⇒
get useful Js , IsUse J,I, or Ttc in downstream
tasks
the fish swam quickly K=2
Ner
Where we are-
Classification : argnynatnwtyffx)wNN
,Bow
- sent
⑤ doc⇒ label
- sent ⇒ label for each wordin that Sentence
part -of- speech tagging① Structurally different problem* → y
tag forX, ,
. ..
gXu → Yi , - . - 14h each word
② Syntax
PartchText to speech : record
Info . extraction : airing ✓ or N ?
POS Tags-
Open - class : there Closed - class ? fixedcan be new words here set
nouns : I:r: :.EE/esTIYaniitTesiaVerbs : see, registered six N ⇒ NP