9
C
L
OMPUTATIONAL
INGUISTICS
CSC 485 Summer 2020
9. Supertagging
Gerald Penn
Department of Computer Science, University of Toronto
Copyright © 2017 Suzanne Stevenson, Graeme Hirst and Gerald Penn. All rights reserved.
Based upon slides by Michael Auli, Rober Hass and Aravind Joshi
WHY SUPERTAG?
If lexical items have more description associated with them, parsing is easier
Only useful if the supertag space is not huge
Straightforward to compile parse from accurate
supertagging
But impossible if there are any supertag errors We can account for some supertag errors
Don’t always want a full parse anyway
WHAT IS SUPERTAGGING?
Systematic assignment of supertags Supertags are:
Statistically selected Robust
Tends to work
Linguistically motivated This makes sense
WHAT IS SUPERTAGGING?
Many supertags for each word Extended Domain of Locality
Each lexical item has one supertag for every syntactic environment it appears in
Inspiration comes from LTAG, lexicalized tree-adjoining grammars, in which all dependencies are localized.
Generally, agreement features such as number and tense, are not part of the supertag.
HOW TO SUPERTAG
“Alice opened her eyes and saw.” Supertags:
Verb
Transitive verb Intransitive verb Infinitive verb …
Noun
Noun phrase (subject)
Nominal predicative
Nominal modifier
Nominal predicative subject extraction …
HOW TO SUPERTAG
“Alice opened her eyes and saw.” Supertags:
S
VP
Verb
Transitive verb Intransitive verb Infinitive verb …
NP↓ saw
NP↓
Noun
Noun phrase (subject)
Nominal predicative
Nominal modifier
Nominal predicative subject extraction …
HOW TO SUPERTAG
A supertag can be ruled out for a given word in a given input string…
Left and/or right context is too long/short for the input
If the supertag contains other terminals not found in the input
HOW TO SUPERTAG
“Alice opened her eyes and saw.” Supertags:
…
…
to saw …
…
Verb
Transitive verb Intransitive verb Infinitive verb …
Noun
Noun phrase (subject)
Nominal predicative
Nominal modifier
Nominal predicative subject extraction …
…
HOW TO SUPERTAG
This works fairly well
50% average reduction in number of possible supertags
HOW TO SUPERTAG
…but there’s more to be done
Good: average number of possible supertags per word
reduced from 47 to 25
Bad: average of 25 possible supertags per word
HOW TO SUPERTAG
Disambiguation by unigrams?
Give each word its most frequent supertag after PoS tagging
~75% accurate
Better results than one might expect given large number
of possible supertags
Common words (determiners, etc.) usually correct
This helps accuracy
Back off to PoS for unknown words
Also usually correct
HOW TO SUPERTAG
Disambiguation by n-grams?
We assume that subsequent words are independent
Trigrams plus Good-Turing smoothing Accuracy around 90%
Versus 75% from unigrams
Contextual information more important than lexical
Reversal of trend for PoS tagging
HOWEVER…
Correctly supertagged text yields a 30X parsing speedup
But even one mistake can cause parsing to fail completely
This is rather likely
Solution: n-best supertags?
When n=3, we get up to 96% accuracy… Not bad at all for such a simple method
425 lexical categories (PTB-CFG: ~50)
12 combinatory rules (PTB-CFG: > 500,000)