Notes on computational linguistics.pdf - UCLA Department of ...

Stabler - Lx 185/209 2003
8.3 Multiple knowledge sources
(140) When a language user recognizes what what has been said, it is clear that various sorts of information
are used.
Given an ambiguous acoustic signal (which is certainly very common!), various hypotheses about the
words of the utterance will fit the acoustic data more or less well. These hypotheses can also vary
with respect to whether they are syntactically well-formed, semantically coherent, and pragmatically
plausible.
To study how recognition could work in this kind of setting, let’s return to simplified problems like the
one that was considered in exercise (3) on page 32.
(141) Suppose, for example, that we have heard “I know John saw Mary” clearly pronounced in a setting with
no significant background noise. Leaving aside quote names and neologisms, the acoustic evidence
might suggest a set of possibilities like this:
0
I-DP
eye-N
1
know-V
no-Neg
2
John-DP
john-N
3
saw-V
some-D
sought-V
some-D
5
4
mare-N
summery-A
Mary-DP
merry-A
The acoustic evidence will support these various hypotheses differentially, so suppose that our speech
processor ranks the hypotheses as follows:
0
I-DP/0.5
eye-N/0.5
1
know-V/0.5
no-Neg/0.5
2
John-DP/0.5
john-N/0.5
3
saw-V/0.5
some-D/0.15
sought-V/0.1
some-D/0.15
6
4
air-N
mare-N/0.2
air-N/0.5
summery-A/0.1
What does this model above suggest as the value of
P(I-DP know-V John-DP saw-V Mary-DP|acoustic signal)?
163
6
airy-A
Mary-DP/0.4
merry-A/0.4
5
airy-A/0.5
E-N
E-N/1
7
7/1