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

Stabler - Lx 185/209 2003
8 Stochastic influences on simple language models
8.1 Motivations and background
(1) Our example parsers have tiny dictionaries. If you just add in a big dictionary, we get many structural
ambiguities.
Just to illustrate how bad the problem is, the following simple examples from Abney (1996a) have
ambiguitiesthatmostpeoplewouldnotnotice,butourparsingmethodswill:
a. I know the cows are grazing in the meadow
b. I know John saw Mary
The word are is a noun in a hectare is a hundred ares, andsaw can be a noun, so the non-obvious
readings of those two sentences are the ones analogous to the natural readings of these:
a. I know the sales force (which is) meeting in the office
b. I know Gatling gun Joe
There are many other readings too, ones which would be spelled differently (if we were careful about
quotes, which most people are not) but pronounced the same:
a. I know “The Cows are Grazing in the Meadow”
b. I know “The Cows are Grazing” in the meadow
c. I know “The Cows are” grazing in the meadow
…
… I know ““The Cows are Grazing in the Meadow””
…
This kind of thing is a problem for mimicking, let alone modeling, human recognition capabilities.
Abney concludes:
The problem of how to identify the correct structure from among the in-principle possible
structures provides one of the central motivations for the use of weighted grammars in computational
linguistics.
(2) Martin Gardner gives us the following amusing puzzle. Insert the minimum number of quotation marks
to make the best sense of the following sentence:
Wouldn’t the sentence I want to put a hyphen between the words fish and and and and and
chips in my fish and chips sign have looked cleaner if quotation marks had been placed before
fish and between fish and and and and and and and and and and and and and and and and
and and and and and chips as well as after chips?
In effect, we solve a problem like this every time we interpret a spoken sentence.
(3) Another demonstration of the ambiguity problem comes from studies like Charniak, Goldwater, and
Johnson (1998). Applying the grammar of the Penn Treebank II to sentences in that Treebank shorter
than 40 words from the Wall Street Journal, they found that their charts had, on average, 1.2 million
items per sentence – obviously, very few of these are actually used in the desired derivation, and the
rest come from local and global ambiguities.
They say:
Numbers like this suggest that any approach that offers the possibility of reducing the work
load is well worth pursuing…
116