Notes on computational linguistics.pdf - UCLA Department of ...
Notes on computational linguistics.pdf - UCLA Department of ...
Notes on computational linguistics.pdf - UCLA Department of ...
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Stabler - Lx 185/209 2003<br />
To deal with this problem, Charniak, Goldwater, and Johns<strong>on</strong> (1998) explore the prospects for using a<br />
probabilistic chart parsing method that builds <strong>on</strong>ly the n best analyses (<strong>of</strong> each category for each span<br />
<strong>of</strong> the input) for some n.<br />
(4) Is it reas<strong>on</strong>able to think that a probabilistic language models can handle these disambiguati<strong>on</strong> problems?<br />
It is not clear that this questi<strong>on</strong> has any sense, since the term ‘probabilistic language model’ apparently<br />
covers almost everything, including, as a limiting case, the simple, discrete models that we have been<br />
studying previously.<br />
However, it is important to recognize that the disambiguati<strong>on</strong> problem is a hard <strong>on</strong>e, and clearly involves<br />
background factors that cannot be regarded as linguistic.<br />
It has been generally recognized since the early studies <strong>of</strong> language in the traditi<strong>on</strong> <strong>of</strong> analytic philosophy,<br />
and since the earliest developments in modern formal semantics, that the problem <strong>of</strong> determining<br />
the intended reading <strong>of</strong> a sentence, like the problem <strong>of</strong> determining the intended reference <strong>of</strong> a name<br />
or noun phrase is, at least, well bey<strong>on</strong>d the analytical tools available now. See, e.g., Partee (1975, p80),<br />
Kamp (1984, p39), Fodor (1983), Putnam (1986, p222), and many others. Putnam argues, for example,<br />
that<br />
…deciding – at least in hard cases – whether two terms have the same meaning or whether they<br />
have the same reference or whether they could have the same reference may involve deciding<br />
what is and what is not a good scientific explanati<strong>on</strong>.<br />
From this perspective, the extent to which simple statistical models account for human language use<br />
is surprising! As we will see, while we say surprising and new things quite a lot, it is easy to discern<br />
creatures <strong>of</strong> habit behind language use as well.<br />
We briefly survey some <strong>of</strong> the most basic c<strong>on</strong>cepts <strong>of</strong> probability theory and informati<strong>on</strong>. Reading quickly<br />
over at least §8.1.1-§8.1.3 is recommended, but the main thread <strong>of</strong> development can be followed by skipping<br />
directly to §8.2.1 <strong>on</strong> page 159.<br />
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