jurafsky&martin_3rdEd_17 (1).pdf

Np dt nn 28 np prp 25 because pcfgs dont allow a rule

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two rules have about equal probability. NP ! DT NN . 28 NP ! PRP . 25 Because PCFGs don’t allow a rule probability to be conditioned on surrounding context, this equal probability is all we get; there is no way to capture the fact that in subject position, the probability for NP ! PRP should go up to .91, while in object position, the probability for NP ! DT NN should go up to .66. These dependencies could be captured if the probability of expanding an NP as a pronoun (e.g., NP ! PRP ) versus a lexical NP (e.g., NP ! DT NN ) were condi- tioned on whether the NP was a subject or an object. Section 13.5 introduces the technique of parent annotation for adding this kind of conditioning. 13.4.2 Lack of Sensitivity to Lexical Dependencies A second class of problems with PCFGs is their lack of sensitivity to the words in the parse tree. Words do play a role in PCFGs since the parse probability includes the probability of a word given a part-of-speech (i.e., from rules like V ! sleep , NN ! book , etc.). But it turns out that lexical information is useful in other places in the grammar, such as in resolving prepositional phrase ( PP ) attachment ambiguities. Since prepo- sitional phrases in English can modify a noun phrase or a verb phrase, when a parser finds a prepositional phrase, it must decide where to attach it into the tree. Consider the following example: (13.19) Workers dumped sacks into a bin. Figure 13.5 shows two possible parse trees for this sentence; the one on the left is the correct parse; Fig. 13.6 shows another perspective on the preposition attachment problem, demonstrating that resolving the ambiguity in Fig. 13.5 is equivalent to deciding whether to attach the prepositional phrase into the rest of the tree at the NP or VP nodes; we say that the correct parse requires VP attachment , and the VP attachment incorrect parse implies NP attachment . NP attachment Why doesn’t a PCFG already deal with PP attachment ambiguities? Note that the two parse trees in Fig. 13.5 have almost exactly the same rules; they differ only in that the left-hand parse has this rule: VP ! VBD NP PP 1 Distribution of subjects from 31,021 declarative sentences; distribution of objects from 7,489 sen- tences. This tendency is caused by the use of subject position to realize the topic or old information in a sentence (Giv´on, 1990) . Pronouns are a way to talk about old information, while non-pronominal (“lexical”) noun-phrases are often used to introduce new referents. We talk more about new and old information in Chapter 23.
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222 C HAPTER 13 S TATISTICAL P ARSING S VP PP NP NN bin DT a P into NP NNS sacks VBD dumped NP NNS workers S VP NP PP NP NN bin DT a P into NP NNS sacks VBD dumped NP NNS workers Figure 13.5 Two possible parse trees for a prepositional phrase attachment ambiguity . The left parse is the sensible one, in which “into a bin” describes the resulting location of the sacks. In the right incorrect parse, the sacks to be dumped are the ones which are already “into a bin”, whatever that might mean.
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