1 / 30

300 likes | 454 Views

Probabilistic and Lexicalized Parsing. Probabilistic CFGs. Weighted CFGs Attach weights to rules of CFG Compute weights of derivations Use weights to pick, preferred parses Utility: Pruning and ordering the search space, disambiguate, Language Model for ASR.

Download Presentation
## Probabilistic and Lexicalized Parsing

**An Image/Link below is provided (as is) to download presentation**
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.
Content is provided to you AS IS for your information and personal use only.
Download presentation by click this link.
While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

**Probabilistic CFGs**• Weighted CFGs • Attach weights to rules of CFG • Compute weights of derivations • Use weights to pick, preferred parses • Utility: Pruning and ordering the search space, disambiguate, Language Model for ASR. • Parsing with weighted grammars (like Weighted FA) • T* = arg maxT W(T,S) • Probabilistic CFGs are one form of weighted CFGs.**Probability Model**• Rule Probability: • Attach probabilities to grammar rules • Expansions for a given non-terminal sum to 1 R1:VP V .55 R2: VP V NP .40 R3: VP V NP NP .05 • Estimate the probabilities from annotated corpora P(R1)=counts(R1)/counts(VP) • Derivation Probability: • Derivation T= {R1…Rn} • Probability of a derivation: • Most likely probable parse: • Probability of a sentence: • Sum over all possible derivations for the sentence • Note the independence assumption: Parse probability does not change based on where the rule is expanded.**S NP VP**VP V NP NP NP PP VP VP PP PP P NP NP John | Mary | Denver V -> called P -> from S VP NP PP VP V NP P NP called from John Mary Denver Structural ambiguity John called Mary from Denver S VP NP NP V NP PP called John Mary P NP from Denver**Cocke-Younger-Kasami Parser**• Bottom-up parser with top-down filtering • Start State(s): (A, i, i+1) for each Awi+1 • End State: (S, 0,n) n is the input size • Next State Rules • (B, i, k) (C, k, j) (A, i,j) if ABC**Probabilistic CKY**• Assign probabilities to constituents as they are completed and placed in the table • Computing the probability • Since we are interested in the max P(S,0,n) • Use the max probability for each constituent • Maintain back-pointers to recover the parse.**Problems with PCFGs**• The probability model we’re using is just based on the rules in the derivation. • Lexical insensitivity: • Doesn’t use the words in any real way • Structural disambiguation is lexically driven • PP attachment often depends on the verb, its object, and the preposition • I ate pickles with a fork. • I ate pickles with relish. • Context insensitivity of the derivation • Doesn’t take into account where in the derivation a rule is used • Pronouns more often subjects than objects • She hates Mary. • Mary hates her. • Solution: Lexicalization • Add lexical information to each rule**An example of lexical information: Heads**• Make use of notion of the headof a phrase • Head of an NP is a noun • Head of a VP is the main verb • Head of a PP is its preposition • Each LHS of a rule in the PCFG has a lexical item • Each RHS non-terminal has a lexical item. • One of the lexical items is shared with the LHS. • If R is the number of binary branching rules in CFG, in lexicalized CFG: O(2*|∑|*|R|) • Unary rules: O(|∑|*|R|)**Example (correct parse)**Attribute grammar**Computing Lexicalized Rule Probabilities**• We started with rule probabilities • VP V NP PP P(rule|VP) • E.g., count of this rule divided by the number of VPs in a treebank • Now we want lexicalized probabilities • VP(dumped) V(dumped) NP(sacks)PP(in) • P(rule|VP ^ dumped is the verb ^ sacks is the head of the NP ^ in is the head of the PP) • Not likely to have significant counts in any treebank**Another Example**• Consider the VPs • Ate spaghetti with gusto • Ate spaghetti with marinara • Dependency is not between mother-child. Vp (ate) Vp(ate) Np(spag) Vp(ate) Pp(with) np Pp(with) v np v Ate spaghetti with marinara Ate spaghetti with gusto**Log-linear models for Parsing**• Why restrict to the conditioning to the elements of a rule? • Use even larger context • Word sequence, word types, sub-tree context etc. • In general, compute P(y|x); where fi(x,y) test the properties of the context; li is the weight of that feature. • Use these as scores in the CKY algorithm to find the best scoring parse.**S**N NP S Adj N NP VP Adv S N underground V NP now poachers control S VP NP N N S S NP VP Adv VP Det NP N N N N NP NP VP VP V NP now the e S NP Adj V V poachers trade VP underground S VP Adv control control S NP NP VP now NP N V NP trade e N e trade Supertagging: Almost parsing Poachers now control the underground trade S S NP VP S NP V NP NP VP e N V NP e poachers : : e Adj : : : underground**Summary**• Parsing context-free grammars • Top-down and Bottom-up parsers • Mixed approaches (CKY, Earley parsers) • Preferences over parses using probabilities • Parsing with PCFG and PCKY algorithms • Enriching the probability model • Lexicalization • Log-linear models for parsing

More Related