CSCI 5832 Natural Language Processing Jim Martin Lecture 17 3 13 08 1 Today 3 13 Statistical Parsing 2 3 13 08 Example 3 3 13 08 1 Probabilistic CFGs The probabilistic model Assigning probabilities to parse trees Getting the probabilities for the model Parsing with probabilities Slight modification to dynamic programming approach Task is to find the max probability tree for an input 4 3 13 08 Basic Probability Model A derivation tree consists of the bag of grammar rules that are in the tree The probability of a tree is just the product of the probabilities of the rules in the derivation 5 3 13 08 Probability Model 1 1 The probability of a word sequence sentence is the probability of its tree in the unambiguous case It s the sum of the probabilities of the trees in the ambiguous case Since we can use the probability of the tree s as a proxy for the probability of the sentence PCFGs give us an alternative to N Gram models as a kind of language model 6 3 13 08 2 Getting the Probabilities From an annotated database a treebank So for example to get the probability for a particular VP rule just count all the times the rule is used and divide by the number of VPs overall 7 3 13 08 Prob CKY Alter CKY so that the probabilities of constituents are stored on the way up Probability of a new constituent A derived from the rule A BC is P A B C P B P C Where P B and P C are already in the table But what we store is the MAX probability over all the A rules 8 3 13 08 Prob CKY 9 3 13 08 3 Problems with PCFGs The probability model we re using is just based on the rules in the derivation Doesn t use the words in any real way Doesn t take into account where in the derivation a rule is used Doesn t really work Most probable parse isn t usually the right one the one in the treebank test set 10 3 13 08 Solution 1 Add lexical dependencies to the scheme Infiltrate the predilections of particular words into the probabilities in the derivation I e Condition the rule probabilities on the actual words 11 3 13 08 Heads To do that we re going to make use of the notion of the head of a phrase The head of an NP is its noun The head of a VP is its verb The head of a PP is its preposition It s really more complicated than that but this will do 12 3 13 08 4 Example right Attribute grammar 13 3 13 08 Example wrong 14 3 13 08 How We used to have VP V NP PP P rule VP That s the count of this rule divided by the number of VPs in a treebank Now we have VP dumped V dumped NP sacks PP into P r VP dumped is the verb sacks is the head of the NP into is the head of the PP Not likely to have significant counts in any treebank 15 3 13 08 5 Declare Independence When stuck exploit independence and collect the statistics you can We ll focus on capturing two things Verb subcategorization Particular verbs have affinities for particular VPs Objects affinities for their predicates mostly their mothers and grandmothers Some objects fit better with some predicates than others 16 3 13 08 Subcategorization Condition particular VP rules on their head so r15 VP V NP PP P r VP Becomes P r15 VP dumped What s the count How many times was this rule used with dump divided by the number of VPs that dump appears in total 17 3 13 08 Preferences Verb subcategorization captures the affinity between VP heads verbs and the VP rules they go with That is the affinity between a node and one of its daughter nodes What about the affinity between VP heads and the heads of the other daughters of the VP Back to our examples 18 3 13 08 6 Example right 19 3 13 08 Example wrong 20 3 13 08 Preferences The issue here is the attachment of the PP So the affinities we care about are the ones between dumped and into vs sacks and into So count the places where dumped is the head of a constituent that has a PP daughter with into as its head and normalize Vs the situation where sacks is a constituent with into as the head of a PP daughter 21 3 13 08 7 Preferences 2 Consider the VPs Ate spaghetti with gusto Ate spaghetti with marinara Here the heads of the PPs are the same with so that won t help But the affinity of gusto for eat is much larger than its affinity for spaghetti On the other hand the affinity of marinara for spaghetti is much higher than its affinity for ate we hope 22 3 13 08 Preferences 2 Note the relationship here is more distant and doesn t involve a headword since gusto and marinara aren t the heads of the PPs Vp ate Vp ate Np spag Vp ate Pp with v np Ate spaghetti with gusto np Pp with v Ate spaghetti with marinara 23 3 13 08 Note In case someone hasn t pointed this out yet this lexicalization stuff is a thinly veiled attempt to incorporate semantics into the syntactic parsing process Duhh Picking the right parse requires the use of semantics 24 3 13 08 8 Break Quiz Chapter 12 12 1 through 12 6 CFGs Major English phrase types problems with CFGs relation to finite state methods Chapter 13 All except 13 4 3 CKY Earley partial parsing sequence labeling Chapter 14 14 1 through14 6 1 Basic prob CFG model getting the counts prob CKY problems with the model lexicalization and grammar rewriting Bring a cheat sheet 25 3 13 08 Rule Rewriting An alternative to using these kinds of probabilistic lexical dependencies is to rewrite the grammar so that the rules do capture the regularities we want By splitting and merging the non terminals in the grammar Example split NPs into different classes 26 3 13 08 NPs Our CFG rules for NPs don t condition on where the rule is applied they re contextfree remember But we know that not all the rules occur with equal frequency in all contexts 27 3 13 08 9 Other Examples Lots of other examples like this in the TreeBank Many at the part of speech level Recall that many decisions made in annotation efforts are directed towards improving annotator agreement not towards doing the right thing Often this involves conflating distinct classes into a larger class TO IN Det etc 28 3 13 08 Rule Rewriting Three approaches Use linguistic intuitions to directly rewrite rules NP Obj and the NP Subj approach Automatically rewrite the rules using context to capture some of what we want Ie Incorporate context into a context free approach Search through the space of rewrites for …
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