SYNATCTIC PARSING Heng Ji [email protected] January 17, 2019

SYNATCTIC PARSING Heng Ji jih@rpi.edu January 17, 2019

SYNATCTIC PARSING Heng Ji [email protected] January 17, 2019 2/40 Syntax By grammar, or syntax, we have in mind the kind of

implicit knowledge of your native language that you had mastered by the time you were 3 years old without explicit instruction Not the kind of stuff you were later taught in grammar school 3/40 Syntax Why should you care?

Grammars (and parsing) are key components in many applications Grammar checkers Dialogue management Question answering Information extraction Machine translation 4/40

Syntax Key notions that well cover Constituency Grammatical relations and Dependency Heads Key formalism Context-free grammars Resources

Treebanks 5/40 Constituency The basic idea here is that groups of words within utterances can be shown to act as single units. And in a given language, these units form coherent classes that can be be shown to behave in similar ways

With respect to their internal structure And with respect to other units in the language 6/40 Constituency Internal structure We can describe an internal structure to the class (might have to use disjunctions of somewhat unlike sub-classes to do this). External behavior

For example, we can say that noun phrases can come before verbs 7/40 Constituency For example, it makes sense to the say that the following are all noun phrases in English...

Why? One piece of evidence is that they can all precede verbs. This is external evidence 8/40 Context-Free Grammars Context-free grammars (CFGs)

Also known as Phrase structure grammars Backus-Naur form Consist of Rules Terminals Non-terminals 9/40

Context-Free Grammars Terminals Well take these to be words (for now) Non-Terminals The constituents in a language Like noun phrase, verb phrase and sentence Rules Rules are equations that consist of a single non-terminal on the left

and any number of terminals and non-terminals on the right. 10/40 Some NP Rules Here are some rules for our noun phrases Together, these describe two kinds of NPs. One that consists of a determiner followed by a nominal And another that says that proper names are NPs.

The third rule illustrates two things An explicit disjunction Two kinds of nominals A recursive definition Same non-terminal on the right and left-side of the rule 11/40 L0 Grammar

12/40 Derivations A derivation is a sequence of rules applied to a string that accounts for that string Covers all the elements in the

string Covers only the elements in the string 13/40 Definition More formally, a CFG consists of

14/40 Parsing Parsing is the process of taking a string and a grammar and returning a (multiple?) parse tree(s) for that string It is completely analogous to running a finite-state transducer with a tape Its just more powerful Remember this means that there are languages we can capture with

CFGs that we cant capture with finite-state methods More on this when we get to Ch. 13. 15/40 An English Grammar Fragment Sentences Noun phrases Agreement

Verb phrases Subcategorization 16/40 Sentence Types Declaratives: A plane left. S NP VP Imperatives: Leave!

S VP Yes-No Questions: Did the plane leave? S Aux NP VP WH Questions: When did the plane leave? S WH-NP Aux NP VP 17/40

Noun Phrases Lets consider the following rule in more detail... NP Det Nominal Most of the complexity of English noun phrases is hidden in this rule. Consider the derivation for the following example All the morning flights from Denver to Tampa leaving before 10 18/40

Noun Phrases 19/40 NP Structure Clearly this NP is really about flights. Thats the central criticial noun in this NP. Lets call that the head. We can dissect this kind of NP into the stuff that can

come before the head, and the stuff that can come after it. 20/40 Determiners Noun phrases can start with determiners... Determiners can be Simple lexical items: the, this, a, an, etc. A car

Or simple possessives Johns car Or complex recursive versions of that Johns sisters husbands sons car 21/40 Nominals Contains the head and any pre- and post- modifiers of the

head. Pre Quantifiers, cardinals, ordinals... Three cars Adjectives and Aps large cars Ordering constraints Three large cars ?large three cars

22/40 Postmodifiers Three kinds Prepositional phrases From Seattle Non-finite clauses Arriving before noon Relative clauses That serve breakfast

Same general (recursive) rule to handle these Nominal Nominal PP Nominal Nominal GerundVP Nominal Nominal RelClause 23/40 Agreement By agreement, we have in mind constraints that hold

among various constituents that take part in a rule or set of rules For example, in English, determiners and the head nouns in NPs have to agree in their number. This flight Those flights *This flights

*Those flight 24/40 The Point CFGs appear to be just about what we need to account for a lot of basic syntactic structure in English. But there are problems

That can be dealt with adequately, although not elegantly, by staying within the CFG framework. There are simpler, more elegant, solutions that take us out of the CFG framework (beyond its formal power) LFG, HPSG, Construction grammar, XTAG, etc. Chapter 15 explores the unification approach in more detail

25/40 Treebanks Treebanks are corpora in which each sentence has been paired with a parse tree (presumably the right one). These are generally created By first parsing the collection with an automatic parser

And then having human annotators correct each parse as necessary. This generally requires detailed annotation guidelines that provide a POS tagset, a grammar and instructions for how to deal with particular grammatical constructions. 26/40

Penn Treebank Penn TreeBank is a widely used treebank. Most well known is the Wall Street Journal section of the Penn TreeBank. 1 M words from the 1987-1989 Wall

Street Journal. 27/40 Treebank Grammars Such grammars tend to be very flat due to the fact that they tend to avoid recursion. To ease the annotators burden

For example, the Penn Treebank has 4500 different rules for VPs. Among them... 28/40 Heads in Trees Finding heads in treebank trees is a task that arises frequently in many applications.

Particularly important in statistical parsing We can visualize this task by annotating the nodes of a parse tree with the heads of each corresponding node. 29/40 Lexically Decorated Tree

30/40 Head Finding The standard way to do head finding is to use a simple set of tree traversal rules specific to each non-terminal in the grammar. 31/40

Noun Phrases 32/40 Summary Context-free grammars can be used to model various facts about the syntax of a language. When paired with parsers, such grammars consititute a critical component in many applications.

Constituency is a key phenomena easily captured with CFG rules. But agreement and subcategorization do pose significant problems Treebanks pair sentences in corpus with their corresponding trees.

33/40 For Now Assume You have all the words already in some buffer The input isnt POS tagged We wont worry about morphological analysis All the words are known These are all problematic in various ways, and would have to be

addressed in real applications. 34/40 Top-Down Search Since were trying to find trees rooted with an S (Sentences), why not start with the rules that give us an S. Then we can work our way down from there to the words.

35/40 Top Down Space 36/40 Bottom-Up Parsing Of course, we also want trees that cover the input words.

So we might also start with trees that link up with the words in the right way. Then work your way up from there to larger and larger trees. 37/40 Bottom-Up Search 38/40

Bottom-Up Search 39/40 Bottom-Up Search 40/40 Bottom-Up Search

41/40 Bottom-Up Search 42/40 Top-Down and Bottom-Up Top-down Only searches for trees that can be answers (i.e. Ss)

But also suggests trees that are not consistent with any of the words Bottom-up Only forms trees consistent with the words But suggests trees that make no sense globally 43/40 Problems Even with the best filtering, backtracking methods are

doomed because of two inter-related problems Ambiguity Shared subproblems 44/40 Ambiguity 45/40

Shared Sub-Problems No matter what kind of search (top-down or bottom-up or mixed) that we choose. We dont want to redo work weve already done. Unfortunately, nave backtracking will lead to duplicated work. 46/40

Shared Sub-Problems Consider A flight from Indianapolis to Houston on TWA 47/40 Dynamic Programming DP search methods fill tables with partial results and thereby

Avoid doing avoidable repeated work Solve exponential problems in polynomial time (well, no not really) Efficiently store ambiguous structures with shared sub-parts. Well cover the CKY algorithm 48/40

CKY Parsing First well limit our grammar to epsilon-free, binary rules (more later) Consider the rule A BC If there is an A somewhere in the input then there must be a B followed by a C in the input. If the A spans from i to j in the input then there must be

some k st. i

As in the case of the TreeBank grammar? Convert it to binary any arbitrary CFG can be rewritten into Chomsky-Normal Form automatically. What does this mean? The resulting grammar accepts (and rejects) the same set of strings as the original grammar. But the resulting derivations (trees) are different.

50/40 Problem More specifically, we want our rules to be of the form ABC Or A w That is, rules can expand to either 2 non-terminals or to a single terminal.

51/40 Binarization Intuition Eliminate chains of unit productions. Introduce new intermediate non-terminals into the grammar that distribute rules with length > 2 over several rules.

S A B C turns into S X C and XAB Where X is a symbol that doesnt occur anywhere else in the the grammar. So 52/40 Sample L1 Grammar

the 53/40 CNF Conversion 54/40 CKY So lets build a table so that an A spanning from i to j in

the input is placed in cell [i,j] in the table. So a non-terminal spanning an entire string will sit in cell [0, n] Hopefully an S If we build the table bottom-up, well know that the parts of the A must go from i to k and from k to j, for some k.

55/40 CKY Meaning that for a rule like A B C we should look for a B in [i,k] and a C in [k,j]. In other words, if we think there might be an A spanning i,j in the input AND

A B C is a rule in the grammar THEN There must be a B in [i,k] and a C in [k,j] for some i

For each cell, loop over the appropriate k values to search for things to add. 57/40 Example 58/40

Example Filling column 5 59/40 Example 60/40

Example 61/40 Example 62/40 Example

63/40 To formalize it: CKY Algorithm 64/40 Exercises Try to parse the following sentence: I prefer meal on flight.

65/40 Take-home Messages Context-free grammars can be used to model various facts about the syntax of a language. When paired with parsers, such grammars consititute a critical component in many applications. Constituency is a key phenomena easily captured with CFG rules.

But agreement and subcategorization do pose significant problems CKY is a bottom-up dynamic programming algorithm We can convert CFG rules into CNF forms

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