Language and Speech Technology: Parsing

Language and Speech Technology: Parsing

Jan OdijkJanuary 2011

LOT Winter School 2011

1

Overview

• Grammars & Grammar Types

• Parsing– Naïve Parsing– Earley Parser– Example (using handouts)

• Earley Parser Extensions

• Parsers & CLARIN

2

Overview





3

Grammars

• Grammar G = (VT, VN, P, S) where

– VT terminal vocabulary

– VN nonterminal vocabulary

– P set of rules α→β (lhs → rhs) • α Є VN

+

• β Є (VN U VT)*

– S Є VN (start symbol)

4

Grammars

• Example Grammar G = (VT, VN, P, S) with

– VT = {the, a, garden, book, in,}

– VN = {NP, Det, N, P, PP}

– P = {PP→P NP, NP→Det N, Det→the, Det→a, N→garden, N→book, P→in }

– S = PP

5

Example Derivation

• PP (start symbol)

• P NP (PP →P NP)

• in NP (P → in)

• in Det N (NP →Det N)

• in the N (Det → the)

• in the garden ( N → garden)

6

Grammar Types

• Finite State Grammars (Type 3)– A → aA, A → a. A Є VN, a Є VT

– Too weak to deal with natural language in toto– Efficient processing techniques– Often used for applications where partial

analyses of natural language are sufficient– Often used for morphology / phonology

7

Grammar Types

• Context-Free Grammars (CFG, Type 2)– A → β. A Є VN

– To weak to deal with natural language• Surely for strong generative adequacy

• Also for weak generative adequacy

– Reasonably efficient processing techniques– Generally taken as a basis for dealing with natural

language, extended with other techniques

8

Grammar Types

• Context-Sensitive Grammars (Type 1)– α→β, |α| <= |β|– Usually not considered in the context of NLP

• Type-0 grammars – No restrictions– Usually not considered except in combination

with CFG9

Overview





10

Parsing

• Parsing – Is an algorithm

• It must finish!

– For assigning syntactic structures• Ambiguity!

– To a sequence of terminal symbols– In accordance with a given grammar– (If possible, efficient)

11

Parsing for CFGs

• Focus here on– Parser for CFGs– for natural language– More specifically: Earley parser

• Why?– Most NLP systems with a grammar use a parser for

CFG as a basis– Basic techniques will also recur in parsers for

different grammar types12

Overview





13

Naïve Parsing

• see handout

• Problems for naïve parsing– A lot of re-parsing of subtrees– Bottom-up

• Wastes time and space on trees that cannot lead to S

– Top-down• Wastes time and space on trees that cannot match

input string

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Naïve parsing

• Top-down– Recursion problem

• Can be solved for right-recursion by matching with input tokens, but

• Problem with left recursion remains:– NP → NP PP

• Ambiguity– Temporary ambiguity– Real ambiguity

15

Naïve parsing

• Naïve Parsing Complexity– Time needed to parse is exponential:– cn (c a constant, length input tokens)– (in the worst case)

• Takes too much time

• Is not practically feasible

16

Overview





17

Earley Parser

• Top-down approach but– Predictor avoids wasting time and space on

irrelevant trees– Does not build actual structures, but stores

enough information to reconstruct structures– Uses dynamic programming technique to avoid

recomputation of subtrees– Avoids problems with left recursion– Makes complexity cubic: n3

18

Earley Parser

• Number positions in input string (0 .. N)

• 0 book 1 that 2 flight 3

• Notation [i,j] stands for the string from position i to position j– [0,1] = “book”– [1,3] = “that flight”– [2,2]= “”

19

Earley Parser

• Dotted Rules– is a grammar rule + indication of progress

– ie. Which elements of the rhs have been seen yet and which ones not yet

– Indicated by a dot (we use an asterisk)

• Example– S → Aux NP * VP

– Aux and NP have been dealt with but VP not yet20

Earley Parser

• Input: – Sequence of N words (words[1..N]), and– grammar

• Output:– a Store = (agenda, chart)

• (sometimes chart = N+1 chart entries: chart[0 .. N])

21

Earley Parser

• Agenda, chart: sets of states

• A state consists of– Dotted rule– Span relative to the input: [i,j]– Previous states: list of state identifiers

• And gets a unique identifier

• Example– S11: VP → V’ * NP; [0,1]; [S8]

22

Earley Parser

• State– Is complete

• iff dot is the last element in the dotted rule

• E.g. state with VP → Verb NP * is complete

• NextCat (state)– Only applies if state is not complete– Is the category immediately following the dot– VP → Verb * NP : NextCat(state)= NP

23

Earley Parser

• 3 operations on states, – Predictor

• Predicts which categories to expect

– Scanner• if a terminal category C is expected, and a word of

category C is encountered in this position,– Consumes the word and shifts the dot

– Completer• Applies to a complete state s, and modifies all states that

gave rise to this state24

Earley Parser

• Predictor– Applies to an incomplete state– ( A → α * B β, [i,j], _)– B is a nonterminal– For each (B → γ) in grammar

• Make a new state s = (B → * γ, [j,j], [])• enqueue(s , store)

– Enqueue (s,ce) = add s to ce unless ce already contains s

25

Earley Parser

• Scanner– Applies to an incomplete state– ( A → α * b β, [i,j], _)– b is a terminal

• Make a new state s = (b → words[j] * , [j,j+1], [])

• enqueue(s , store)

26

Earley Parser

• Completer– Applies to an complete state– ( B → γ *, [j,k], L1)– For each (A → α * B β, [i,j], L2) in chart[j]

• Make new state s = (A → α B * β, [i,k], L2 ++ L1)

• enqueue(s , store)

27

Earley Parser

• Store = (agenda, chart)

• Apply operations on states in the agenda until the agenda is empty

• When applying an operation to a state s in the agenda– Move the state s from the agenda into the chart– Add the resulting states of the operation to the

agenda28

Earley Parser

• Initial store = ([Г → *S], emptychart)– Where Г is a ‘fresh’ nonterminal start symbol

• Input sentence accepted– Iff there is a state (Г → S *, [0,N], LS) in the

chart and the agenda is empty

• Parse tree(s) can be reconstructed via the list of earlier states (LS)

29

Overview





30

Overview





31

Earley Parser Extensions

• Replace elements of V by feature sets (attribute-value matrices, AVMs)– Harmless if finitely valued– E.g. instead of NP [cat=N, bar=max,

case=Nom]– Usually other relation than ‘=‘ used for

comparison• E.g. ‘is compatible with’, ‘unifies with’, ‘subsumes’

32


• Replace rhs of rules by regular expressions over V (or AVMs)

• E.g. VP → V NP? (AP | PP)* abbreviates• VP → V, VP → V NP, VP → V APorPP, VP → V NP

APorPP,

• APorPP → AP APorPP, APorPP → PP APorPP, APorPP → AP, APorPP → PP

• Where APorPP is a ‘fresh’ virtual nonterminal

• Virtual : is discarded when constructing the trees

33


• My grammatical formalism has no PS rules!

• But only ‘lexical projection’ of syntactic selection properties (subcategorization list)

• E.g. buy: [cat=V, subcat = [_ NP PP, _ NP]] create PS rules on the fly

– If buy occurs in the input tokens, create rules

• VP → buy NP PP and VP → buy NP

– From the lexical entry

– And use these rules to parse34


• My grammar contains ε-rules:– NP → ε– Where ε stands for the empty string– (i.e. NP matches the empty string in the input

token list)

• Earley parser can deal with these!

• But extensive use creates many ambiguities!

35


• My grammar contains empty categories– Independent

• PRO as subject of non-finite verbs– PRO buying books is fun

• pro as subject of finite verbs in pro-drop languages– pro no hablo Español

• Pro as subject of imperatives– pro schaam je!

• Epsilon rules can be used or represent this at other level

36


• My grammar contains empty categories– Dependent

• trace of wh-movement – What did you buy t

• Trace of Verb movement (e.g V2 in Dutch, German, Aux movement in English

– Hij belt hem op t

– Did you t buy a book?

– Epsilon rules are not sufficient

37


• Other types (levels) of representation• LFG: (c-structure, f-structure)• HPSG: DAGs (special type of AVMs)• (constituent structure, semantic representation)

• Use CFG as backbone grammar– Which accepts a superset of the language– For each rule specify how to construct other level

of representation– Extend Earley parser to deal with this

38


• Other types (levels) of representation• f-structure, DAGs, semantic representations are not

finitely valued

• Thus it will affect efficiency

• But allows dealing with e.g.– Non-context-free aspects of a language

– Unbounded dependencies (e.g. by ‘gap-threading’)

39

Earley Parser in Practice

• Parsers for natural language yield – Many many parse trees for an input sentence

• Many more than you can imagine (thousands)

• Even for relatively short, simple sentences

• They are all syntactically correct

• But make no sense semantically

40

Earley Parser in Practice

• Additional constraining is required– To reduce the temporary ambiguities– To come up with the ‘best’ parse

• Can be done by semantic constraints– But only feasible for very small domains

• Is most often done using probabilities– Rule probabilities derived from frequencies in

treebanks 41

Parsers: Some Examples

• Dutch: Alpino parser

• Stanford parsers– English, Arabic, Chinese

• English: ACL Overview

42

Overview





43

Parsers & CLARIN

• Parser allows one to automatically analyze large text corpora

• Resulting in treebanks

• Can be used for linguistic research– But with care!!

• Example: Lassy Demo (Dutch)– Simple search interface to LASSY-small Treebank– Use an SVG compatible browser (e.g. Firefox)

44

Parsers & CLARIN

• Example of linguistic research using a treebank:

• Van Eynde 2009: A treebank-driven investigation of predicative complements in Dutch

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Thanks for your attention!

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Language and Speech Technology: Parsing

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