Dependency Parsing - COM6513 Natural Language Processing · Relation extraction, e.g. identify...

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Dependency ParsingCOM6513 Natural Language Processing

Nikos [email protected]

@nikaletras

Computer Science Department

Week 5Spring 2021

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In previous lectures...

Text Classification: Given an instance x (e.g. document),predict a label y ∈ YTasks: sentiment analysis, topic classification, etc.

Algorithm: Logistic Regression

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In previous lectures...

Sequence labelling: Given a sequence of wordsx = [x1, ...xN ], predict a sequence of labels y ∈ YN

Tasks: part of speech tagging, named entity recognition, etc.

Algorithms: Hidden Markov Models, Conditional RandomFields

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In this lecture...

Model richer linguistic representations: graphs

Dependency parses: Graphs representing syntactic relationsbetween words in a sentence

Two approaches:

Graph-based Dependency ParsingTransition-based Dependency Parsing

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Dependecny Parsing: Applications

Relation extraction, e.g. identify entity pairs (AM, ArcticMonkeys), (Abbey Road, Beatles), (Different Class, Pulp)with the relation music album by

Question answering

Sentiment analysis

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What is a Dependency Parse?

Dependency parse (or tree): Graph representing syntacticrelations between words in a sentence

Nodes (or vertices): Words in a sentence

Edges (or arcs): Syntactic relations between words, e.g. dog

is the subject (nsubj) of likes (list of standard dependencyrelations)

Dependency Parsing: Automatically identify the syntacticrelations between words in a sentence

https://wiki.opencog.org/w/Dependency_relations

https://wiki.opencog.org/w/Dependency_relations

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Graph constraints

Connected: every word can be reached from any other wordignoring edge directionality

Acyclic: can’t re-visit the same word on a directed path

Single-Head: every word can have only one head

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Well-formed Dependency Parse?

Connected?

NO

Acyclic? YES

Single-headed? YES

Solution?

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Connected? NO

Acyclic? YES

Single-headed? YES

Solution?

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Connected? NO

Acyclic?

YES

Single-headed? YES

Solution?

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Connected? NO

Acyclic? YES

Single-headed? YES

Solution?

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Connected? NO

Acyclic? YES

Single-headed?

YES

Solution?

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Connected? NO

Acyclic? YES

Single-headed? YES

Solution?

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Well-formed Dependency Parse

Add a special root node with edges to any nodes without heads(main verb and punctuation).

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Dependency Parsing: Problem setup

Training data is pairs of word sequences (sentences) anddependency trees:

Dtrain = {(x1,G 1x )...(xM ,G M

x )}xm = [x1, ...xN ]

graph Gx = (Vx,Ax)

vertices Vx = {0, 1, ...,N}edges Ax = {(i , j , k)|i , j ∈ V , k ∈ L(labels)}

We want to learn a model to predict the best graph:

Gx = arg maxGx∈Gx

score(Gx, x)

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Learning a dependency parser


Gx = arg maxGx∈Gx

score(Gx, x)

where the Gx is a well-formed dependency tree.

Can we learn it using what we know so far?

Enumerationover all possible graphs will be expensive.

How about a classifier that predicts each edge? Maybe.But predicting an edge makes some edges invalid due to theacyclic and single-head constraints.

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Gx = arg maxGx∈Gx

score(Gx, x)


Can we learn it using what we know so far? Enumerationover all possible graphs will be expensive.


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Gx = arg maxGx∈Gx

score(Gx, x)



How about a classifier that predicts each edge?

Maybe.But predicting an edge makes some edges invalid due to theacyclic and single-head constraints.

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Gx = arg maxGx∈Gx

score(Gx, x)




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Maximum Spanning Tree

Spanning Tree: In graph theory, a spanning tree T of anundirected graph G is a subgraph that includes all of thevertices of G, with the minimum possible number of edges.

Tree: In computer science, a tree is a widely used datastructure (Abstract Data Type) that simulates a hierarchicaltree structure, with a root value and subtrees of children witha parent node, represented as a set of linked nodes.

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Maximum Spanning Tree

Score all edges, but keep only the max spanning tree usingChu-Liu-Edmonds algorithm, a modification to Kruskal’s algorithmfor extracting Maximum Spanning Trees.

Exact solution in O(N2) time using Chu-Liu-Edmonds.

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Kruskal’s algorithm

Input scored edges E

sort E by cost (opposit of score)

G = {}while G not spanning do:

pop the next edge e

if connecting different trees :

add e to G

Return G

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Graph-based Dependency Parsing

Decompose the graph score into arc scores:

Gx = arg maxGx∈Gx

score(Gx, x)

= arg maxGx∈Gx

w · Φ(Gx, x) (linear model)

= arg maxGx∈Gx

∑(i ,j ,l)∈Ax

w · φ((i , j , l), x) (arc-factored)

Can learn the weights with a Conditional Random Field!

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Feature Representation

What should φ((head , dependent, label), x) be?

unigram: head=had, head=VERB

bigram: head=had & dependent=effect

head=VERB & dependent=NOUN & between=ADJ

head=had & label=dobj & other-label=nsubj

NO!!! Breaks the arc-factored scoring

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What should φ((head , dependent, label), x) be?

unigram: head=had, head=VERB

bigram: head=had & dependent=effect

head=VERB & dependent=NOUN & between=ADJ

head=had & label=dobj & other-label=nsubjNO!!! Breaks the arc-factored scoring

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More Global models

Even though inference and learning are global, features arelocalised to arcs.

Can we have more global features?

Yes we can! Considersubgraphs spanning a few edges. But inference becomesharder, requiring more complex dynamic programs and cleverapproximations.

Is it worth it? Syntactic parsing has many applications, thusbetter compromises between speed and accuracy are alwayswelcome!

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More Global models

Even though inference and learning are global, features arelocalised to arcs.

Can we have more global features? Yes we can! Considersubgraphs spanning a few edges. But inference becomesharder, requiring more complex dynamic programs and cleverapproximations.

Is it worth it? Syntactic parsing has many applications, thusbetter compromises between speed and accuracy are alwayswelcome!

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Transition-based Dependency Parsing

Graph-based dependency parsing restricts the features toperform joint inference efficiently.

Transition-based dependency parsing trades joint inferencefor feature flexibility.

No more argmax over graphs, just use a classifier with anyfeatures we want!

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Joint vs incremental prediction

Joint: score (and enumerate) complete outputs (graphs)

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Joint vs incremental prediction

Incremental: predict a sequenceof actions (transitions)constructing the output

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Transition system

The actions A the classifier f can predict and their effect on thestate which tracks the prediction: St+1 = S1(α1 . . . αt)

What should the actions (transitions) be for dependency parsing?

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Transition system setup

Input: Vertices Vx = {0, 1, ...,N} (words sentence x)

State S = (Stack,B,A):

Arcs A (dependencies predicted so far)Buffer Buf (words left to process)Stack Stack (last-in, first out memory)

Initial state: S0 = ([], [0, 1, ...,N], {})Final state: Sfinal = (Stack , [],A)

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Transition system

Shift (Stack , i |Buf ,A)→ (Stack|i ,Buf ,A): push next word fromthe buffer (i) to stack

Reduce (Stack|i ,Buf ,A)→ (Stack,Buf ,A): pop word top of thestack (i) if it has a head

Right-Arc(label) (Stack|i , j |Buf ,A)→(Stack |i |j ,Buf ,A ∪ {(i , j , l)}): create edge (i , j , label) between topof the stack (i) and next in buffer (j), push j

Left-Arc(label) (Stack|i , j |Buf ,A)→ (Stack, j |Buf ,A ∪ {(j , i , l)}):create edge (j , i , label) and pop i , if i has no head

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Transition system


Reduce (Stack|i ,Buf ,A)→ (Stack ,Buf ,A): pop word top of thestack (i) if it has a head


Left-Arc(label) (Stack|i , j |Buf ,A)→ (Stack, j |Buf ,A ∪ {(j , i , l)}):create edge (j , i , label) and pop i , if i has no head

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Transition system


Reduce (Stack|i ,Buf ,A)→ (Stack ,Buf ,A): pop word top of thestack (i) if it has a head


Left-Arc(label) (Stack |i , j |Buf ,A)→ (Stack, j |Buf ,A ∪ {(j , i , l)}):create edge (j , i , label) and pop i , if i has no head

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Example

Stack = []Buffer = [ROOT, Economic, news, had, little, effect, on, financial,markets, .]

Action?

Shift

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Example

Stack = []Buffer = [ROOT, Economic, news, had, little, effect, on, financial,markets, .]

Action? Shift

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Example

Stack = [ROOT]Buffer = [Economic, news, had, little, effect, on, financial,markets, .]

Action?

Shift

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Example

Stack = [ROOT]Buffer = [Economic, news, had, little, effect, on, financial,markets, .]

Action? Shift

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Example

Stack = [ROOT, Economic]Buffer = [news, had, little, effect, on, financial, markets, .]

Action?

Left-Arc(amod)

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Example

Stack = [ROOT, Economic]Buffer = [news, had, little, effect, on, financial, markets, .]

Action? Left-Arc(amod)

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Example

Stack = [ROOT]Buffer = [news, had, little, effect, on, financial, markets, .]

Action?

Shift

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Example

Stack = [ROOT]Buffer = [news, had, little, effect, on, financial, markets, .]

Action? Shift

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Example

Stack = [ROOT, news]Buffer = [had, little, effect, on, financial, markets, .]

Action?

Left-Arc(nsubj)

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Example

Stack = [ROOT, news]Buffer = [had, little, effect, on, financial, markets, .]

Action? Left-Arc(nsubj)

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Example

Stack = [ROOT]Buffer = [had, little, effect, on, financial, markets, .]

Action?

Right-Arc(root)

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Example

Stack = [ROOT]Buffer = [had, little, effect, on, financial, markets, .]

Action? Right-Arc(root)

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Example

Stack = [ROOT, had]Buffer = [little, effect, on, financial, markets, .]

Action?

Shift

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Example

Stack = [ROOT, had]Buffer = [little, effect, on, financial, markets, .]

Action? Shift

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Example

Stack = [ROOT, had, little]Buffer = [effect, on, financial, markets, .]

Action?

Left-Arc(amod)

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Example

Stack = [ROOT, had, little]Buffer = [effect, on, financial, markets, .]

Action? Left-Arc(amod)

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Example

Stack = [ROOT, had]Buffer = [effect, on, financial, markets, .]

Action?

Right-Arc(dobj)

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Example

Stack = [ROOT, had]Buffer = [effect, on, financial, markets, .]

Action? Right-Arc(dobj)

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Example

Stack = [ROOT, had, effect]Buffer = [on, financial, markets, .]

Action?

let’s fast-forward...

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Example


Action? let’s fast-forward...

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Example

Stack = [ROOT, had, .]Buffer = []

Empty buffer.

DONE!

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Example

Stack = [ROOT, had, .]Buffer = []

Empty buffer. DONE!

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Other transition systems?

This was the arc-eager system. Others:

arc-standard (3 actions)easy-first (not left-to-right), etc.

All operate with actions combining:

moving words from the buffer to the stack and back(shift/un-shift)popping words from the stack (reduce)creating labeled arcs left and right

Intuition: Define actions that are easy to learn

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arc-standard (3 actions)

easy-first (not left-to-right), etc.




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arc-standard (3 actions)easy-first (not left-to-right), etc.




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Input: sentence x

state S1 = initialize(x); timestep t = 1

while St not final do

action αt = arg maxα∈A

f (α,St)

St+1 = St(αt); t = t + 1

What is f ?

A multiclass classifierWhat do we need to learn it?

learning algorithm (e.g. logistic regression)

labelled training data

feature representation

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Input: sentence x

state S1 = initialize(x); timestep t = 1

while St not final do

action αt = arg maxα∈A

f (α,St)

St+1 = St(αt); t = t + 1

What is f ? A multiclass classifierWhat do we need to learn it?

learning algorithm (e.g. logistic regression)

labelled training data

feature representation

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What are the right actions?

We only have sentences labelledwith graphs:Dtrain = {(x1,G 1

x )...(xM ,G Mx )}

Ask an oracle to tell us theactions constructing the graph!

In our case, a set of rules comparing the current stateS = (Stack ,Buffer ,ArcsPredicted) with Gx returning the correctaction as label

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x )...(xM ,G Mx )}


In our case, a set of rules comparing the current stateS = (Stack,Buffer ,ArcsPredicted) with Gx returning the correctaction as label

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x )...(xM ,G Mx )}


In our case, a set of rules comparing the current stateS = (Stack ,Buffer ,ArcsPredicted) with Gx returning the correctaction as label

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Learning from an oracle

Given a labelled sentence and a transition system, an oracle returnsstates labelled with the correct actions.

Dtrain = {(x1,G 1x )...(xM ,G M

x )}xm = [x1, ..., xN ]

graph Gx = (Vx,Ax)

vertices Vx = {0, 1, ...,N}edges Ax = {(i , j , k)|i , j ∈ V , k ∈ L(labels)}

states Sm= [S1, ...,ST ]

actions αm= [α1, ..., αT ]

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What features would help us predict the correction actionRight-Arc(prep)?

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Words/PoS in stack and buffer:wordS1=effect, wordB1=on, wordS2=had, posS1=NOUN,etc.

Dependencies so far:depS1=dobj, depLeftChildS1=amod,depRightChildS1=NULL, etc.

Previous actions:αt−1 = Right-Arc(dobj), etc.

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Transition-based vs Graph-based parsing

Transition-based tends to be better on shorter sentences,graph-based on longer ones

Graph-based tends to be better on long-range dependencies

Graph-based lacks the rich structural features

Transition-based is greedy and suffers from early mistakes

Actually, can we ameliorate the greedy issue?

Use Beam Search!

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Transition-based vs Graph-based parsing

Transition-based tends to be better on shorter sentences,graph-based on longer ones

Graph-based tends to be better on long-range dependencies

Graph-based lacks the rich structural features

Transition-based is greedy and suffers from early mistakes

Actually, can we ameliorate the greedy issue?Use Beam Search!

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Non-Projectivity

Arcs are crossing each other

long-range dependencies

free word order

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Non-projective Transition-based parsing

The standard stack-based systems cannot do it.

But there are extensions:

swap actions: word reoderingk-planar parsing: use multiple stacks (usually 2)

Standard graph-based parsing handles non-projectivity.

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Incremental Language Processing

Other problems solved with similar approaches (a.k.a.transition-based, greedy):

semantic parsing (converting a natural language utterance to alogical form)coreference resolution

Whenever you have a problem with a very large space ofoutputs, worth considering

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Evaluation

Head-finding word-accuracy:unlabelled: % of words with the right headlabelled: % of words with the right head and label

Sentence accuracy: % of sentences with correct graph

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Bibliography

Chapter 11 from Eisenstein

Nivre and McDonald’s tutorial slides

Nivre’s article on deterministic transition-based dependencyparsing

Nivre and McDonald’s paper comparing their approaches

https://github.com/jacobeisenstein/gt-nlp-class/blob/master/notes/eisenstein-nlp-notes.pdf

http://stp.lingfil.uu.se/~nivre/eacl14.html

http://www.mitpressjournals.org/doi/pdf/10.1162/coli.07-056-R1-07-027

http://www.aclweb.org/anthology/D07-1013

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Coming up next week...

Feed-forward Neural Networks

Getting ready for Assignment 2!

Dependency Parsing - COM6513 Natural Language Processing · Relation extraction, e.g. identify...

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