Transcript A Probabilistic Representation of Systemic Functional Grammar

A Probabilistic Representation of
Systemic Functional Grammar
Robert Munro
Department of Linguistics,
SOAS,
University of London
Outline
Introduction
Functions in the nominal group
Machine learning
Testing framework
Classification vs unmarked function
Gradational realization
Delicacy
Conclusions
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Introduction
An exploration of the ability of machine
learning to learn and represent functional
categories as fundamentally probabilistic
Gauged in terms of the ability to:
computationally learn functions from labeled
examples and apply to new texts.
represent functions probabilistically: a gradation
of potential realization between categories.
explore finer layers of delicacy.
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Functions in the nominal group
Functions:
Deictic, Ordinative, Quantitative, Epithet,
Classifier, Thing (Halliday 1994)
Deictic Ordin.
The
first
Quant.
Epith.
Class.
Thing
three
tasty
red
wines
Gradations:
Here, ‘red’ functions also functions as an Epithet.
The uptake of such marked classifiers will not be
uniform.
Overlap does not necessarily limit significance.
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Machine Learning
Machine learning:
computational inference from specific examples.
A learner named Seneschal was developed
for the task here:
probabilistic
seeks sub-categories (improves both
classification and analysis)
allows categories to overlap
not too dependent on the size of the data set
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Machine Learning
The task here:
Given categories &
with known values for x
and y, infer a probabilistic model (potentially with
sub-categories) that can classify new examples:
x
?
?
?
?
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?
?
?
y
Machine Learning
It is important that attributes (x,y,z...) :
represent features that distinguish functions
can be discovered automatically (for large scales)
are meaningful for analysis…?
Compared to manually constructed parsers:
greater scales than are practical
more features/dimensions than are possible
(100’s are common)
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Testing Framework
The model was learned from 10,000 labeled
words from Reuters sports newswires from
1996
23 features:
part-of-speech and its context
punctuation
group / phrase contexts
collocational tendencies
probability of repetition
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Testing Framework
 Accuracy:
 The ability to correctly identify the dominant
function in 4 test corpora (1,000 words each):
1.
2.
3.
4.
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Reuters Sports Newswires (1996)
Reuters Sports Newswires (2003)
Bio-informatics abstracts
Extract from Virginia Woolf’s ‘The Voyage Out’
Testing Framework
Gradational model of realization:
calculated as the probability of a word realizing
other functions, averaged between all clusters.
Finer layers of delicacy:
Manual analysis of clusters found within a
function.
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Unmarked function
Unmarked function: function defined by only
part-of-speech (POS) and word order.
eg: adjective = Epithet, non-final noun = Classifier
Previous functional parsers have assumed that
most instances are unmarked:
POS taggers are almost 100% accurate
word order is trivial
…so the problem is solved?
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Unmarked function
This is a false assumption.
Across the corpora:
< 40% of non-final adj’s realized Epithets
< 50% of Classifiers were nouns
44% of Classifiers were ‘marked’!
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Unmarked function
This task halved the classification error:
25%
Un m a r k e d B a s e lin e
le a r n in g r a te
e rro r
20%
15%
10%
5%
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p e rc e n t o f tra in in g s e t
%
90
%
80
%
70
%
60
%
50
%
40
%
30
%
20
%
10
1%
0%
Gradational Realization
Thing
With relationships
Nominal
Although
functions
describedexisting
are
as typically
probabilistic,
between
represented
all
deterministically:
functions
Class.
Deictic Ordin.
The
first
Quant.
Epith.
Class.
Epith.
Thing
three
tasty
red
wines
Quant.
Ordin.
Deictic
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Delicacy
Demonstrative
Deictic
Possessive
Ordinative
Numerative
Tabular
Quantitative
Discursive
Epithet
Expansive
Classifier
Hyponymic
First Name
Named Entity
Intermediary
Last Name
Thing
Group-Releasing
Nominative
non-Nom.
Nominal
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Stated
Described
Delicacy
More delicate functions for Classifiers
(Matthiessen 1995) :
Hyponymic: describing a taxonomy or general
‘type-of’ relationship eg: ‘red wine’, ‘gold medal’,
‘neural network architecture'
Expansive: expands the description of the
Head. eg: ‘knee surgery’, ‘optimization problems',
‘sprint champion’,
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Delicacy
DDe
emico
tin
Po c
ss
Or
d
ntic
meoic
DeD
ss
Po
d
Or
.
dlainr
Obru
Ta
c
Dis
ith
E p ant .
s
Qpuan
Ex
n
po
Hy
e
am t
F.N ithe
Epm e
a
I.N
e
am
L.N
r
m ifie
No lass
C N
nNo
ted
Sta
r
sc g
DeThin
TOar
bduin
la.
Dis r
c
Ep
ith
EQxua
pant
ns.
Hy
po
n
F.N
a
E m
I.Npith e
amet
e
L.N
am
No e
Clm
Noassif
n- N ier
Sta
ted
De
Thscr
ing
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Delicacy
More delicate descriptions can be found:
more features
more instances / registers
other algorithms / parameters
Methodology can be applied to:
other parts of a grammar
other languages
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Conclusions
Gradational modeling of functional realization
is desirable
Sophisticated methods are necessary for
computationally modeling functions:
Markedness is common
Machine learning is a useful tool and
participant in linguistic analysis.
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Thank you
Acknowledgments:
Geoff Williams
Sanjay Chawla
The slides and extended paper will be
published at:
www.robertmunro.com/research/
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