Pen Picture
What is Formal Linguistics?
Research
PhD supervision
Teaching
Publications
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Pen Picture
I came to Manchester to take a post as Professor of
Formal
Linguistics in the Centre for Computational Linguistics at UMIST in
1995. Since then there have been various institutional changes, so I am
now in the School of Computer Science at the University of Manchester,
though I'm still professor of formal linguistics and I still teach and
research in the same areas. My first degree, from the University of
Sussex,
was in Logic and I have an MSc in Logic from London University and a
PhD
in Artificial Intelligence from Sussex. I came to UMIST from University
College Dublin, where I was Professor of Artificial Intelligence.
What is Formal Linguistics?
I hold the post of Professor of Formal Linguistics. What this means is
that my research is directed at obtaining precise formal descriptions
of
the way that natural language works. People have studied language for
thousands
of years. The ability to use language is one of the major distinctions
between humans and other intelligent animals, and it is hardly
surprising
that people have long wanted to know how we do it. Until quite
recently,
however, most work in this area was rather imprecise. There simply was
no way of making theories of language precise, and there was no way of
subjecting linguistic theories to detailed extensive tests.
It is clear that language can be described at a variety of levels.
Languages
have structural properties which characterise when an utterance
is well-formed and when it is not. Some of these are about the
structure
of words -- about `morphology'. Questions such as why reconstructions
and unreconstructed, which are both derived from the underlying
word construct are legitimate English words, but unreconstructions
is not. Questions about the relationships between the Spanish words blanco,
blanca,
blancos
and blancas. Questions about why adding -ing to infer
leads to inferring, with a doubled r, whereas
enter
produces entering with a single r. Other structural
questions
deal with the way that words are put together to form sentences - what
is the relationship between I gave Mary a book and I gave a
book
to Mary, or between I believe Betty is a fool and Betty,
I
believe, is a fool? Why does I believe that she loves me with
all my heart sound much stranger than I believe with all my
heart
that she loves me, despite the fact that it actually has a much
simpler
grammatical structure? Why is the sentence The dessert I thought
was
revolting acceptable as part of a complex sentence such as I
enjoyed
the main course but the dessert I thought was revolting
but not as
a free-standing sentence?
They also have informational properties. Language would not
matter
if it couldn't be used for conveying ideas. If all we did when we
processed
language was assign structural analyses to words and sentences then it
would be no more than a parlour game - less interesting than chess or
draughts.
But that's not all we do. We recognise the decisions that lie behind
the
production of a particular utterance, and we know what these decisions
signify.
As a professor of formal linguistics, I am concerned to provide
precise
formal descriptions of as many aspects of language as I can. My work
is,
however, always informed by the realisation that structural
(syntactic/morphological)
choices encode information - that John loves Mary reports a
situation
where John has a particular set of attitudes towards Mary and says
almost
nothing about Mary's attitude to John, whereas Mary loves John
tells
us about Mary but not about John, and that Mary is loved by John
reports the same situation as John loves Mary but from a
different
point of view. My interests are the same as any linguist's; but I am
concerned
to state any theory I come up with using a framework which lends itself
to formality and precision.
What's this
got to
do with Computation?
Why am I so concerned to use a precise formal language for my
linguistic
descriptions? I believe that we cannot fully understand how language
works
unless we consider the processes that underly it. People have
studied
language for thousands of years, but until recently they did not have a
framework for describing processes, and for seeing how process-oriented
descriptions might work out. Computer science provides us with just
such
a framework. All other things being equal, a linguistic theory which
supports
a description of how to get from a surface form to a mental model is,
at
least for me, better than one that doesn't. Of course any such theory
should
cover as much of the data as possible - a computationally tractable
theory
that covers only a tiny fragment of what is going on in some language
is
no better than a theory which covers a much larger part of the language
with less rigour. But if you can describe a good part of a language and
realise
your theory as a computer program then it seems to me that
you have done something worthwhile.
As I said above, my research falls largely within the area of seeing
what happens when you try to formalise linguistic theories - does it
turn
out to be impossible, can you do it but only in ways that are
computationally
intractable, can you do it up to a certain point, ...? The aim of this
kind of research is partly to see what will be involved in writing
programs
that embody these theories, since the way a theory is formalised has
consequences
for its computational tractability. I believe, however, that you cannot
do this kind of work very effectively as an abstract study. To get a
real
understanding of what is involved in formalising some piece of
linguistic
theory, and of what the abstract results about ease or difficulty of
computing
with your formalisation mean, you have to actually do it.
I therefore actively work on developing programs which do structural
(syntactic and morphological) analysis, use the results of this to
compute
semantic representations, and then reason about why someone might have
said something with a specific semantic content in a given situation. I
think you have to do linguistics before you can stand back and
talk
about how it should be done; and I think that you probably need to have
something to say about all levels of linguistic description.
Otherwise
you will find yourself shovelling all the things you can't do into one
of the levels that you don't work on. Pragmatics, for instance, has
long
been a receptacle for everything that semanticists find too hard to
deal
with. But if you decide to work on semantics and pragmatics at the same
time, you have to actually face up to these problems, because you can't
pass them on to someone else - the someone else you'd like to pass them
to is yourself!
My recent work concentrates on topics in semantics, and in
particular
on the notion that in order to understand the significance of an
utterance
you have to think about its relationship to the rest of what you know -
to
your general knowledge of the world, to your understanding of the words
in the utterance, and to the (linguistic and extra-linguistic) context
in which it was uttered. In order to work in this area, you have to
have
access to an inference engine, i.e. to a program which can
perform
reasoning. My publications include several papers on theorem proving
for
a particularly expressive logic (the more expressive a logic, the
harder
it is to formalise the rules that you need for working with it. But
since
natural language is itself extremely expressive, you need a very
expressive
logic if you want to paraphrase it, and hence you have to take up the
challenge
of reasoning in such languages). But since the meaning of an utterance
is encoded at least partly in its grammatical structure you also need
to
have programs which can carry out grammatical analyses. I have a parser
and a grammatical framework which is particularly well-suited
to
languages with very free phrase order - languages such as Spanish and
Persian.
A unitary program for everything
I have a single program which does various amounts of linguistic
processing
for various languages. At present, it does a pretty good job of going
from
an English input sentence to reasoning about the circumstances under
which
someone might utter that sentence; and it does morphological and
syntactic
processing to various degrees for German, Spanish, Malay, Greek,
French,
Arabic and Persian. Most of my research students work on various
aspects
of this program. This seems to me to be a good idea: they get to do
interesting
work without having to implement all the basics (parsers, feature
description
languages, etc.) from scratch, and I get people to do work that I
actually
want done. Contact me if you'd like an evaluation copy of this: the
user
manual, which gets updated from time to time, is available here.
Computer Vision
I also have a long-standing interest in computer vision. This is not my
major research area, but I have at various times taught general AI
courses
for which I needed to cover a number of topics that lie outside my main
interests in language and reasoning. Since I can't bear to teach things
that I haven't implemented, I have developed a suite of tools for
extracting
information from images (the reason why I hate teaching things I
haven't
implemented is that I know from experience that the techniques
described
in textbooks always have limitations that the books don't discuss, and
the only way to find out what they are is by experimenting with them).
Some of these tools are fairly standard - things for doing edge
detection
and region growing, template matching algorithms, matching up elements
of successive frames from a sequence of images, and so on - but there
are
one or two things I'm quite pleased with. In particular, there is an
algorithm
for obtaining viewpoint independent representations of images which
makes
it possible to classify objects seen in silhouette no matter how they
have
been rotated. I have supervised a number of students in this area, and
am always very happy to supervise final-year undergraduate and MSc
projects
in computer vision.
PhD supervision
I have supervised PhD students in a variety of areas of artificial
intelligence, though in recent years I have generally concentrated on
language-oriented theses. The following list should give some idea of
the topics that I am happy to supervise.
-
Tariq Ahmad, Classification of Tweets using Multiple Thresholds
with Self-correction and Weighted Conditional Probabilities,
University of Manchester, 2019
-
Dena A Alabbad, An investigation into approaches to text-to-speech
synthesis for Modern Standard Arabic, University of Manchester,
2019
-
Fahad Albogamy, Structural analysis of Arabic tweets, University
of Manchester, 2018
-
Amal Mohammad Alshahrani, An investigation into the
cross-linguistic robustness of textual equivalence techniques,
University of Manchester, 2018
-
Ali Almiman, Natural language inference over dependency
trees, University of Manchester, 2017
-
Sardar Jaf, The application of constraint rules to data-driven
parsing, University of Manchester, 2015
-
Majed Alsabaan, Pronunciation support for Arabic learners,
University of Manchester, 2015
-
Iman Alsharhan, Exploiting phonological constraints and automatic
identification of speaker classes for Arabic speech
recognition, University of Manchester, 2014
-
Aparna Garg, Video event analysis using activity primitives, University
of
Manchester, 2013
-
Maytham Alabbas, Textual Entailment for Modern Standard Arabic, University
of
Manchester, 2013
-
Majda Al-Liabi, Computational Support for Learners of Arabic, University
of
Manchester, 2012
-
Yasser Muhammad Naguib Sabtan, Lexical
Selection for Machine Translation, University
of
Manchester, 2011
-
Chen-Li Kuo, Interpreting intonation
in English-Chinese spoken language translation, 2008,
-
Carlo
Lusuardi,
The
Use of NLP techniques in CALL for the diagnosis of specific errors
made by
learners
of FrenchUniversity
of
Manchester, 2008
-
Katherine Hargreaves: A
computational treatment of Somali morphosyntax, University of
Manchester, 2007
-
Wafa Idris: Toward the simulation of
multi-limbed robots, University of Manchester, 2006
-
Hanady Mansour Ahmed: Natural
Language Processing Engine for Arabic Text-to-speech, University
of Manchester, 2005
-
Ting Law: An investigation into
statistical approaches for the resolution of ambiguities in English,
University
of Manchester, 2005
-
John Kerins: Modelling temporal
discourses: towards the integration of semantic modelling
techniques into computer-assisted language learning software,
UMIST,
2004
-
Vahid Mirzaeian: Content-based
support for Persian learners of English, UMIST, 2003
-
Debora Field: A single action, an
infinity of effects: an investigation into reasoning-centred
planning for the purposes of planning dialogue without speech acts,
UMIST,
2003
-
Marie-Josee Hamel:
Re-using natural
language processing tools in computer assisted language learning,
UMIST,
2002
-
Andromache Areta: Robust parsing of
English spoken language, UMIST, 2002
-
Mathias Schulze:
Textana -- Grammar
and Grammar Checking in Parser-Based CALL, UMIST, 2001
-
Irina Reyero-Sans: Semantics of
Spanish spatial prepositions, UMIST, 1997
-
Robert
Gaizauskus: Deriving Answers
to Logical Queries by Answer Composition, Sussex, 1992
-
John Kelly: Artificial Intelligence:
a critical study, UCD, 1990
-
Sharon
Wood: Planning in a Rapidly
Changing Environment, Sussex, 1990
-
Chris
Thornton: Concept Learning as
Data Compression, Sussex, 1988
-
Anthony Robins:
Representation in
Connectionist Models, Sussex, 1988
My research has been published in various books
and papers . Links to copies of published articles are provided to
ensure timely
dissemination
of scholarly and technical work. Copyright and all rights therein are
retained
by the author(s) or by other copyright holders. All persons
copying
this information are expected to adhere to the terms and constraints
invoked
by each author's copyright. In most cases, these works may not be
reposted
without the explicit permission of the copyright holder.
Teaching
At the moment I teach first- and second-year courses on artificial
intelligence and an advanced course on semantics,
though
at various times I have taught courses in a wide variety of
other
areas, including computer vision and a general course in computational
linguistics. I played a significant role in the development of our the
UMIST BSc in Artificial Intelligence , which has been running since
October
1997.
I am the author of books on the use of logic in artificial
intelligence
(Formal Methods in Artificial Intelligence, Cambridge University
Press, 1988) and on the semantics of English (The Logical Structure
of English, Pitman, 1990), and co-author of books on using the
programming
language POP-11 for artificial intelligence, and have edited the
proceedings
of two AI conferences (Artificial Intelligence: Methodology,
Systems,
Applications VII, (proceedings AIMSA-96), IOS Press, 1996 and Prospects
for
Artificial Intelligence, (proceedings of AISB-93)).
Courses currently offered
CT116 Introduction to Artificial Intelligence
This is, obviously enough, an introduction to AI. There are different
ways
of thinking about AI - as a branch of computer science, as a way of
doing
cognitive science, as way of exploiting results from computer science,
and so on. The aim of this module is to set up the particular view of
AI
that underpins our degree. Thus the course covers a number of
philosophical
and methodological issues, rather than providing a rapid introduction
to
AI techniques. The
lecture notes (usually in a rather partial and messy form) can be
found
online (currently as a .pdf file - late releases of Internet Explorer
and
Netscape can cope with .pdf files, otherwise you'll have to download it
and use acroread to read it (or just double click to open it under
Windows)).
CT220 Programming in Prolog
Prolog is an excellent programming language for some tasks and a
dreadful
one for others. This course covers the major topics in Prolog and
illustrates
them with various applications for which Prolog is well suited. The aim
is to give an introduction to various AI topics (NLP, theorem proving,
planning) and to Prolog at the same time. I don't know how well it
works
because this is the first year I've given this course (and indeed the
first
time I've taught a course on programming for a very long time). The teaching
materials are available online.
L2013 Knowledge Rep Practical
Reasoning and knowledge representation are a key topic in Artificial
Intelligence.
A very large part of AI is concerned with representing and using
knowledge,
and one of the best ways (the best way ???) of doing this is to use the
formalisms developed by logicians. I currently teach a second year
course
aimed at getting students to implement a number of well-known
knowledge-representation
techniques. It's all very well telling students how to do things, but
you
won't really understand what to do until you try it. This is a
course
where students produce quite substantial programs, working in groups.
At
the end of this course, students have first-hand experience of what is
involved in producing serious AI systems, and they also have a much
deeper
appreciation of the limits of AI techniques. AI textbooks tell you how
to do things, but they never admit how flawed and fragile the resulting
systems are. Doing it yourself tells you more than any book (or any
teacher)
possibly can.
CT322 Computational Representations of
Meaning
This course explores the idea that what matters about a sentence is
what
you can infer when you hear it. It therefore concentrates on showing
how
to construct formal paraphrases of natural language utterances, since
we
can only explore the notion of whether P follows from Q if P and Q are
expressed in some formal (logical) language. A large part of the course
deals with the extreme subtlety of natural language, and the
consequences
of this for the idea that we can reasonably provide formal paraphrases
in any formal language. Again I believe that such a course
should
at least be backed up by programs which construct and manipulate the
kinds
of representation being discussed. Students therefore have access to
the
program described above , and to a number of smaller systems which
illustrate
specific points. The teaching
material for this course is available on-line.
Last updated: 16 March 2007
