The story of how a graduate student went from formalism to data, a brief tale of how engineering without tradition can lead thought in the right direction, and a mild caution of how intellectual skepticism is worth little without a corresponding dose of intellectual enthusiasm

Karlgren, Jussi

English

Swedish Institute of Computer Science Publications Database

1ABSTRACT
The story of how a graduate student went from formalism to data, 
a brief tale of how engineering without tradition can lead thought in
the right direction, and a mild caution of how intellectual skepticism 
is worth little without a corresponding dose of intellectual enthusiasm.
When I first came to DSV in the Fall of 1987 I was looking for
diversion. I was busy finishing up my Bachelor's degree in
Computational Linguistics and felt I needed classes to broaden my
perspective.
This was at a time when DSV was within easy reach for curi-
ous students from other departments – located on campus at the
university instead of in an industrial park, so I could simply walk
over from my department to the other hallway and look for inter-
esting things to think about. DSV had plenty to offer, and I signed
up for a class called “Artificial intelligence programming” which,
as it turned out, taught the basics of structured programming in
high-level languages such as Prolog, Lisp, and Smalltalk. It was
taught by Carl Gustav Jansson and several seasoned graduate stu-
dents – most of whom today indeed have graduated and many of
whom still can be found at the department or in its close vicinity. 
Even today the sense of awe and possibility the classes instilled
in me is easy to recollect. The course books are still some of my
favourite volumes: “The Art of Prolog” and “The Structure of
Computer Programs”. One with Asian artwork, the other with
crudely drawn wizards on the cover. The contents were no less
inspiring – they expressed the sense that everything was possible
given the appropriate computational model. Wizards! Exotism!
Fun!  
This is where the subtitle above comes in: an apocryphal anec-
dote tells us what the then King of England (it is unclear which
one – the story is attributed to various monarchs) said when first
confronted with the design of St Pauls cathedral: “Amusing,
Awful, and Artficial”. The king intended this as praise, meaning
that the building was pleasantly thought-provoking, awe-inspiring,
FROM BOXES AND ARROWS TO CONVERSATION AND NEGOTIATION
Or how Research should be Amusing, Awful, and Artificial
Jussi Karlgren
Jussi Karlgren is a researcher at
the Swedish Institute of Computer
Science where he specializes in
the study of text and interaction,
especially as regards reader
understanding of topicality and
stylistic features of text. 
He holds a candidate degree 
in Computational Linguistics 
and Mathematics, a licentiate
degree in Computer and Systems
Sciences, and a doctorate in
Computational Linguistics.
The Art of Prolog
Bildförslag: En av de nämnda
kursböckerna. Jag har ännu inte
fått boken från institutionen.
dsvbok jussi karlgren.qxp  2006-08-28  00:59  Sida 1
2and artful. The words have since shifted in meaning (which is why
the story is told today) but the combination of the three is what
most of us in research strive for in what we do. We would do well
if we more often and more explicitly thought along those lines.
Linguistics is a methodical area of study. 
Linguistics is mostly about applying formal models to arguably
that most fluid of symptoms of human intellectual activity: verbal
communicative behaviour. But linguists expend or used to expend
enormous effort on debating the absolute merits of various differ-
ent formalizations rather than working on applications of the
same. At the time I am writing about, linguists in general, and
leading researchers here at the department in Stockholm in partic-
ular, had already started turning to more data-oriented and less
theory-oriented methodologies: corpus linguistics, focusing on the
methodology of collecting data; typology, focusing on the differ-
ences and relations between languages rather than the specifics.
But this reform had not yet percolated down into the undergradu-
ate curriculum, nor made its way into basic textbooks. I had spent
a long time trying to make sense of arguments of formalism rather
than understanding people, how they speak, and how they think. 
The field of artificial intelligence in the eighties was neither
straight-laced nor crabby. Thinking about it, I can still feel that
rush of boundless potential I experienced in taking those first
classes. I still believe in those first realizations. Yes, we can model
human cognition! Since I had already burnt my fingers on the
fruitless representational debates of linguistics at the time I knew
from the start that finding the perfect model counts for little in the
end, whereas representing the salient aspects of behaviour does.
So far, this, of course, has been a common story, told and told
again: a young and enthusiastic graduate student finds that many
of the theories of the preceding generation of researchers is over-
loaded with ideology and not sensitive enough to the reality they
model. That realization, in fact, drives much of research. The risk
every disillusioned researcher runs in this situation is to tumble
into improductive agnosticism: collect data, gripe about dysfunc-
tional models, and do nothing. In the words of a traditional lin-
guist: “Viel Data und wenig Theorie”. Skepticism rampant leads
nowhere! 
From DSV to SISU
I continued studying at DSV taking classes such as “Knowledge
representation and reasoning”. After gaining my degree I signed
up as a graduate student at the department in Carl Gustaf Jansson’s
research lab (which was more difficult than it sounds – Anne
Marie Philipson spent a fair amount of effort to help me dodge the
bureaucractic obstacles). I almost immediately found employment
at SISU, a research institute closely associated with DSV. 
SISU busied itself mainly with applications of conceptual 
JUSSI KARLGREN
“ In the words of a traditional 
linguist:‘Viel Data und wenig
Theorie’.
dsvbok jussi karlgren.qxp  2006-08-28  00:59  Sida 2
3modelling, building models of human knowledge using a general-
ized entity –relationship framework. The approach taken at SISU
was to build models that could be put to use. Their exact form and
principles were of less consequence than their quality – and this
was best assured through judicious editing and non-trivial intellec-
tual investment on the part of the knowledge engineer. The project
I worked in was a project at IBM, to build a written English-
language dialogue interface for a relational database. To make it
work, it needed a fairly complete model of the domain it was to be
employed in. The amount of human knowledge engineering put
into constructing conceptual models at SISU, while quite rich,
practical, and useful, was neither Artificial, Amusing, nor Awful
for those of us who, like me, were more interested in modeling
conceptual behaviour than in representation. I was looking for
mechanisms to model automatic acquisition and maintenance of
conceptual structures. Many of the graduate students at DSV
seemed to work with exciting machine learning experiments –
much more Artificial, Amusing, and Awful as far as I could tell. 
How find The Optimal Representation?
So I gave up the job at SISU, better to study the behaviour of
human communication, and returned to DSV. Back at the depart-
ment – still located in its less than stylish but intellectually stimu-
lating environment on campus – I spent hours on end in the cafete-
ria, in Jacob Palme’s classes and seminars on computer-mediated
human communication, and on the Usenet discussing Chinese
rooms (Searle, 1980; Harnad, 1989), human communicative abili-
ties and human cognition. Is adequate communicative behaviour a
sufficient condition to postulate cognitive realism? If something
passes the Turing test  – is that something sentient? And is it not
sentient if it fails the test? Computer science is not equipped to
answer those questions without reaching out to other areas of
study. Fishing about, I found parallel distributed programming and
connexionism, which seemed to be the answer to many of my
doubts (Rumelhart and McClelland, 1986). Here I found a repre-
sentation intended to model behaviour rather than formal charac-
teristics! I went to a Summer school to further study neural net-
works and connectionist representations where details of the repre-
sentation were carefully and anxiously scrutinized by leading pro-
ponents of the field with regard to their orthodoxy of representa-
tion. That summer school cured me of that remedy, and in conse-
quence of all remedies. This is one of the major lessons I learnt in
my early graduate years: I ceased to search for The Optimal
Representation and have since then used numerous different repre-
sentations and conceptualizations of data in the projects I have
worked on.
My main project in the late eighties concerned recommender
systems (Karlgren, 1990). I built prototypes to use algebraic
FROM BOXES AND ARROWS TO CONVERSATION AND NEGOTIATION
 The Turing test
was introduced by Alan M. Turing
(1912-1954) in his 1950 article
“Computing Machinery and
Intelligence” (Mind, Vol. 59, No.
236, pp. 433-460) 
TheTuring Test is meant to deter-
mine if a computer program has
intelligence.
A group of “natural language”
researchers, among them Jussi
Karlgren worked at the IBM labo-
ratory on the island of Lidingö 
outside Stockholm. The informa-
tion pamphlet about the project 
is from 1988.
dsvbok jussi karlgren.qxp  2006-08-28  00:59  Sida 3
4analysis to extract user preferences from observations on user
information access behaviour: the objective was to build a system
which would recommend users a document based on both their
individual reading behaviour and on the reading behaviour of
other users like them. The behaviour I wanted to model is that of 
a competent librarian: “If you like that book, you might like this
one.” As it turned out, data to get the system in the air were diffi-
cult to come by. Even today, with the WWW in general use, boot-
strapping is a major task for the productive deployment of recom-
mender systems; then, obtaining data, any data, was a real chal-
lenge. And in fact, data collection has been a major bottleneck for
any fruitful representation and modeling of intellectual behaviour:
it is difficult to obtain realistic amounts of data to prove or dis-
prove the models in question. In the end, a few years later, I ended
up using .newsrc files extracted from my colleagues home directo-
ries (Karlgren, 1994 a) which rendered me a severe dressing down
by reviewers who felt I had been disrespectful of my colleagues'
integrity. This project used simple raw data, manipulated them
minimally, and extracted knowledge from them in the simplest
manner conceivable. Today, numerous commercial implementa-
tions operate using these and similar algorithms. An interesting
facet of intelligent behaviour can be modeled with simple tools –
if the data are there. 
Research at SICS
My experiences with conceptual modeling made me think about
how to transmit the fairly stable representations to the more flexi-
ble counterpart in conversation – how to teach the user the repre-
sentation the system purported to have learnt from users. I went
back to the project I had worked on with SISU (which in mean-
while had completed its system development phase without my
help and were shipping systems to delighted users) and studied
how users mirrored the linguistic usage of systems. Under Carl
Gustaf Jansson´s supervision, this gave me my licentiate degree
and a platform for several years of continued research at SICS,
another research institute in the department's vicinity, where I still
work (Karlgren, 1992). I continued to study how a system might
be built to express itself through superficially spurious and redun-
dant conversational moves and thereby enrich the interaction
(Karlgren, 1994 b). The idea was to use a stable, fixed, and
unchangeable conceptual model as a base, and to build an interac-
tion module which traversed it in cases where user input was
ambiguous or inexact, mumbling about related content: “Salaries
for consultants? Consultants have costs. Employees have salaries.”
Here, even a simple symbolic interaction module with a static rep-
resentation could produce something which gave a much more
flexible interaction model. Again, an interesting facet of intelligent
communicative behaviour can be modeled with simple tools – if
the knowledge of counterpart adaptivity is there. 
JUSSI KARLGREN
dsvbok jussi karlgren.qxp  2006-08-28  00:59  Sida 4
5The trap of doubting everything and building nothing
So these were the more important lessons I have learned during
my time at DSV. Most of the pieces of the puzzle were there all
along. It has taken me a while to compose a picture out of them.
Some things I know now: First, and this is a personal lesson I
have chosen to learn, knowledge, whether in human or machine, is
to be made and created on the fly. Second, no representation is
better than its use. It took a number of false starts to learn this. I
am not alone in this insight: several other contributions in this vol-
ume will bear witness to the same intellectual progression. 
Third, and this is the most general one, and one I would like to
have other young researchers remember: we cannot do sensible
things if we do not try for awful, amusing and artificial! While we
should be wary of representational conviction, spending time dis-
carding representation after representation is not enough. One
should not tumble into the trap of doubting everything and build-
ing nothing. There is nothing amusing, awful, nor artificial in cri-
tique alone, deconstruction is not a sufficient goal for research,
and skepticism needs to be tempered with enthusiasm to lead any-
where. 
The road from representational pettiness via the unproductive
reaches of viel Data und wenig Theorie to a theory of data, its use
and the behaviour of its users is long and no less laborious than
the work involved in proving or disproving theories. But it is more
productive! And turning back to the theme of this present volume:
how did we get here? The group at DSV running research projects
on representation and reasoning proceeded seamlessly from one
false start to another – typically for computer science, with no 
preset intellectual compass founded in old schools of thought or
solid convictions, luckily the then young researchers never got
bogged down into limiting and constraining tradition. The short
and unfettered intellectual direction coupled with the enthusiasm
for experimental engineering that is computer science is to thank
for much! 
References
Harnad, Stevan. 1989. Minds, Machines and Searle. Journal of
Theoretical and Experimental Artificial Intelligence 1: 5-25. 
Karlgren, Jussi. 1990. An Algebra for Recommendations: Using
Reader Data as a Basis for Measuring Document Proximity. SYSLAB
Technical Report 179. Stockholm University, Stockholm, Sweden. 
Karlgren, Jussi. 1992. The Interaction of Discourse Modality and User
Expectations in Human-Computer Dialog. Licentiate thesis, depart-
ment of Computer and Systems Sciences, Stockholm University
Karlgren, Jussi. 1994 b. Mumbling – User-driven Cooperative
Interaction. SICS Technical Report T94:01. Swedish Institute of
Computer Science, Stockholm, Sweden.
FROM BOXES AND ARROWS TO CONVERSATION AND NEGOTIATION
“ There is nothing amusing, awful,
nor artificial in critique alone,
deconstruction is not a sufficient
goal for research, and skepticism
needs to be tempered with 
enthusiasm to lead anywhere.”
dsvbok jussi karlgren.qxp  2006-08-28  00:59  Sida 5
6Karlgren, Jussi. 1994 a. Newsgroup Clustering Based On User
Behavior – A Recommendation Algebra. SICS Technical Report
T94:04. SICS, Stockholm, Sweden.
Rumelhart, David E.; McClelland, James L. Parallel Distributed
Processing: Explorations in the Microstructure of Cognition:
Psychological and Biological Models. Cambridge, 
Massachusetts: MIT Press. 1986.
Searle, John. 1980a. Minds, Brains, and Programs. Behavioral and
Brain Sciences 3: 417-424.
JUSSI KARLGREN
dsvbok jussi karlgren.qxp  2006-08-28  00:59  Sida 6


An Algebra for Recommendations: Using Reader Data as a Basis for Measuring Document Proximity.

dsvbok jussi karlgren.qxp 2006-08-28 00:59

Minds, Brains, and Programs.

Minds, Machines and Searle.

Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Psychological and Biological Models.

The Interaction of Discourse Modality and User Expectations in Human-Computer Dialog. Licentiate thesis, department of Computer and Systems Sciences,

From boxes and arrows to conversation and negotiation: or how research should be amusing, awful, and artificial

RISE – Research Institutes of Sweden

Digitala Vetenskapliga Arkivet - Academic Archive On-line

http://soda.swedish-ict.se/3036/1/DSVbok_Jussi_Karlgren_version_3..pdf

From boxes and arrows to conversation and negotiation: or how research should be amusing, awful, and artificial

Abstract

Similar works

Full text

Available Versions

Swedish Institute of Computer Science Publications Database

RISE – Research Institutes of Sweden

Digitala Vetenskapliga Arkivet - Academic Archive On-line