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AIList Digest Volume 1 Issue 062
AIList Digest Sunday, 25 Sep 1983 Volume 1 : Issue 62
Today's Topics:
Language Understanding & Scientific Method,
Conferences - COLING 84
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Date: 19 Sep 83 17:50:32-PDT (Mon)
From: harpo!utah-cs!shebs @ Ucb-Vax
Subject: Re: Natural Language Understanding
Article-I.D.: utah-cs.1914
Lest usenet readers think things had gotten silent all at once, here's
an article by Fernando Pereira that (apparently and inexplicably) was
*not* sent to usenet, and my reply (fortunately, I now have read-only
access to Arpanet, so I was able to find out about this)
_____________________
Date: Wed 31 Aug 83 18:42:08-PDT
From: PEREIRA@SRI-AI.ARPA
Subject: Solutions of the natural language analysis problem
[I will abbreviate the following since it was distributed in V1 #53
on Sep. 1. -- KIL]
Given the downhill trend of some contributions on natural language
analysis in this group, this is my last comment on the topic, and is
essentially an answer to Stan the leprechaun hacker (STLH for short).
[...]
Lack of rigor follows from lack of method. STLH tries to bludgeon us
with "generating *all* the possible meanings" of a sentence. Does he
mean ALL of the INFINITY of meanings a sentence has in general? Even
leaving aside model-theoretic considerations, we are all familiar with
he wanted me to believe P so he said P
he wanted me to believe not P so he said P because he thought
that I would think that he said P just for me to believe P
and not believe it
and so on ...
in spy stories.
[...]
Fernando Pereira
___________________
The level of discussion *has* degenerated somewhat, so let me try to
bring it back up again. I was originally hoping to stimulate some
debate about certain assumptions involved in NLP, but instead I seem
to see a lot of dogma, which is *very* dismaying. Young idealistic me
thought that AI would be the field where the most original thought was
taking place, but instead everyone seems to be divided into warring
factions, each of whom refuses to accept the validity of anybody
else's approach. Hardly seems scientific to me, and certainly other
sciences don't evidence this problem (perhaps there's some fundamental
truth here - that the nature of epistemology and other AI activities
are such that it's very difficult to prevent one's thought from being
trapped into certain patterns - I know I've been caught a couple
times, and it was hard to break out of the habit - more on that later)
As a colleague of mine put it, we seem to be suffering from a
"difference in context". So let me describe the assumptions
underpinning my theory (yes I do have one):
1. Language is a very fuzzy thing. More precisely, the set of sound
strings meaningful to a human is almost (if not exactly) the set of
all possible sound strings. Now, before you flame, consider: Humans
can get at least *some* understanding out of a nonsense sequence,
especially if they have any expectations about what they're hearing
(this has been demonstrated experimentally) although it will likely be
wrong. Also, they can understand mispronounced or misspelled words,
sentences with missing words, sentences with repeated words, sentences
with scrambled word order, sentences with mixed languages (I used to
have fun by speaking English using German syntax, and you can
sometimes see signs using English syntax with "German" words), and so
forth. Language is also used creatively (especially netters!). Words
are continually invented, metaphors are created and mixed in novel
ways. I claim that there is no rule of grammar that cannot be
violated. Note that I have said *nothing* about changes of meaning,
nor have I claimed that one could get much of anything out of a random
sequence of words strung together. I have only claimed that the set
of linguistically valid utterances is actually a large fuzzy set (in
the technical sense of "fuzzy"). If you accept this, the implications
for grammar are far-reaching
- in fact, it may be that classical grammar is a curious but basically
irrelevant description of language (however, I'm not completely
convinced of that).
2. Meaning and interpretation are distinct. Perhaps I should follow
convention and say "s-meaning" and "s-interpretation", to avoid
terminology trouble. I think it's noncontroversial that the "true
meaning" of an utterance can be defined as the totality of response to
that utterance. In that case, s-meaning is the individual-independent
portion of meaning (I know, that's pretty vague. But would saying
that 51% of all humans must agree on a meaning make it any more
precise? Or that there must be a predicate to represent that meaning?
Who decides which predicate is appropriate?). Then s-interpretation
is the component that depends primarily on the individual and his
knowledge, etc.
Let's consider an example - "John kicked the bucket." For most
people, this has two s-meanings - the usual one derived directly from
the words and an idiomatic way of saying "John died". Of course,
someone may not know the idiom, so they can assign only one s-meaning.
But as Mr. Pereira correctly points out, there are an infinitude of
s-interpretations, which will completely vary from individual to
individual. Most can be derived from the s-meaning, for instance the
convoluted inferences about belief and intention that Mr. Pereira
gave. On the other hand, I don't normally make those
s-interpretations, and a "naive" person might *never* do so. Other
parts of the s-interpretation could be (if the second s-meaning above
was intended) that the speaker tends to be rather blunt; certainly a
part of the response to the utterance, but is less clearly part of a
"meaning". Even s- meanings are pretty volatile though - to use
another spy story example, the sentence might actually be a code
phrase with a completely arbitrary meaning!
3. Cognitive science is relevant to NLP. Let me be the first to say
that all of its results are at best suspect. However, the apparent
inclination of many AI people to regard the study of human cognition
as "unscientific" is inexplicable. I won't claim that my program
defines human cognition, since that degree of hubris requires at least
a PhD :-) . But cognitive science does have useful results, like the
aforementioned result about making sense out of nonsense. Also, lot
of common-sense results can be more accurately described by doing
experiments. "Don't think of a zebra for the next ten minutes" - my
informal experimentation indicates that *nobody* is capable - that
seems to say a lot about how humans operate. Perhaps cognitive
science gets a bad review because much of it is Gedanken experiments;
I don't need tests on a thousand subjects to know that most kinds of
ungrammaticality (such as number agreement) are noticeable, but rarely
affect my understanding of a sentence. That's why I say that humans
are experts at their own languages - we all (at least intuitively)
understand the different parts of speech and how sentences are put
together, even though we have difficulty expressing that knowledge
(sounds like the knowledge engineer's problems in dealing with
experts!). BTW, we *have* had a non- expert (a CS undergrad) add
knowledge to our NLP system, and the folks at Berkeley have reported
similar results [Wilensky81].
4. Theories should reflect reality. This is especially important
because the reverse is quite pernicious - one ignores or discounts
information not conforming to one's theories. The equations of motion
are fine for slow-speed behavior, but fail as one approaches c (the
language or the velocity? :-) ). Does this mean that Lorenz
contractions are experimental anomalies? The grammar theory of
language is fine for very restricted subsets of language, but is less
satisfactory for explaining the phenomena mentioned in 1., nor does it
suggest how organisms *learn* language. Mr. Pereira's suggestion that
I do not have any kind of theoretical basis makes me wonder if he
knows what Phrase Analysis *is*, let alone its justification.
Wilensky and Arens of UCB have IJCAI-81 papers (and tech reports) that
justify the method much better than I possibly could. My own
improvement was to make it follow multiple lines of parsing (have to
be contrite on this; I read Winograd's new book recently and what I
have is really a sort of active chart parser; also noticed that he
gives nary a mention to Phrase Analysis, which is inexcusable - that's
the sort of thing I mean by "warring factions").
4a. Reflecting reality means "all of it" or (less preferable) "as
much as possible". Most of the "soft sciences" get their bad
reputation by disregarding this principle, and AI seems to have a
problem with that also. What good is a language theory that cannot
account for language learning, creative use of language, and the
incredible robustness of language understanding? The definition of
language by grammar cannot properly explain these - the first because
of results (again mentioned by Winograd) that children receive almost
no negative examples, and that a grammar cannot be learned from
positive examples alone, the third because the grammar must be
extended and extended until it recognizes all strings as valid. So
perhaps the classical notion of grammar is like classical mechanics -
useful for simple things, but not so good for photon drives or
complete NLP systems. The basic notions in NLP have been thoroughly
investigated;
IT'S TIME TO DEVELOP THEORIES THAT CAN EXPLAIN *ALL* ASPECTS OF
LANGUAGE BEHAVIOR!
5. The existence of "infinite garden-pathing". To steal an example
from [Wilensky80],
John gave Mary a piece of his.........................mind.
Only the last word disambiguates the sentence. So now, what did *you*
fill in, before you read that last word? There's even more
interesting situations. Part of my secret research agenda (don't tell
Boeing!) has been the understanding of jokes, particularly word plays.
Many jokes are multi-sentence versions of garden- pathing, where only
the punch line disambiguates. A surprising number of crummy sitcoms
can get a whole half-hour because an ambiguous sentence is interpreted
differently by two people (a random thought - where *did* this notion
of sentence as fundamental structure come from? Why don't speeches
and discourses have a "grammar" precisely defining *their*
structure?). In general, language is LR(lazy eight).
Miscellaneous comments:
This has gotten pretty long (a lot of accusations to respond to!), so
I'll save the discussion of AI dogma, fads, etc for another article.
When I said that "problems are really concerned with the acquisition
of linguistic knowledge", that was actually an awkward way to say
that, having solved the parsing problem, my research interests
switched to the implementation of full-scale error correction and
language learning (notice that Mr. Pereira did not say "this is
ambiguous - what did you mean?", he just assumed one of the meanings
and went on from there. Typical human language behavior, and
inadequately explained by most existing theories...). In fact, I have
a detailed plan for implementation, but grad school has interrupted
that and it may be a while before it gets done. So far as I can tell,
the implementation of learning will not be unusually difficult. It
will involve inductive learning, manipulation of analogical
representations to acquire meanings ("an mtrans is like a ptrans, but
with abstract objects"....), and other good things. The
nonrestrictive nature of Phrase Analysis seems to be particularly
well-suited to language knowledge acquisition.
Thanks to Winograd (really quite a good book, but biased) I now know
what DCG's are (the paper I referred to before was [Pereira80]). One
of the first paragraphs in that paper was revealing. It said that
language was *defined* by a grammar, then proceeded from there.
(Different assumptions....) Since DCG's were compared only to ATN's,
it was of course easy to show that they were better (almost any
formalism is better than one from ten years before, so that wasn't
quite fair). However, I fail to see any important distinction between
a DCG and a production rule system with backtracking. In that case, a
DCG is really a special case of a Phrase Analysis parser (I did at one
time tinker with the notion of compiling phrase rules into OPS5 rules,
but OPS5 couldn't manage it very well - no capacity for the
parallelism that my parser needed). I am of course interested in
being contradicted on any of this.
Mr. Pereira says he doesn't know what the "Schank camp" is. If that's
so then he's the only one in NLP who doesn't. I have heard some
highly uncomplimentary comments about Schank and his students. But
then that's the price for going against conventional wisdom...
Sorry for the length, but it *was* time for some light rather than
heat! I have refrained from saying much of anything about my theories
of language understanding, but will post details if accusations
warrant :-)
Theoretically yours*,
Stan (the leprechaun hacker) Shebs
utah-cs!shebs
* love those double meanings!
[Pereira80] Pereira, F.C.N., and Warren, D.H.D. "Definite Clause
Grammars for Language Analysis - A Survey of the Formalism and
a Comparison with Augmented Transition Networks", Artificial
Intelligence 13 (1980), pp 231-278.
[Wilensky80] Wilensky, R. and Arens, Y. PHRAN: A Knowledge-based
Approach to Natural Language Analysis (Memorandum No.
UCB/ERL M80/34). University of California, Berkeley, 1980.
[Wilensky81] Wilensky, R. and Morgan, M. One Analyzer for Three
Languages (Memorandum No. UCB/ERL M81/67). University of
California, Berkeley, 1981.
[Winograd83] Winograd, T. Language as a Cognitive Process, vol. 1:
Syntax. Addison-Wesley, 1983.
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Date: Fri 23 Sep 83 14:34:44-CDT
From: Lauri Karttunen <Cgs.Lauri@UTEXAS-20.ARPA>
Subject: COLING 84 -- Call for papers
[Reprinted from the UTexas-20 bboard.]
CALL FOR PAPERS
COLING 84, TENTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL LINGUISTICS
COLING 84 is scheduled for 2-6 July 1984 at Stanford University,
Stanford, California. It will also constitute the 22nd Annual Meeting
of the Association for Computational Linguistics, which will host the
conference.
Papers for the meeting are solicited on linguistically and
computationally significant topics, including but not limited to the
following:
o Machine translation and machine-aided translation.
o Computational applications in syntax, semantics, anaphora, and
discourse.
o Knowledge representation.
o Speech analysis, synthesis, recognition, and understanding.
o Phonological and morpho-syntactic analysis.
o Algorithms.
o Computational models of linguistic theories.
o Parsing and generation.
o Lexicology and lexicography.
Authors wishing to present a paper should submit five copies of a
summary not more than eight double-spaced pages long, by 9 January
1984 to: Prof. Yorick Wilks, Languages and Linguistics, University of
Essex, Colchester, Essex, CO4 3SQ, ENGLAND [phone: 44-(206)862 286;
telex 98440 (UNILIB G)].
It is important that the summary contain sufficient information,
including references to relevant literature, to convey the new ideas
and allow the program committee to determine the scope of the work.
Authors should clearly indicate to what extent the work is complete
and, if relevant, to what extent it has been implemented. A summary
exceeding eight double-spaced pages in length may not receive the
attention it deserves.
Authors will be notified of the acceptance of their papers by 2 April
1984. Full length versions of accepted papers should be sent by 14
May 1984 to Dr. Donald Walker, COLING 84, SRI International, Menlo
Park, California, 94025, USA [phone: 1-(415)859-3071; arpanet:
walker@sri-ai].
Other requests for information should be addressed to Dr. Martin Kay,
Xerox PARC, 3333 Coyote Hill Road, Palo Alto, California 94304, USA
[phone: 1-(415)494-4428; arpanet: kay@parc].
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End of AIList Digest
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