NORKOMPLEKS. Some Linguistic Specifications and Applications

  1. 1. Torbjørn Nordgård

    Department of Linguistics - University of Trondheim

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1. Introduction
NORKOMPLEKS is a national lexicon project
which aims at providing a computational dictionary to be used in computational linguistics applications in Norway (NORKOMPLEKS is an acronym for NORsk KOMPutasjonelt LEKSikon, a
computational lexicon for Norwegian). The project is mainly funded by the Norwegian Research
Council, but there is also a substantive amount of
funding provided by Telenor. The ancestor of
NORKOMPLEKS is NORLEX, and this presentation will describe the results of NORLEX which
are to be preserved or pursued in NORKOMPLEKS.
The parts of NORKOMPLEKS to be discussed
here are written and implemented in Quintus Prolog, but in the descriptions below the information
is given in a more readable form.
2. Linguistic Information in
NORKOMPLEKS exists in a preliminary version
where all Norwegian verbs in the Bokmål standard
are described in some detail. We will concentrate
on the morphological encoding system and the
syntactic distinctions that are made in the system.
2.1 The Morphological Encoding System
Both written standards of Norwegian (i.e. Bokmål
and Nynorsk) are special compared to most other
written standards since they allow many inflection
patterns for one and the same lexeme. Consider an
Lexeme: suge (English translation: to
Inflections: imp:"sug"
inf: "suge"
p-part: "suga"
p-part: "sugd"
p-part: "suget"
pr-part: "sugende"
pres: "suger"
pret: "saug"
pret: "suga"
pret: "sugde"
pret: "suget"
Obviously, a computational dictionary for Norwegian must be able to cover all forms which are
allowed, i.e. the words shown in example (1). This
information can be put into the dictionary in two
ways: One rather naive alternative is a full listing
of all the possible forms attached to each lexeme
in the dictionary. The better solution would be to
describe the inflections in (1) as a set of inflectional patterns. The patterns are as follows:
(2) suge (infinitive), sug (imperative), suger (present), suget
(past), suget (past participle), sugende (present participle)
(3) suge, sug, suger, sugde, sugd, sugende
(4) suge, sug, suger, saug, sugd, sugende
(5) suge, sug, suger, suga, suga, sugende
In NORKOMPLEKS, each of these patterns has a
name. The verb “suge” is described as in (6):
Word: suge
Category: Verb
Inflection Codes: v1,v8,v121,v11
Syntactic Properties: Transitive, Intransitive,
The Particle "inn"+ NP
In a later section we will consider the syntactic
properties in more detail. The implemented Prolog-description of this verb is given in (7):
Multiple inflection patterns associated with one
particular form is also found in other languages.
The distinction between “British” and “American” English is an example. However, the variation in declensions found in Norwegian is unique
not only because there are so many possibilities,
as witnessed by the example above, but also because there is no legally established norm as to
which combinations of codes are preferred. Quite
the opposite; – the majority in “Norsk Språkråd”
(Board for the Norwegian Language) will not try
to establish a norm which could possibly be interpreted as prescriptive “advice” or demand. This
does not mean that such norms are illegal.
Newspapers or publishers can freely adhere to
their own standards, but the authorities will not try
to enforce any such norm. It appears, however, to
be a fact that a couple of “styles” or systematic
connections between inflectional patterns exist.
As an example, the code in (2) co-occurs with the
pattern in (8), but not with that in (9) (the verb
“stryke” has English translations like “stroke”,
“delete”, “brush”, “iron”):
(8) stryke stryker strøk strøket strykende
(9) stryke stryker strauk strøket strykende
The situation is the same for inflectional patterns
for nouns. In a “moderate” or “conservative” and
“Danish-like” style the masculine form “solen”
(“the sun”) is used together with patterns (2) and
(8), whereas the more “radical” form “sola” is
compatible with (9) and (4). (5) is also a “radical”
form, and (3) is somewhat intermediate. Clear-cut
distinctions are hard to make, however, and corpus
studies should be used as the empirical basis for
describing these patterns (cf. the remarks below).
Such facts are familiar to most Norwegians, but
they are rarely made explicit so that stylistic consistence can be formalized. A lexicon like NORKOMPLEKS will make it easier to establish
norms by inspecting the sets of co-occuring pairs
or triples of declination codes in the lexicon. When
such pairs or triples are found, they can either be
“weighted” or inspected manually, or they can be
validated on the basis of tagged corpora. But since
the amount of tagged corpora of Norwegian is
limited, manual “prescription” is the only option.
When tagged corpora are available various norms
can be deduced on the basis of selected text material. An algorithm based on the following general
principles will be implemented and tested, and
some initial results will be presented in the talk
(the tests will be applied to a few of the available
tagged texts):
i. Compute the set of code pairs, triples and
quadruples (quintuples have not been identified yet)
ii. For each tagged word in a corpus, decide
whether the stem (dictionary entry) contains a
code in the set of code pairs, triples or quadruples.
iii. If so, count the code(s) which is (are) compatible with the surface form.
The code in a pair, triple or quadruple which is
observed most frequently will be selected, thereby
formally characterizing the morphological stylistic flavour of the corpus. Such results are interesting when it comes to the development of spelling correction systems for a “liberal” language
like Norwegian because the spelling corrector will
be able to detect inconsistent forms that do not
conform to some predefined norm (i.e. a user
defined norm or a norm defined by some organization).
2.3 The Syntactic Encoding System
In automatic sentence processing the amount of
structural ambiguity during analysis is a wellknown and pervasive problem (cf. for instance
Hirst 1986), especially when atomic symbols in
grammars are replaced by more structured and
informative entities, i.e. feature structures (see e.g.
Barton There are various techniques which
can be used in order to reduce these problems, for
instance tabular parsing (Early (1970), Kaplan
(1973), Wiren (1992)), combinations of data-driven and hypothesis driven algorithms, lookahead
buffers (Marcus 1980, Nordgård 1993,1995)), and
so on. But it appears to be the case that the amount
of ambiguity can most successfully be reduced
when lexical items are equipped with detailed
information about the local contexts in which they
can appear (here we put aside problems related to
displaced constituents and empty categories). In
this connection another aspect of NORKOMPLEKS is interesting because this lexicon contains quite a lot of syntactic information associated
with each verb. An example was given above, cf.
(7). The classifications used in NORLEX (and
NORKOMPLEKS) are given in (10):
trans Transitive verb
intrans Intransitive verb
trans1 Special intransitive verb
intrans1 Ergative verb
intrans2 Verb with no thematic roles
seg Reflexive verb
pp[(på)] PP-complement; head = på
seg,np Reflexive verb + np
seg,pp[(med)] Reflexive verb + pp
np,part([bort,vekk]) Particle + np
s([at]) Sentential Complement
s([å]) Infinitival Complement
np,s([å]) np + Infinitival Complement
np,s([at]) np + Sentential Complement
vp VP-komplement
part[over] Particle
part([mot]),s([å]) Particle + Infinitival Complement
np,part([mot]),s([å]) : np +
Infinitival + inf.kompl
Observe that the classifications are surface oriented. Their interpretation in some syntactic or semantic theory is a different issue.
A truly novel contribution of the lexicon is the
classification of matrix verbs, i.e. verbs which take
sentential complements, infinitivals included.
This type of information is absent in most electronic dictionaries, but it is very important in natural
language processing because it can be used to
restrict the number of hypothesized embedded
sentences in standard data-driven parsers. Incorrect hypotheses about embedded sentences are
expensive because the search space can be dramatically broadened, depending on the exact formulation of the rules, of course.
Since verbs have a fairly rich information about
their syntactic properties this lexicon will make it
easier to assign the correct syntactic properties to
verbs in running text. But on the other hand, some
of the descriptions are locally ambiguous, as for
the verb “advare” (“warn”). This verb has the
following syntactic properties:
trans It can be used as an ordinary transitive verb
np,pp(mot) It can take a nominal object and the particle
np,part(mot),s(å) It can take a nominal object, the particle
"mot" and an infinitival clause
pp(mot) It can take a prepositional object, headed by
part(mot),s(å) It can take the particle "mot" and an
infinitival clause
If a rule-based tagger is expected to choose among
these descriptions it will have to inspect the string
to the right in order to decide which syntactic
version is the correct one. If only one lookahead
buffer cell is provided, the tagger will have serious
problems if the verb is followed by a noun because
all the first three possibilities would be applicable
in such a case. A tagger with some general lookahead buffer could be used to handle such cases.
Constraint-based tagging systems appear to be of
particular interest in this respect because they have
flexible means of inspecting the input string backwards and forwards, cf. Voutilainen
It should be noted that the descriptions in NORKOMPLEKS will be modified in the next couple
of years because there is a need to include more
syntactically and semantically relevant information, e.g. in the description of control verbs. The
statement “np,s([å])” does not say anything about
whether we are dealing with subject or object
control. Neither is information about thematic roles included.
3. Concluding Remarks
Two important aspects of NORKOMPLEKS have
been discussed: The morphological encoding system and the syntactic information associated with
each verb. This lexicon is the first large-scale
machine-readable dictionary for Norwegian. It has
information that goes beyond “standard” information in electronic dictionaries, and it has a morphological coding system which makes it suitable for
use in a variaty of language environments, including stylistically marked or specially defined contexts where spelling norms are controversial.
Barton, Berwick and Ristad (1987) Computational Complexity and Natural Language. MIT
Press, Cambridge, Mass.
Earley, Jay (1970): An Efficient Context-Free Parsing Algorithm. Communications of the ACM,
14, 453-460.
Hirst, Græme (1986). Semantic Interpretation and
the Resolution of Ambiguity. Cambridge University Press, Cambridge.
Kaplan, Ronald (1973): A General Syntactic Processor. In Natural Language Processing (Randall Rustin, ed.). Algorithmics Press, New
Marcus, Mitchell (1980): A Theory of Syntactic
Recognition for Natural Language. MIT Press,
Cambridge, Mass.
Nordgård, Torbjørn (1993): A GB-Related Parser
for Norwegian. Peter Lang, Berne.
Nordgård, Torbjørn (1995): E-Parser: An Implementation of a Deterministic GB-Related Parsing System. In Computers and the Humanities
Vuotilainen, Heikkilä and Anttila (1992): Constraint Grammar of English. University of Helsinki, Dept. of General Linguistics.
Wiren, Mats (1992): Studies in Incremental Natural-Language Analysis. Doctoral Dissertation,
University of Linköping

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Conference Info

In review


Hosted at University of Bergen

Bergen, Norway

June 25, 1996 - June 29, 1996

147 works by 190 authors indexed

Scott Weingart has print abstract book that needs to be scanned; certain abstracts also available on dh-abstracts github page. (

Conference website:

Series: ACH/ICCH (16), ALLC/EADH (23), ACH/ALLC (8)

Organizers: ACH, ALLC