A Word Database for Natural Language Processing 
Brigitte Barnett 
Hubert Lehmann 
Magdalena Zoeppritz 
IBM Scientific Center, Tiergartenstral3e 15, 6900 Heidelberg, Federal Republic of Germany 
Abstract: The paper describes the design of a fair sized lexi- 
cal database that is to be used with a natural language 
based expert system with German as the language of inte- 
raction. Sources for entries and tools for constructing and 
maintaining the database are discussed, as well as the in- 
formation needed in the lexicon for the purposes of syntactic 
and semantic processing. 
1 Introduction 
The intent of this paper is to show some aspects of a com- 
puter dictionary geared towards the natural language com- 
ponent of an expert system. The dictionary is organized as 
a database to integrate tile various aspects of lexicographic 
work and, at the same time, enable fast access from a parser. 
Work on the lexicon was long neglected - both in theoretical 
linguistics and natural language processing projects - so we 
felt that a principled approach was overdue (cf. Sedelow 
(1985) for a survey of related work). In the past two years, 
we concentrated therefore on the formulation of criteria for 
establishing syntactic features which have to be coded in the 
lexicon, and we will report here on some of our findings. 
This will be preceded by a brief overview of the aims of our 
overall project and a short description of the prototype sy- 
stem we are building. We will then describe the design of 
our lexicographic database including the criteria for selecting 
sources of the vocabulary and some of our tools for editing 
and querying. 
The main objectives of the project Linguistics and Logic 
Based Legal Expert System, which is a Joint Research Pro- 
ject between the University of T/ibingen and the IBM 
Scientific Center Heidelberg, are to design and implement a 
natural language based knowledge acquisition and query 
system and to build a legal expert system on its basis. It 
consists of the following components: 
• The dialog component controls the interaction with users 
and contains among other things an explanation compo- 
nent and a component for preparing system output for 
display and for eventually generating natural language 
explanatory texts. 
In a so-called user profile, as much information about a 
user is kept as necessary: to improve answers and ex- 
planations, one must know certain things about the user, 
mainly about her or his knowledge in current sessions. 
For example, one may want to avoid explanations about 
details the user already knows. 
• The deductive component is activated by user queries, by 
input of new knowledge, and by requests of the Natural 
Language Analyzer. 
• The knowledge manager administers the actual know- 
ledge base in the working area as well as its permanent 
version in the database. It is the only component allo- 
wed to update the permanent knowledge base. It loads 
knowledge from the database into storage and requests 
consistency checks for new knowledge. With the excep- 
tion of the lexical database, the Knowledge Manager 
also accesses the database on behalf of other system 
components. 
• The SQL Data System (IBM (1983)) maintains the da- 
tabase which is a repository of facts and rules: 
. linguistic knowledge, e.g. dictionary and grammar 
• common sense knowledge, including a thesaurus 
• legal knowledge (law, rules from commentaries and 
decisions, legal strategies) 
• cases 
• user profiles 
• The natural language analyzer and its dictionary are ex- 
tensions and modifications of the existing User Specialty 
Languages system (USL) developed at the Heidelberg 
Scientific Center (Lehmann, H., N. Ott, M. Zoeppritz 
(1985), M. Zoeppritz (1984)). USL is a natural lan- 
guage front end to SQL/DS (IBM 1983) operational in 
six languages. Within the scope of this project, it will be 
enhanced to suit the requirements of a natural language 
(German) based expert system. This means that it must 
be able to deal with both running texts and queries and 
to translate them into their corresponding logical form. 
The Natural Language Analyzer consists of the follo- 
wing parts: 
- a sentence separator splitting texts into sentences, 
• a pre-parser for dictionary look-up, 
. the parser and the routines for semantic analysis, 
• routines for the generation of the logical form from 
intermediate structures (cf. Guenthner and Leh- 
mann (1984) for a description), 
• routines for semi-automatic generation of thesaurus 
extensions (Wirth, R. (1984)). 
As a specific application, the area of German traffic law was 
chosen for the expert system which shall be used in two 
modes: for consultation by a legal expert and as a tutor for 
law students (cf. Alschwee et al. (1985) for details). 
2 Description of the Dictionary 
Within such an environment, a fairly large-sized and detai- 
led dictionary is needed. Aspects of its design, the structure 
in the database, and the editing and querying facilites will 
be discussed (cf. also Barnett (1985)). The expected size of 
the dictionary within the scope of the project is estimated to 
be some 20,000 entries. Its current size is some 12,000 ent- 
ries. 
435 
2.1 Word Database 
Because we must be able to handle a large number of words 
in this project, we felt that it would be necessary to admini- 
strate them in a more appropriate form than the usual file 
organization and that a relational database would be the 
best tool for dealing with lexical information because of the 
following advantages: 
• excerpting grammatical information according to speci- 
fic features; 
• links to related information not necessarily kept in the 
same table; 
• easier control of updates; 
• many types of integrity checks; 
• automatic backup so that, in case of a systems break- 
down, a consistent status remains available; 
• another great advantage of database technology is con- 
currency capabilities which preventusers working on the 
same table from getting in each other's way.; 
• and, within the realm of this project, the possibility to 
link to the Natural Language Analyzer. 
2.2 Scope 
The scope of the information contained in our dictionary is 
geared towards the processing of natural language by com- 
puter. Lexical information must therefore be more detailed 
and more explicit than in standard dictionaries intended for 
humans. Also, a computer dictionary is of no value unless 
it matches the grammar and the needs of the semantic pro- 
cessing. 
We started with the coding of morphological and syntactic 
information, since we felt to be on rather stable ground 
there. We will report on some of the difficulties we encoun- 
tered - many of them not unknown to theoretical linguistics 
- in the next section. 
Semantic information is coded primarily in the form of 
meaning rules, but we have not included these in our lexical 
database yet, as we are still experimenting with different 
kinds of information and representations before we go to 
large-scale coding. We also hope that, at least to some ex- 
tent, the acquisition of such information can be automated 
(cf. the approach taken by Wirth (1984)). 
2.3 Sources 
For the purpose of our particular application, we need to 
cover the vocabulary occurring in German traffic law. 
However, to meet the goal of general applicability, it is also 
necessary to include the core of the general German voca- 
bulary. We will try therefore to code the relevant legal 
words based on texts from this very domain. In addition - 
and this is the greater problem - we must try to define which 
words pertain to the common vocabulary. 
As a first step, we have compiled a preliminary list of 
the 4000 most frequent German words from an existing 
frequency dictionary (Meier (1967)) and news texts. 
Later, we will make frequency counts of representative 
samples of texts to arrive at a more reliable list of words. 
® IBM Germany has a dictionary of 70,000 entries con- 
taining morphological and hyphenation information. 
436 
• The vocabulary of the application area, i.e. from the 
legal domain, stems from the following sources: 
• A collection of relevant court decisions (from our 
study partner), 
• A number of accident descriptions collected from 
newspapers, 
• A few word lists used for document retrieval from 
both the Legal and Public Relations departments of 
IBM Germany. 
• We plan to investigate to what extent machine-readable 
dictionaries or legal texts can be used for an automatic 
or semi-automatic acquisition of lexical and grammatical 
information and of common-sense knowledge. 
2.4 Layout of the Dictionary Relation 
In our word database, every word constitutes an entry, and 
most columns in the entry contain information concerning a 
particular word. Even though semantic aspects are not 
coded in this particular version, one may regard the codes 
as a representation of a word's morphological and syntactic 
meaning. Some words have more than one entry: to code 
multiple entries becomes necessary when different gramma- 
tical feature sets have to be assigned to one lemma. 
All words are contained in a single table or relation. One 
could also envisage a separate table for every part of speech; 
however, this would be rather inconvenient, as it would be 
impossible to compare grammatical phenomena across dif- 
ferent categories. Also it may be desirable to look at words 
of the same root but belonging to different parts of speech. 
With this necessity in mind, we designed an overall, general 
relation which would contain all words. In order to treat the 
words individually and according to their specific needs, a 
so-called "view" was defined for each part of speech. The 
present structure of the relation is described in Figure 1. 
2.5 Tools and Aids 
To facilitate coding and to ensure its accuracy, we use the 
following tools: 
Editing: A Dictionary Editor (a menu-driven program run- 
ning under ISPF (IBM 1982) interacting with the SQL/DS 
database) was developed to facilitate adding, updating, de- 
leting, and checking of entries at the terminal. 
Under this editor, a specific set of menus and help panels 
was implemented for nouns, verbs, and adjectives. Whereas 
the main menus contain only short hints to the grammatical 
information as a sort of reminder to the lexicographer, help 
menus give more detailed examples for the individual codes. 
Subpanels, as extensions to the main panel for input, and 
error messages also assist the lexicographer. Codes arc ve- 
rified by the Dictionary Editor to keep down the error rate. 
Queries, Reports, and Files: Independently of the Dictionary 
Editor, the lexicographers work with the standard database 
interfaces ISQL (IBM 1983) and QMF (IBM 1983, 1984) 
to query, extract, recover, and to view the contents of the 
word database. QMF is used to select information and to 
format, display, and write reports. - The style of data dis- 
play is easy to understand, so that persons who are not 
experts in data processing but are competent linguists can 
examine and alter the linguistic description according to 
General Description Field in Database Noun Verb Adjective 
Lermna (may not be empty) 
Current number of entry 
Part of Speech 
MORPIIOLOGY: 
Morphological codes 
Morphological codes 
SYNTAX: 
Possible complement(s) 
Prepositions governed: alternating or cumulative 
Up to 4 adverbiMs 
Complement Clauses introduced by "dab ~ 
Complement Clauses: hKinitive introduced by "zu" 
Logical Subject of infinitive clause 
Complement Clauses introduced by "ob" 
Different for each part of speech: 
Different for each part of speech: 
Different for each part of speech: 
Different for each part of speech: 
Obligatory Coml}lcments 
Subject Area 
Source: Userid of who coded; origin of entry 
Frequency Count 
Source of Frequency Cmmt 
Level of Style 
Stem for 1st Entry: 
Secondary Stems: 
11yphenation 
Date of last Update 
5 additional, not yet used fields 
WORT Infinitive Nominative 
CONT # # 
CAT N VERB 
MORPII1 Declension code Sets of suffixes 
MORPtI2 Alternative declension Syntax dud scope of 
stems 
SYNTA 1 .. Valency 
SYNTA2-5 Preposition Preposition 
SYNTB 1-4 place/temporal/modal place/temporal/modal 
SYNTB5 case/preposition case/preposition 
SYNTB6 case/preposition case/preposition 
SYNTB7 case/preposition case/preposition 
SYNTB8 case/preposition case/preposition 
SYNTB9 Gender Separable Prefix 
SYNTB 10 Alternate Gender Reilexivity 
SYNTC1 .. Inlpersomd Subject only 
SYNTC2 Usage of Participle 
SYNTC3 .o x 
SIJItJECT x x 
SOURCE x x 
FREQ x x 
FREQS x x 
STYLE x x 
XREF Nominative Singular lmqnitive without ~-(e)n", 
without separat}lc prefix 
XREF (cont'd) Plural Furm lml}erfect, Past Participle 
'I'RI{NN x x 
I)ATUM x x 
141,F2,F3,F4,1;5 .... 
Positive # 
ADJ 
Scope of stem form 
Declension class 
Valency 
Preposition 
place/temporal/modal 
case/preposition 
case/preposition 
case/preposition 
case/preposition 
not used attributively 
not used adverbially 
not used predicatively 
x 
x 
x 
x 
x 
x 
Positive 
(kadation 
x 
x 
Fig.i: Structure of the Word Relation 
their particular needs. 
It is part of the work of a lexicographer to account for all 
grammatical constructions within which a word may ap- 
pear. To achieve this, the lexicographers consult standard 
dictionaries such as Duden (1976-1981) and Brockhaus 
Wahrig (1980.. 1984), but most importantly, they consult the 
texts mentioned above in the form of a concordance which 
we generate dynamically. 
3 Syntactic Information 
The restrictions on co-occurrence with other words (or 
phrases) is what we consider to be syntactic information. 
Here we include information on government (or valency) 
and on adverbials (or attributes) which serve to subclassify 
the various parts of speech. Our work is based on the work 
of Fillmore (1968), Gross (1984), Heidolph et al. (1980), 
Steini~ (1969), Bierwisch (1963), Helbig and Schenkel 
(1975), Sommerfeldt and Schreiber (1977, 1980), and on 
Zoeppritz (1984). 
For the practical work on a dictionary, it is of utmost im- 
portance to make fully explicit the criteria for the different 
classifications used. Such criteria are notoriously difficult 
to extract from theoretical as well as practice..oriented 
works. 
3,1 Government 
German verbs can be classified according to the objects they 
govern (accusative (A), dative (D), genitive (G), prepositio- 
nal object (P), predicate noun or adjective (N)). We decided 
to include also the subject (N) among the complements go.. 
verned by the verb. A similar classification can be carried 
out for adjectives (they govern cases as well as prepositions) 
and for nouns (which govern prepositional attributes; geni- 
tive attributes are not coded but admitted for every noun). 
While some of the complements may be missing in a sen- 
tence, others must be regarded as obligatory, and this must 
be coded in the dictionary as well. So we code two features 
indicating the maximum and the minimum of complements 
of a given word. This is illustrated by the adjective iiberlegen 
(superior) which gets the maximal code DP and the minimal 
code ZZ (nil): 
(D) (P) 
Paul ist uns im Weitsprung fiberlegen. 
(Paul is superior to us in the long jump.) 
Paul ist im Weitsprung/iberlegen. 
Paul ist uns fiberlegen. 
Paul ist fiberlegen. 
There are a number of problems in determining what com- 
plements can be governed by a given verb. We will discuss 
here the problcm with datives, and in the section on adver- 
bials we will discuss the problem of how to distinguish bet- 
ween adverbials and prepositional objects. 
437 
It was noted by case grammarians (but also to some extent 
in traditional grammar) that datives perform different func- 
tions ("semantic roles"), and that only some of them should 
be regarded as subeategorizing the class of verbs; the others 
are sometimes called "free" datives which are exemplified by 
ethical: Wirf mir die Vase nicht weg. 
(Be sure not to throw the vase away) 
possession: Paul brach mir den Arm. 
(Paul broke my arm) 
benefactive: Paul fibersetzte mir den Brief 
(Paul translated the letter for me) 
responsibility: Die Vase ist mir zerbrochen. 
(The vase broke during the time I had it) 
(cf. also Heidolph et al. (1980) for a discussion and Wegener 
(1985)). 
Free datives are never obligatory, but all other criteria so far 
are only semantically motivated and are - particularly in the 
case of the benefactive - not very well defined. But still, free 
datives can be taken into account by the grammar and can 
thus be attached to any suitable verb. 
3.2 Complement Clauses 
In accordance with Bierwisch (1963) and Heidolph et al. 
(1980), we consider complement clauses as filling the posi- 
tions of nominal complements. The complement clauses we 
consider are daft (that) clauses, ob (whether) clauses, and 
infinitive clauses (pure infinitives and infinitive clauses in- 
troduced by zu (to)). Bierwisch was first in subcategorizing 
German verbs according to the implied subjects of the infi- 
nitive clauses they govern. Consider the following examples 
in English 
John permitted Paul to leave 
John persuaded Paul to leave 
This is problematic, however, with some verbs in German 
when the infinitive clause contains modal verbs. Consider 
Er flehte sie an zu gehen 
(he begged her to leave) 
with the implied subject sie, and 
Er flehte sie an, gehen zu dfirfen 
(He begged her to be permitted to leave) 
with the implied subject er. This shift of implied subject 
must be coded in the lexicon (for verbs like diirfen), so that 
the code can be used by the syntax rules for complex verbs. 
A second problem concerns cases where an implied subject 
cannot be found in the matrix clause as in 
Paul ordnete an, den Saal zu rfiumen 
(Paul ordered the room to be cleared) 
Es ist verboten, den Rasen zu betreten 
(it is forbidden to walk on the lawn) 
There are two different phenomena involved: The dative 
governed by verbieten, would be the implied subject, but 
438 
happens to be omitted, whereas anordnen does not govern a 
candidate for implied subject. If there is no suitable candi- 
date in the context, we get a generic interpretation, and we 
code our complement features accordingly. 
3.3 Adverbials 
Our approach to adverbials is closely related to the one 
taken by Steinitz (1969) and Heidolph et al. (1980), which - 
to us - seems far better motivated than e.g. the classification 
in Brockhaus Wahrig (1980-1984). Certain types of adver- 
bials we consider to be governed by certain verbs, nouns and 
adjectives, and these are hence used for subcategorization. 
They include adverbials of 
place: Paul wohnt in Heidelberg 
(Paul lives in Heidelberg) 
direction: Paul geht nach Heidelberg 
(Paul goes to Heidelberg) 
modality: Paul benimmt sich schlecht 
(Paul behaves badly) 
measure: der Vortrag dauert eine Stunde 
(the lecture lasts one hour) 
It has been our tendency to code adverbials only when they 
are obligatory, but this certainly does not cover all the in- 
formation necessary. 
A further problem concerns the decision between adverbial 
and prepositional object. Criteria for this distinction have 
been described by Steinitz (1969) and Heidolph (1980): they 
mainly involve observations on the role prepositions play - 
their variability and whether they have retained their mea- 
ning. Consider 
Paul stood on the table 
Paul insisted on the table 
In the first case, we could have near, under, by, etc. instead 
of on whereas in the second case, we do not have a choice. 
3.4 Coding Example 
The following example shows the test questions and corre- 
sponding coding decisions for verbs and adjectives. The 
sample form is iiberlegen, that appears as verb with sepa- 
rable prefix in the meaning 'to cover', as verb with insepa- 
rable prefix in the meaning 'to reflect', and as an adjective 
meaning 'superior'. 
Tests for iiberlegen 'to cover': 
Prefix: separable or not? 
legt fiber -- hat fibergelegt -- fiberzulegen 
Full government: 
Paul legt Maria eine Jacke fiber 
Dative can be left out: 
Paul will eine Jacke fiberlegen 
Accusative cannot be left out: 
*Paul legt der Maria fiber *Paul legt fiber 
Coding for ftberlegen 'to cover' 
Stem: leg 
Prefix i~ber 
Word class: VERB 
Government: nominative dative accusative 
Obligatory: nominative accusative 
Testing for iiberlegen 'to reflect': 
Prefix: separable or not? 
er fiberlegt -- hal: fiberlegt -- zu fiberlegen 
Full government: 
Paul fiberlegt sich einc Frage 
Dative can be left out: 
Paul fiberlegt eine Frage 
Accusative can be left out as well: 
Paul fiberlegt 
Does the dative have to be reflexive? 
*Paul fiberlegt uns eine Frage 
The accusative can be replaced by zu-infinitive, dad and 
ob-clauses: 
Paul fiberlegt (sich), Maria zu besuchen 
Paul fiberlegt (sich), dab er Maria besuchen will 
Paul iiberlegt (sich), ob er Maria besuchen will 
The implied subject of infinitive clauses is the main clause 
subject: 
Paul fiberlegt sich, Maria zu besuchen 
Paul besucht Maria 
Coding for iitberlegen 'to :reflect': 
Word class: VERB 
Stem: iiberleg 
Government: nominative dative accusative 
Clauses: infinitive ,as accusative 
implied subject is nominative 
dad-clause as accusative 
ob-clause as accusative 
Reflexive: dative 
Obligatory: nominative accusative 
Testing for iiberlegen 'superior': 
Full government: 
Paul ist Maria irn Weitsprung fiberlegen 
der uns allen im Weitsprung fiberlegene Paul 
Prepositional can be left out: 
Paul ist Maria/iberlegen 
der uns allen fiberlegene Paul 
Dative can be omitted as well: 
Paul ist fiberlcgen °- der fiberlegene Paul 
Zu-infinitives (marginally) and dad-clauses are possible in 
lieu of the prepositional. Clauses inuoduced by ob are not 
allowed, not even with negation. 
Paul ist Maria darin fiberlegen, 
dab er welter springen kann 
*Paul ist Maria (nicht) darin fiberlegen, 
ob er weiter springen kann 
Diese Schrift ist anderen darin /iberlegen, 
leichter lesbar zu sein 
The subject of infinitive clauses is the head of the adjective: 
der darin, springen zu kSnnen,/iberlegene Paul 
Paul ist fiberlegen 
Die Schriff ist der anderen darin /iberlegen, 
besser lesbar zu scin 
Die Schrift ist t)esser lesbar 
The preposition may not be omitted: 
*Paul ist Maria/iberlegen, 
welter springen zu k6nnen 
The adjective can be used in predicative and attributive 
position: 
Paul ist fiberlegen .-- der fiberlegcne Paul 
Coding for i~berlegen 'superior'. 
Word class: ADJECTIVE 
Stem: iiberlegen 
Government: dative prepositional 
Prepositions: in bei 
Clauses: infinitive as prepositional 
daft-clause as prepositional 
Restrictions: no restrictions to predicative or attributive 
use 
4 Semantic D~ovmation 
When dealing with semantic information wc should distin- 
guish between the information needed for obtaining a (if' 
possible) disambiguated logical form and the information 
needed to draw inferenccs from this logical form, even 
though - at least in part o this information may be idcntical. 
Among the former we include a concept lattice (or 
hierarchy) and selection restrictions which both are special 
cases of the meaning rules wc use to represent common sense 
and domain knowledge. (For a dctailcd discussion of our 
approach to knowledge reprcscntation, el. Guenthncr / 
Lehmann / Sch6nfeld, 1986). All of this information we 
encode as Prolog tcrms, and we also store these in SQL/DS, 
but separate from the word relation described above. 
We give an example here of thc conccpt hierarchy currcntly 
used in our Natural Language Analyzcr. ("bt" stands for 
broader term, the period in the third rule indieatcs a com- 
pound tcrm): 
bt( angek lagt, mens oh). 
bt(fahrz:mg, fortbewegungsmJttel). 
bt( fortbewegungsmittel,hergestelit, objekt). 
439 
This hierarchy is used in conjunction with the selection re- 
strictions listed below to disambiguate sentences, to recover 
ellipses, and to resolve anaphoric references. (The format 
of the selection restrictions is: Lemma, reading, state vs. 
event, list of restrictions for the respective complements, 
including an indication whether the verb is distributive (dist) 
or collective on a given complement): 
verb(abbremsen,l,event, 
nom(dist,fortbewegungsmittel).nil). 
verb(abbremsen,2,event, 
nom(dist,mensch). 
acc(dist,fortbewegungsmittel).nil). 
Wirth (1984) has described a procedure to extend a concept 
hierarchy and selection restrictions from text on the basis of 
given sentences. In his procedure, human intervention is still 
required, and it seems doubtful at this point whether a fully 
automatic procedure is feasible. Further, one observes a 
certain discrepancy between linguistic usage and logical 
behavior of certain words. We are investigating ways to 
overcome these problems, but a discussion of them has to 
be left to forthcoming publications. 
5 Conclusions 
We have described the design of a lexical database to be 
used with a natural language based expert system, discussed 
a number of problems we encountered when coding syntac- 
tic information for words, and also mentioned where add- 
tional work needs to be done in order to achieve a compre- 
hensive dictionary for language processing. 
By November 1985, we coded morphological and syntactic 
information for some 5,500 nouns, approximately 3,000 
verbs, and 3,500 adjectives. Our next steps are to fully in- 
tegrate the lexical database with the rest of our prototype to 
improve our concordance programs, and to continue the 
development of criteria for syntactic subcategorization. 
Acknowledgement 
We want to thank B. Endres, A Franzke, S. Goeser, D. 
Jacob, E. Latniak, W. Pietschkc, S. Ritzenfeld, G. Schfilzke, 
A. Storrer, and especially K. Horlfinder for the work they 
did in developing the Dictionary Editor, coding dictionary 
entries, and for their help and suggestions in formulating 
criteria for coding. Without them, this paper would not have 
been possible. 
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