



This paper presents an integrated tool for German morphology and statistical part-of-speech tagging which aims at making some well established methods widely available. The software is very user friendly, runs on any PC and can be downloaded as a complete package (including lexicon and documentation) from the World Wide Web. Compared with the performance of other tagging systems the tagger produces similar results.

Es wird ein integriertes Programmpaket vorgestellt, das ein Morphologie- und ein Taggingmodul f<span>ü</span>r das Deutsche enth<span>ält</span>. Die frei verf<span>üg</span>bare Software zeichnet sich insbesondere durch hohe Benutzerfreundlichkeit aus und kann <span>ü</span>ber das World Wide Web bezogen werden. Die Qualit<span>ä</span>t der erzielten Ergebnisse entspricht dem derzeitigen Stand der Forschung.

Introduction
============

Morphology systems, lemmatisers and part-of-speech taggers are some of the basic tools in natural language processing. There are numerous applications, including syntax parsing, machine translation, automatic indexing and semantic clustering of words. Unfortunately, for languages other than English, such tools are rarely available, and different research groups are often forced to redevelop them over and over again. Considering German, quite a few morphology systems (Hausser 1996) and taggers (see table \[Tagger\]) have been developed, which are described in Wothke et al. (1993) (IBM Heidelberg), Steiner (1995) (University of M<span>ü</span>nster), Feldweg (1995) (University of T<span>ü</span>bingen), Schmid (1995) (University of Stuttgart), Armstrong et al. (1995) (ISSCO Geneva), and Lezius (1995) (University of Paderborn). However, in most cases, the tagger is isolated from the morphology system. It relies on a lexicon of full forms which, of course, may be generated by a morphological tool. Unfortunately, most German lexicons are not available due to copyright restrictions and - as far as we know - none of them is public-domain. Therefore we have decided to make our system Morphy publicly available. It combines a morphological and tagging module in a single package and can be downloaded from the World Wide Web.[^2]

<span>|l||c|c|c|c|c|c|</span> & IBM & Univer- & Univer- & Univer- & & Univer-\
& Heidel- & sity of & sity of & sity of & ISSCO & sity of\
& berg & M<span>ü</span>nster & T<span>ü</span>bingen & Stuttgart & Geneva & Paderborn\

learning & super- & super- & unsuper- & unsuper- & unsuper- & super-\
method & vised & vised & vised & vised & vised & vised\

context & bi- & tri- & bi- & bi- & bi- & bi- & tri-\
method & grams & grams & grams & grams & grams & grams\

training & 20.000 & 200.000 & 200.000 & 20.000 & 20.000 & 20.000\
corpus & words & words & words & words & words & words\

test & 10.000 & 30.000 & 20.000 & 5.000 & 1.850 & 5.000\
corpus & words & words & words & words & words & words\

size of & 534.514 & 850.000 & 500.000 & 350.000 & 30.000 & 100.000\
lexicon & words & words & words & words & words & words\

tag sets & 689 & 143 & - & - & - & 456\
large / small & 33 & 54 & 42 & 50 & 56 & 51\

accuracy & 77.7% & 81.5% & - & - & - & 84.7%\
large / small & 93.4% & 92.8% & 96.7% & 97.0% & 96.5% & 95.9%\

Since it has been created not only for linguists, but also for second language learners, it has been designed for standard PC-platforms and great effort has been put in making it as easy to use as possible.

The morphology module of Morphy
===============================

The morphology system is based on the Duden grammar (Drosdowski 1984). It consists of three parts: <span>*Analysis*</span>, <span>*generation*</span> and <span>*lexical system*</span>.

The lexical system is more sophisticated than other systems in order to allow a user-friendly extension of the lexicon. When entering a new word, the user is asked the minimal number of questions which are necessary to infer the new word's grammatical features and which any native speaker should be able to answer. In most cases only the root of the word has to be typed in, questions are answered by pressing the number of the correct alternative (see figure \[Lex\] for the dialogue when entering the verb <span>*telefonieren*</span>). Currently, the lexicon comprises 21.500 words (about 100.000 word forms) and is extended continuously.

Starting from the root of a word and the inflexion type as stored in the lexicon, the generation system produces all inflected forms which are shown on the screen. Among other morphological features it considers vowel mutation, shifts between <span>*<span>ß</span>*</span> and <span>*ss*</span> as well as pre- and infixation of markers for participles.

The analysis system for each word form determines its root and its part of speech, and, if appropiate, its gender, case, number, tense and comparative degree. It also segments compound nouns using a longest-matching rule which works from right to left. Since the system treats each word form separately, ambiguities can not be resolved. For ambiguous word forms any possible lemma and morphological description is given (for some examples see table \[analysis\]). If a word form can not be recognised, its part of speech is predicted by an algorithm which makes use of statistical data on German suffix frequencies.

<span>|l|</span>\

1\. Geben Sie den Stamm ein: <span>**telefonieren**</span>\

2\. Wird das Verb schwach konjugiert?\

: Ja\

2: Nein\

3\. Wie lautet die 2. Person Singular Pr<span>ä</span>sens?

\

: du telefonierst\

2: du telefonierest\

3: du telefoniert\

4\. Wie lautet das Partizip des Verbs?\

: telefoniert\

2: getelefoniert\

Verb klassifiziert!\
\

Morphy's lookup-mechanism when analyzing texts is not based on a lexicon of full forms. Instead, there is only a lexicon of roots together with their inflexion types. When analyzing a word form, Morphy cuts off all possible suffixes, builds the possible roots, looks up these roots in the lexicon, and for each root generates all possible inflected forms. Only those roots which lead to inflected forms identical to the original word form will be selected (for details see Lezius 1994).

Naturally, this procedure is much slower than the simple lookup-mechanism in a full form lexicon.[^3] Nevertheless, there are advantages: First, the lexicon can be kept small,[^4] which is an important consideration for a PC-based system intended to be widely distributed. Secondly, the processing of German compound nouns fits in this concept.

The performance of the morphology system has been tested at the Morpholympics conference 1994 in Erlangen (see Hausser (1996), pp. 13-14, and Lezius (1996)) with a specially designed test corpus which had been unknown to the participants. This corpus comprised about 7.800 word forms and consisted of different text types (two political speeches, a fragment of the Limas-corpus and a list of special word forms). Morphy recognised 89.2%, 95.9%, 86.9% and 75.8% of the word forms, respectively.

<span>|l|l|l|</span>

word form & morphological description & root\

Fl<span>ü</span>ssen & Substantiv Dativ Plural maskulinum & Flu<span>ß</span>\

Bauern- & Substantiv Dativ Plural neutrum & Bauer / Haus\
h<span>ä</span>usern & &\

Schiffahrts- & Substantiv Genitiv Singular maskulinum & Schiff / Fahrt /\
hafenmeisters & & Hafen / Meister\

K<span>ü</span>sse & Substantiv Nominativ Plural maskulinum & Ku<span>ß</span>\
& Substantiv Genitiv Plural maskulinum & Ku<span>ß</span>\
& Substantiv Akkusativ Plural maskulinum & Ku<span>ß</span>\
& Verb 1. Person Singular Pr<span>ä</span>sens & k<span>ü</span>ssen\
& Verb 1. Person Singular Konjunktiv 1 & k<span>ü</span>ssen\
& Verb 3. Person Singular Konjunktiv 1 & k<span>ü</span>ssen\
& Verb Imperativ Singular & k<span>ü</span>ssen\
einnahm & Verb 1. Person Singular Pr<span>ä</span>teritum & (ein)nehmen\
& Verb 3. Person Singular Pr<span>ä</span>teritum & (ein)nehmen\
verspieltest & Verb 2. Person Singular Pr<span>ä</span>teritum & ver-spielen\
& Verb 2. Person Singular Konjunktiv 2 & ver-spielen\
verspieltes & Adjektiv Nominativ Singular neutrum & verspielt (ver-spielen)\
& Adjektiv Akkusativ Singular neutrum & verspielt (ver-spielen)\
edlem & Adjektiv Dativ Singular neutrum & edel\
& Adjektiv Dativ Singular maskulinum & edel\

The tagging module of Morphy
============================

Since morphological analysis operates on isolated word forms, ambiguities are not resolved. The task of the tagger is to resolve these ambiguities by taking into account contextual information. When designing a tagger, a number of decisions have to be made:

-   Selection of a tag set.

-   Selection of a tagging algorithm.

-   Selection of a training and test corpus.

Tag Set
-------

Like the morphology system, the tagger is based on the classification of the parts of speech from the Duden grammar. Supplementary additions have been taken from the system of Bergenholtz and Schaeder (1977). The so-formed tag set includes grammatical features as gender, case and number. This results in a very complex system, comprising about 1000 different tags (see Lezius 1995). Since only 456 tags were actually used in the training corpus, the tag set was reduced to half. However, most German word forms are highly ambiguous in this system (about 5 tags per word form on average).

Although the amount of information gained by this system is very high, previous tagging algorithms with such large tag sets led to poor results in the past (see Wothke et al. 1993; Steiner 1995). This is because different grammatical features often have the same surface realization (e.g. nominative noun and accusative noun are difficult to distinguish by the tagger). By grouping together parts of speech with different grammatical features this kind of error can be significantly reduced. This is what current small tag sets implicitly do. However, one has to keep in mind that the gain of information provided by the tagger is also reduced with a smaller tag set.

Since some applications do not require detailed distinctions, we also constructed a small tag set comprising 51 tags as shown in table \[tagset\]. Both tag sets are constructed in such a way that the large tag set can be directly mapped onto the small tag set.

<span>||l|l|l||</span>

tag name & explanation of the tag & example\

SUB & Substantiv & *(der) Mann\
EIG & Eigenname & *Egon, (Herr) Hansen\
**

VER & finite Verbform & *spielst, l<span>ä</span>uft\
VER INF & Infinitiv & *spielen, laufen\
VER PA2 & Partizip Perfekt & *gespielt, gelaufen\
VER EIZ & erweiterter Infinitiv mit *zu & *abzuspielen\
VER IMP & Imperativ & *lauf', laufe\
VER AUX & finite Hilfsverbform & *bin, hast\
VER AUX INF & Infinitiv & *haben, sein\
VER AUX PA2 & Partizip Perfekt & *gahabt, gewesen\
VER AUX IMP & Imperativ & *sei, habe\
VER MOD & finite Modalverbform & *kannst, will\
VER MOD INF & Infinitiv & *k<span>ö</span>nnen, wollen\
VER MOD PA2 & Partizip Perfekt & *gekonnt, gewollt\
VER MOD IMP & Imperativ & *k<span>ö</span>nne\
**************

ART IND & unbestimmter Artikel & *ein, eines\
ART DEF & bestimmter Artikel & *der, des\
**

ADJ & Adjektivform & *schnelle, kleinstes\
ADJ ADV & Adjektiv, adverbiell & *(Er l<span>ä</span>uft) schnell.\
**

PRO DEM ATT & Demonstrativpronomen, attributiv & *diese (Frau)\
PRO DEM PRO & Demonstrativpronomen, pronominal & *diese\
PRO REL ATT & Relativpronomen, attributiv & *,dessen (Frau)\
PRO REL PRO & Relativpronomen, pronominal & *,welcher\
PRO POS ATT & Possesivpronomen, attributiv & *mein (Buch)\
PRO POS PRO & Possesivpronomen, pronominal & *(Das ist) meiner.\
PRO IND ATT & Indefinitpronomen, attributiv & *alle (Menschen)\
PRO IND PRO & Indefinitpronomen, pronominal & *(Ich mag) alle.\
PRO INR ATT & Interrogativpronomen, attributiv & *Welcher (Mann)?\
PRO INR PRO & Interrogativpronomen, pronominal & *Wer?\
PRO PER & Personalpronomen & *er, wir\
PRO REF & Reflexivpronomen & *sich, uns\
************

ADV & Adverb & *schon, manchmal\
ADV PRO & Pronominaladverb & *damit, dadurch\
KON UNT & unterordnende Konjunktion & *da<span>ß</span>, da\
KON NEB & nebenordnende Konjunktion & *und, oder\
KON INF & Infinitivkonjunktion & *um (zu spielen)\
KON VGL & Vergleichskonjunktion & *als, denn, wie\
KON PRI & Proportionalkonjunktion & *desto, um so, je\
PRP & Pr<span>ä</span>position & *durch, an\
********

SKZ & Sonderklasse f<span>ü</span>r *zu & *(,um) zu (spielen)\
ZUS & Verbzusatz & *(spielst) ab\
INJ & Interjektion & *Wau, Oh\
ZAL & Zahlw<span>ö</span>rter & *eins, tausend\
ZAN & Zahlen & *100, 2\
ABK & Abk<span>ü</span>rzung & *Dr., usw.\
*******

SZD & Doppelpunkt & :\
SZE & Satzendezeichen & .!?\
SZG & Gedankenstrich & -\
SZK & Komma & ,\
SZS & Semikolon & ;\
SZN & sonstige Satzzeichen & ()/\

Tagging algorithm
-----------------

The tagger uses the Church-trigram-algorithm (Church 1988), which is still unsurpassed in terms of simplicity, robustness and accuracy. However, since we assumed that longer n-grams may give more information, and since we observed that some longer n-grams are rather frequent in corpora (see figure 2 for some statistics on the Brown-corpus), we decided to compare the Church algorithm with a tagging algorithm relying on variable context widths as described by Rapp (1995).

Starting from an ambiguous word form which is to be tagged, this algorithm considers the preceding word froms - which have already been tagged - and the succeeding word forms still to be tagged. For this ambiguous word form the algorithm constructs all possible tag sequences composed of the already computed tags on the left, one of the possible tags of the critical word form and possible tags on the right.

The choice of the tag for the critical word form is a function for the length of the tag sequences to the left and to the right which can be found in the training corpus. A detailed description of this algorithm is given in Rapp (Rapp 1995, pp. 149-154).

Although some authors (Cutting et al. 1992; Schmid 1995; Feldweg 1995) claim that unsupervised tagging algorithms produce superior results, we chose supervised learning. These publications pay little attention to the fact that algorithms for unsupervised tagging require great care (or even luck) when tuning some initial parameters. It frequently happens that unsupervised learning with sophisticated tag sets ends up in local minima, which can lead to poor results without any indication to the user. Such behavior seemed unacceptable for a standard tool.

$$\beginpicture

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\setplotarea x from 0 to 1000, y from 0 to 200

\begin{footnotesize}

\axis bottom label {size of the corpus}
      ticks numbered from 0 to 1000 by 200 /

\axis left label {\stack{n,-,g,r,a,m,s}}
      ticks numbered from 0 to 200 by 50 /

\setplotsymbol ({\rm.})
\linethickness0.01em
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\plot 0 0          100 40      200 65
      400 100      600 125     800 145
      1000 155 /


\plot 0 0          100 10      200 15
      600 22.5     1000 23.5 /


\plot 0 0          500 2       1000 4 /

\setlinear
\plotsymbolspacing0.4em


\plot 0 0  200 200 /

\put {n=2} at 1050 7
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\put {n=4} at 1050 157.5

\end{footnotesize}
\endpicture$$

Training and test corpus
------------------------

For training and testing we took a fragment from the "Frankfurter-Rundschau"-corpus,[^5] which we have been collecting since 1992. Tables and other non-textual items were removed manually. A segment of 20.000 word forms was used for training, another segment of 5.000 word forms for testing. Any word forms not recognised by the morphology system were included in the lexicon. Using a special tagging editor which - on the basis of the morphology module - for each word gives a choice of possible tags, both corpora were tagged semiautomatically with the large tag set. A recent version of the editor additionally predicts the correct tag.

Results
=======

Using the probabilities from the manually annotated training corpus, the test corpus was tagged automatically. The results were compared with the previous manual annotation of the test corpus. This was done for both tagging algorithms and tag sets. For the small tag set, the Church algorithm achieved an accuracy of 95.9%, whereas with the variable-context algorithm an accuracy of 95.0% was obtained. For the large tag set the respective figures are 84.7% and 81.8%.

In comparison with other research groups, the results are similar for the small tagset and slightly better for the large tagset (see table \[Tagger\]). Surprisingly, inspite of considering less context, the Church algorithm performs better than the variable-context algorithm in both cases.

This is the reason why the current implementation of Morphy only includes the Church algorithm.[^6] As an example, figure \[Example\] gives the annotation results of a few test sentences for both tag sets.

$$\beginpicture

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\setplotarea x from 0 to 20000, y from 0 to 100

\begin{footnotesize}

\axis bottom label {size of the training corpus}
      ticks numbered from 0 to 20000 by 5000 /

\axis left label {\stack{a,c,c,u,r,a,c,y}}
      ticks numbered from 0 to 100 by 10 /

\setplotsymbol ({\rm.})
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\plot 0 0          500 70      1000 93.8
      2500 94.3    5000 94.6   7500 94.7
      10000 94.8   12500 94.9  15000 95.0
      17500 94.8   20000 95.0 /



\plotsymbolspacing0.05em

\plot 0 0          500 70      1000 93.9
      2500 94.7    5000 94.9   7500 95.2
      10000 95.4   12500 95.5  15000 95.5
      17500 95.7   20000 95.9 /

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\put{var.context} at 17500 92

\put{small} at 21250 96
\put{tag set} at 21250 93


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\plot 0 0          500 51      1000 69.2
      2500 74.0    5000 75.9   7500 77.2
      10000 78.6   12500 79.4  15000 80.1
      17500 80.8   20000 81.8 /



\plotsymbolspacing0.05em

\plot 0 0          500 51      1000 71.4
      2500 75.6    5000 78.9   7500 81.8
      10000 82.7   12500 83.3  15000 83.6
      17500 84.2   20000 84.7 /

\put{Church} at 17500 87
\put{var.context} at 17500 77

\put{large} at 21250 84
\put{tag set} at 21250 81

\end{footnotesize}
\endpicture$$

However, there are also some advantages on the side of the variable-context algorithm. First, its potential when using larger training corpora seems to be slightly higher (see figure \[Results\]). Secondly, when the algorithm is modified in such a way that sentence boundaries are not assumed to be known beforehand, the performance degrades only minimally. This means that this algorithm can actually contribute to finding sentence boundaries. And third, if there are sequences of unknown word forms in the text, the algorithm takes better guesses than the Church algorithm (examples are given in Rapp 1995, p. 155). When about 2% of the word forms in the test corpus were randomly replaced by unknown word forms, the quality of the results for the Church algorithm decreased by 0.7% for the small and by 2.0% for the large tag set. The respective figures for the variable-context algorithm are 0.9% and 1.3%, which is better overall.

In a further experiment, the contribution of the lexical probabilities to the quality of the results was examined. Without the lexical probabilities, the results decreased by 0.3% (small) and 0.6% (large tag set) for the Church algorithm, the respective figures for the variable-context algorithm were 0.9% and 0.0%.

     Die     Frau   bringt     das     Essen    .
  --------- ------ -------- --------- ------- -----
   ART DEF   SUB     VER     ART DEF    SUB    SZE

     Ich     meine    meine    Frau    .
  --------- ------- --------- ------ -----
   PER PRO    VER    POS ATT   SUB    SZE

  -----------------------------------------------------------------------------------------------------------------------
                             Winde                              das   im   Winde   flatternde   Segel   um   die   Winde
  ------------------------------------------------------------ ----- ---- ------- ------------ ------- ---- ----- -------
   **SUB & ART DEF & PRP & SUB & ADJ & SUB & PRP & ART & SUB\                                                     
                               **                                                                                 
  -----------------------------------------------------------------------------------------------------------------------

           Die                Frau           bringt
  --------------------- ----------------- -------------
   ART DEF NOM SIN FEM   SUB NOM FEM SIN   VER 3PE SIN

           das                Essen         .          Ich
  --------------------- ----------------- ----- -----------------
   ART DEF AKK SIN NEU   SUB AKK NEU SIN   SZE   PER NOM SIN 1PE

      meine             meine               Frau          .
  ------------- --------------------- ----------------- -----
   VER 1PE SIN   POS AKK SIN FEM ATT   SUB AKK FEM SIN   SZE

<span>|c|c|c|</span> Winde & das & im\
VER

3PE

SIN &

DEM NOM

SIN NEU

PRO

& PRP DAT\

<span>|c|c|c|</span> Winde & flatternde & Segel\
SUB DAT MAS SIN & PA1

SOL

NEU AKK

PLU

& SUB AKK NEU

PLU

\

     um              die                Winde         .
  --------- --------------------- ----------------- -----
   PRP AKK   ART DEF AKK SIN FEM   SUB AKK FEM SIN   SZE

Conclusions
===========

We have compared two different tagging algorithms and two different tag sets. The first tagging algorithm is the Church algorithm which uses trigrams to compute contextual probabilities. The second algorithm, the so-called variable-context algorithm, has been described in paragraph 3. The smaller of the two tag sets contains 51 parts-of-speech, the larger tag set includes additional grammatical features such as case, number and gender. The small tag set is a subset of the large tag set.

In comparison with the Church algorithm, the variable-context algorithm produces similar results for the small tag set, but significantly inferior results for the large tag set. On the other hand, the performance of the variable-context algorithm for the large tag set improves faster with increasing size of the training corpus than the performance of the Church algorithm. Thus, with tagging more training texts manually, similar results are to be expected for the two algorithms.

Considering the two tag sets, the results for the small tag set are significantly better. Nevertheless, with increasing size of the training corpus an approximation of the results seems to be possible.

One of our aims for the near future is to use the output of the tagger for lemmatization. In this way a sentence like <span>*Ich meine meine Frau.*</span> could be unambiguously reduced to <span>*ich / meinen / mein / Frau*</span>.

Bibliography {#bibliography .unnumbered}
============

S. Armstrong, G. Russell, D. Petitpierre and G. Robert (1995). An open architecture for multilingual text processing. In: <span>*Proceedings of the ACL SIGDAT Workshop. From Texts to Tags: Issues in Multilingual Language Analysis*</span>, Dublin.

H. Bergenholtz and B. Schaeder (1977). <span>*Die Wortarten des Deutschen*</span>. Klett, Stuttgart.

K. Church (1988). A stochastic parts program and noun phrase parser for unrestricted text. In: <span>*Second Conference on Applied Natural Language Processing*</span>, pp. 136-143. Austin, Texas.

D. Cutting, J. Kupiec, J. Pedersen and P. Sibun (1992). A practical part-of-speech tagger. In: <span>*Proceedings of the Third Conference on Applied Language Processing*</span>, pp. 133-140. Trento, Italy.

G. Drosdowski (1984). <span>*Duden. Grammatik der deutschen Gegenwartssprache*</span>. Dudenverlag, Mannheim.

H. Feldweg (1995). Implementation and evaluation of a German HMM for POS disambiguation. In: Feldweg and Hinrichs, eds., <span>*Lexikon und Text*</span>, pp. 41-46. Niemeyer, T<span>ü</span>bingen.

R. Hausser (1996). <span>*Linguistische Verifikation. Dokumentation zur Ersten Morpholympics*</span>. Niemeyer, T<span>ü</span>bingen.

W. Lezius (1994). Aufbau und Funktionsweise von Morphy. Internal report. Universit<span>ä</span>t-GH Paderborn, Fachbereich 2.

W. Lezius (1995). Algorithmen zum Taggen deutscher Texte. Internal report, Universit<span>ä</span>t-GH Paderborn, Fachbereich 2.

W. Lezius (1996). Morphologiesystem MORPHY. In: R. Hausser, ed., <span>*Linguistische Verifikation: Dokumentation zur Ersten Morpholympics 1994*</span>, pp. 25-35. Niemeyer, T<span>ü</span>bingen.

R. Rapp (1995). Die Berechnung von Assoziationen - Ein korpuslinguistischer Ansatz. In: Hellwig and Krause, eds., <span>*Sprache und Computer*</span>, vol. 16, Olms, Hildesheim.

H. Schmid (1995). Improvements in part-of-speech tagging with an application to German. In: Feldweg and Hinrichs, eds., <span>*Lexikon und Text*</span>, pp. 47-50. Niemeyer, T<span>ü</span>bingen.

P. Steiner (1995). Anforderungen und Probleme beim Taggen deutscher Zeitungstexte. In: Feldweg and Hinrichs, eds., <span>*Lexikon und Text*</span>. Niemeyer, T<span>ü</span>bingen.

K. Wothke, I. Weck-Ulm, J. Heinecke, O. Mertineit and T. Pachunke (1993). Statistically Based Automatic Tagging of German Text Corpora with Parts-of-Speech - Some Experiments. Technical Report 75.93.02, IBM Germany, Heidelberg Scientific Center.

[^1]: In: D. Gibbon, ed., *Natural Language Processing and Speech Technology. Results of the 3rd KONVENS Conference, Bielefeld, October 1996. Mouton de Gruyter, Berlin, 1996.*

[^2]: URL: http://www-psycho.uni-paderborn.de/lezius/morpho.html

[^3]: Morphy's current analysis speed is about 50 word forms per second on a fast PC, which is sufficient for many purposes. For the processing of larger corpora we have used Morphy to generate a full-form lexicon under UNIX. This has led to an analysis speed of many thousand word forms per second.

[^4]: Only 750 KB memory is necessary for the current lexicon.

[^5]: This corpus was generously donated by the Druck- und Verlagshaus Frankfurt am Main and has been included in the CD-ROM of the European Corpus Initiative. We thank Gisela Zunker for her help with the acquisition and preparation of the corpus.

[^6]: The speed of the tagger (including morphological analysis) is about 20 word forms per second for the large and 100 word forms per second for the small tag set on a fast PC.

{'source': 'arXiv_out/cmp-lg9610006'}