First Wordnets for Base Concepts in French, German, Czech and Estonian

Version 1, Final

November 26, 1998

Contributors:

Claudia Kunze, Andreas Wagner, Universität Tübingen
Dominique Dutoit, Memodata
Laurent Catherin, Université d’Avignon
Karel Pala, Pavel Sevecek, Masaryk University
Kadri Vider, Leho Paldre, Heili Orav, Haldur Oim, University of Tartu

Deliverable 2D007, , WP 3.2, WP 4.2
EuroWordNet, LE2-4003 or LE4-8283

Identification number
	LE4-8328-2D-007
	
Type
	Document and Lingware	
Title
	First Wordnets Base Concepts in French, German, Czech and Estonian	
Status
	Final	
Deliverable
	2D007	
Work Package
	WP3 and WP 4	
Task
	T3.2 and T4.2	
Period covered
	July 1998 - September 1998	
Date
	November 26, 1998	
Version
	1	
Number of pages
	23	
Authors
	•	Claudia Kunze, Andreas Wagner, Universität Tübingen
•	Dominique Dutoit, Memodata
•	Laurent Catherin, Université d’Avignon
•	Karel Pala, Pavel Sevecek, Masaryk University
•	Kadri Vider, Leho Paldre, Heili Orav, Haldur Oim, University of Tartu
	
WP/Task responsible	TUE, AVI	
Project contact point	 Piek Vossen
 Computer Linguistiek
 University of Amsterdam
 Spuistraat 134
 1012 VB Amsterdam
 The Netherlands
 tel.  +31 20 525 4669
 fax. +31 20 525 4429
 e-mail: Piek.Vossen@hum.uva.nl	
EC project officer	Ray Hudson	
Status
	Public	
Actual distribution
	Project Consortium, the EuroWordNet User Group, the world via http://let.uva.nl/˜	
Suplementary notes
	n. a.	
Key words	Linguistic Resources, Multilingual Wordnets, Language Engineering
	
Abstract	This deliverable describes the development of core wordnets for French, German, Estonian and Czech wordnets (Subset 1) in EuroWordNet-2. 
The Set of Common Base Concepts (see deliverable 2D001) which has been established on the basis of local Base Concept Selections for all eight languages served as a common starting point for building the first noun and verb subsets within the extension phase of EuroWordNet.
The Subset 1 wordnets mainly encode the hyperonyms and first level hyponyms of Base Concepts respectively. 
Whereas the creation of French and German subsets could rely on language specific wordnets already available, the subsets for Estonian and Czech wordnets have been build from scratch.	
Status of the abstract
	Final	
Received on	
	
Recipient’s catalogue number		

Executive Summary

This deliverable describes the development of core wordnets for French, German, Estonian and Czech wordnets (Subset 1) in EuroWordNet-2. 
The Set of Common Base Concepts (see deliverable 2D001) which has been established on the basis of local Base Concept Selections for all eight languages served as a common starting point for building the first noun and verb subsets within the extension phase of EuroWordNet.
The Subset 1 wordnets mainly encode the hyperonyms and first level hyponyms of Base Concepts respectively. 
Whereas the creation of French and German subsets could rely on language specific wordnets already available, the subsets for Estonian and Czech wordnets have been build from scratch.

Table of Contents

1	Introduction	7
2	French Subset 1	8
2.1	The method for building French Wordnet	8
2.2	Selection of subset 1	8
2.3	Manual verification of the synsets	11
3	German Subset 1	12
3.1	Building of the German Subset 1	12
3.2	Procedure	12
3.3	Artificial concepts	13
3.4	Problems and perspectives	13
4	Czech Subset 1	15
4.1	The method used for building Czech Wordnet	15
4.2	Preparation of Subset 1	15
5	Estonian Subset 1	18
5.1	Work Process	18
5.2	Problems	18
5.2.1	Language internal links	18
5.2.2	Linking to ILI	18
6	Overview	21

List of Tables

Table 1: Overview Subset 1 for French	9
Table 2: Equivalence Relations for French Subset 1	9
Table 3: Language Internal Relations for French Subset 1	10
Table 4: Manual Verification of French Subset 1	11
Table 5: Overview Subset 1 for German	13
Table 6: Equivalence Relations for German Subset 1	13
Table 7: Language Internal Relations for German Subset 1	14
Table 8: Overview Subset 1 for Czech	16
Table 9: Equivalence Relations for Czech Subset 1	17
Table 10: Overview Subset 1 for Estonian	19
Table 11: Equivalence Relations for Estonian Subset 1	19
Table 12: Language Internal Relations for Estonian Subset 1	20
Table 13: Subset2 Overview: FR, DE, CZ, EE	21
Table 14: Overview of Language Internal Relations	22
Table 15: Overview of Equivalence Relations	23

1	
Introduction

This document describes the building of Subset 1 in EuroWordNet-2. The four languages which are covered in the extension phase of EuroWordNet are French (AVI-MEM), German (TUE), Estonian (TAR) and Czech (BRN).
The common starting point for building the first subsets for each of the languages is provided by the Set of Common Base Concepts (as described in deliverable 2D001), which is the result of language specific selections and comparative evaluations on the data taking into account the outcome of Base Concepts of EuroWordNet-1 (D017D034D036 of EuroWordNet-1, LE-4003) as well.
Base Concepts show a certain level of conceptual abstraction which is reflected by their high position in the wordnet hierarchy and their high number of relational links to other concepts. The 1010 nominal and 300 verbal Base Concepts constitute the basic items, which enable to create the core wordnets of the different sites independently.
Each partner followed more or less - due to specific building conditions -the following steps:
1. providing equivalent representatives for missing Base Concepts of the local selections;
2. creation of synsets for the Local BC and Common BC representatives;
3. encoding of the hyperonyms of the Base Concepts (Local & Common BCs);
4. encoding of the first level hyponyms of Base Concepts;
5. encoding of synsets which are related to BC representatives by other relation types (than hyponomy);
6. encoding of sub-hyponymy of Base Concepts.
The procedure is described in more detail in D014D015 of EuroWordNet-1.The first core wordnets of the new languages as described in this deliverable will be evaluated within the next period of the project (see 2D008: Compared and restructured wordnets for BCs in French, German, Estonian & Czech).

2	French Subset 1
2.1	The method for building French Wordnet

The method we have chosen to use for building our Wordnet, was the translation of the synsets of Wordnet 1.5 into French. In this way we keep very similar structures for the networks, but it will give us a good overlapping with the other languages.
The first part of the work was the conversion of Princeton Wordnet 1.5 into the same relational format that we were using for our resource. All the data are so integrated into the Lexidiom format, developed by Memodata, which allows us to integrate as many languages as we want.
The next phase was the automatic translation of the Wordnet. This was realised by Memodata. The last part of the work was the manual verification of the translation. For that we have created a tool, which allows us to easily navigate into the hierarchy and modify the result. For each synset we have to verify if the translation is correct and if a word is missing.
2.2	Selection of subset 1

Three sets compose subset 1: the base concepts, their hyperonyms and the first level of their hyponyms. We have manually added the base concepts that haven’t been automatically translated. 1300 synsets compose the base concepts (990 nouns and 310 verbs)
We have then selected all the hyperonyms of the base concepts. All levels of hyperonyms have been selected. There are around 70 synsets in this set.
The last set is composed by the first level of hyponyms of the base concepts. For the nouns only the synsets with one or more hyponyms have been selected (around 5000 synsets). The synsets without hyponyms have been rejected for subset 1. We still have kept these synsets so we can always add them to our Wordnet in the future. For the verbs all of our synsets have been selected (around 2000 synsets), there were too many synsets without hyponyms.

Table 1: Overview Subset 1 for French

	Nouns	Verbs	Total	
base concepts	1010	300	1310	
Hyperonyms	76	13	89	
direct hyponyms	4437	2221	6658	
Total	5523	2534	8057	
Number of senses (variants)	8376	4658	13036	
X variants per synset	1.51	1.83	1.61	
Number of entries (words)	5914	2550	8466	
X senses per word	1.42	1.83	1.53	
Language Internal Relations	10482	4888	15370	
Average per synset	1.89	1.93	1.91	
Equivalence Relations	5523	2534	8057	
Average per synset	1	1	1	
Synsets without ILI	0	0	0	
Table 2: Equivalence Relations for French Subset 1

	Nouns	Verbs	Total	
EQ_BE_IN_STATE	0	0	0	
EQ_HAS_HOLONYM	0	0	0	
EQ_HAS_HYPERONYM	0	0	0	
EQ_HAS_HYPONYM	0	0	0	
EQ_HAS_MERONYM	0	0	0	
EQ_INVOLVED	0	0	0	
EQ_NEAR_SYNONYM	13	71	84	
EQ_ROLE	0	0	0	
EQ_SYNONYM	5510	2463	7973	
EQ_CAUSES	0	0	0	
EQ_IS_CAUSED_BY	0	0	0	
EQ_HAS_SUBEVENT	0	0	0	
EQ_IS_SUBEVENT_OF	0	0	0	
Total	5523	2534	8057	

Because of the method we used for building our Wordnet, the internal relations inside of the subset 1 are actually very similar to the internal relations in Wordnet 1.5. 

Table 3: Language Internal Relations for French Subset 1

	Nouns	Verbs	Total	
Synsets	5523	2534	8057	
BE_IN_STATE	0	0	0	
CAUSES 	0	54	54	
HAS_HYPERONYM	4963	2417	7380	
HAS_HYPONYM 	4963	2417	7380	
HAS_HOLONYM	0	0	0	
HAS_HOLO_LOCATION	0	0	0	
HAS_HOLO_MADEOF	13	0	13	
HAS_HOLO_MEMBER	40	0	40	
HAS_HOLO_PART	225	0	225	
HAS_HOLO_PORTION	0	0	0	
HAS_MERONYM	0	0	0	
HAS_MERO_LOCATION	0	0	0	
HAS_MERO_MADEOF	13	0	13	
HAS_MERO_MEMBER	40	0	40	
HAS_MERO_PART	225	0	225	
HAS_MERO_PORTION	0	0	0	
HAS_SUBEVENT	0	0	0	
HAS_XPOS_HYPERONYM	0	0	0	
HAS_XPOS_HYPONYM	0	0	0	
INVOLVED	0	0	0	
INVOLVED_AGENT	0	0	0	
INVOLVED_DIRECTION	0	0	0	
INVOLVED_INSTRUMENT	0	0	0	
INVOLVED_LOCATION	0	0	0	
INVOLVED_PATIENT	0	0	0	
INVOLVED_SOURCE_DIRECTION	0	0	0	
INVOLVED_TARGET_DIRECTION	0	0	0	
IS_CAUSED_BY	0	0	0	
IS_SUBEVENT_OF	0	0	0	
NEAR_ANTONYM	0	0	0	
NEAR_SYNONYM	0	0	0	
ROLE	0	0	0	
ROLE_AGENT	0	0	0	
ROLE_DIRECTION	0	0	0	
ROLE_INSTRUMENT	0	0	0	
ROLE_LOCATION	0	0	0	
ROLE_PATIENT	0	0	0	
ROLE_SOURCE_DIRECTION	0	0	0	
ROLE_TARGET_DIRECTION	0	0	0	
STATE_OF	0	0	0	
XPOS_NEAR_ANTONYM	0	0	0	
XPOS_NEAR_SYNONYM	0	0	0	
Total	10482	4888	15370	
Average per synset	1.89	1.93	1.91	
2.3	
Manual verification of the synsets

The automatic translation has given us around 20000 synsets in French. Actually we have manually verified around 17000 synsets.
For each synset, we have verified if the French words inside are correct. This verification is made with several bilingual French-English dictionaries. We have found that 40% of those words were wrong. But a big part of them has obtained a very low translation score during the automatic process. When we found a word missing in a synset, it has been added manually.
The base concepts and the hyperonyms that haven't been automatically processed have been manually translated. We have found a correspondence for each of those synsets. In the same way a set of hyponyms of the base concepts with more than one base concept has been manually added.
Table 4: Manual Verification of French Subset 1

	Noun	Verb	Total	
Synsets automatically translated	14217	5172	19389	
Synsets actually verified	14217	3171	17388	
Synsets manually added	1620	229	1849	
Total verified	15837	3400	19237	
Number of words automatically created	30947	13216	44163	
Number of wrong words	14370	5779	20149	
Number of words manually added	6382	2932	9314	
Total verified	22959	10369	33328	
Average per synset	1.45	3.05	1.73	

3	German Subset 1

3.1	Building of the German Subset 1

The German core wordnet is derived from the GermaNet resource, which has been developed at Tuebingen along the lines of WordNet 1.5 and from which appropriate concepts have been linked to ILI-records.
The GermaNet database was loaded in TALS, an adaptation of the Amsterdam Lexical System (ALS) as described in 2D005.
We used the TALS tool to browse through GermaNet and WordNet 1.5 simultaneously and for the manual creation of relational links between our synsets to the ILI.

3.2	Procedure

Our local Base Concept selection only provided for half the set of Common Base Concepts since we applied the base concept selection criteria quite strictly (leaving out Roschian Basic Level Concepts).
First of all the missing Base Concepts were linked to equivalent representatives. After having detected some major gaps w.r.t. the GermaNet coverage, we restructured our resource by adding some missing concepts to the database.
We then linked the hyperonyms of Base Concepts and all first level hyponyms to the ILI, which have turned out to cover the 5.000 nominal and 2.500 verbal synsets, required (as described in the tables).
It was not possible for us to match every hyperonymic and hyponymic concept of a Base Concept to a synonymic ILI-representative (as it wasn't the case for Base Concepts either). So we made heavy use of non-synonymy relation types for the EQ-links.
Due to the lack of concepts in GermaNet, which denote event nominalizations, we established quite a number of cross POS-links (e.g. linking German verbs to ILI nominalizations).
In our German subset, two semantic fields have not been fully represented: plants and animals. Our respective sub-wordnets involve terminological classification and lack general language concepts. They shall be objects to restructuring.
3.3	
Artificial concepts
Our core subset comprises 60 artificial concepts, which are directly being linked to synonymic equivalent representatives. This constitutes an argument in favor of employing artificial concepts in the wordnet hierarchies in case they are justified and reasonable.
A closer inspection of EuroWordNet Base Concepts yields similar conceptual constructs of a non-lexical nature like “change_integrity” (equivalent synset for` “?matZustandsveraenderung_1”). 
3.4	Problems and perspectives
The building of subset 1 made visible some inconsistencies w.r.t. reading distinctions both in GermaNet and EuroWordNet resp. WordNet 1.5, which focus mainly on regular polysemy and verb diathesis. The evaluation procedure may be very helpful to improve the wordnets also in this respect.
For the extension of our core wordnet we are planning to employ the MRD-HarperCollins for semi-automatically creating ILI-links.
Table 5: Overview Subset 1 for German
	Nouns	Verbs	Total	
Synsets	5029	2899	7928	
Number of senses (variants)	6679	3824	10503	
X variants per synset	1.33	1.32	1.32	
Number of entries (words)	6254	2811	9065	
X senses per word	1.07	1.36	1.16	
Language Internal Relations	11464	6268	17732	
Average per synset	2.28	2.16	2.24	
Equivalence Relations	5424	3202	8626	
Average per synset	1.08	1.1	1.09	
Synsets without ILI	0	0	0	
Table 6: Equivalence Relations for German Subset 1
	Nouns	Verbs	Total	
EQ_BE_IN_STATE	1	9	10	
EQ_HAS_HOLONYM	74	0	74	
EQ_HAS_HYPERONYM	594	293	887	
EQ_HAS_HYPONYM	226	51	277	
EQ_HAS_MERONYM	35	1	36	
EQ_INVOLVED	0	0	0	
EQ_NEAR_SYNONYM	219	309	528	
EQ_ROLE	0	18	18	
EQ_SYNONYM	4272	2488	6760	
EQ_CAUSES	3	21	24	
EQ_IS_CAUSED_BY	0	9	9	
EQ_HAS_SUBEVENT	0	1	1	
EQ_IS_SUBEVENT_OF	0	2	2	
Total	5424	3202	8626	

Table 7: Language Internal Relations for German Subset 1
	Nouns	Verbs	Total	
Synsets	5029	2899	7928	
BE_IN_STATE	0	0	0	
CAUSES 	0	47	47	
HAS_HYPERONYM	5113	3011	8124	
HAS_HYPONYM 	5113	3011	8124	
HAS_HOLONYM	488	0	488	
HAS_HOLO_LOCATION	0	0	0	
HAS_HOLO_MADEOF	0	0	0	
HAS_HOLO_MEMBER	0	0	0	
HAS_HOLO_PART	0	0	0	
HAS_HOLO_PORTION	0	0	0	
HAS_MERONYM	488	0	488	
HAS_MERO_LOCATION	0	0	0	
HAS_MERO_MADEOF	0	0	0	
HAS_MERO_MEMBER	0	0	0	
HAS_MERO_PART	0	0	0	
HAS_MERO_PORTION	0	0	0	
HAS_SUBEVENT	0	0	0	
HAS_XPOS_HYPERONYM	0	0	0	
HAS_XPOS_HYPONYM	0	0	0	
INVOLVED	0	0	0	
INVOLVED_AGENT	0	0	0	
INVOLVED_DIRECTION	0	0	0	
INVOLVED_INSTRUMENT	0	0	0	
INVOLVED_LOCATION	0	0	0	
INVOLVED_PATIENT	0	0	0	
INVOLVED_SOURCE_DIRECTION	0	0	0	
INVOLVED_TARGET_DIRECTION	0	0	0	
IS_CAUSED_BY	0	0	0	
IS_SUBEVENT_OF	0	1	1	
NEAR_ANTONYM	262	198	460	
NEAR_SYNONYM	0	0	0	
ROLE	0	0	0	
ROLE_AGENT	0	0	0	
ROLE_DIRECTION	0	0	0	
ROLE_INSTRUMENT	0	0	0	
ROLE_LOCATION	0	0	0	
ROLE_PATIENT	0	0	0	
ROLE_SOURCE_DIRECTION	0	0	0	
ROLE_TARGET_DIRECTION	0	0	0	
STATE_OF	0	0	0	
XPOS_NEAR_ANTONYM	0	0	0	
XPOS_NEAR_SYNONYM	0	0	0	
Total	11464	6268	17732	
Average per synset	2.28	2.16	2.24	

4	Czech Subset 1
4.1	The method used for building Czech Wordnet
The method we have chosen for building Czech Wordnet consisted of three relatively independent steps. The first part of the work was the necessary conversion of Princeton Wordnet 1.5 into the text database format in which our resources were kept. Second, the list of Czech Base Concepts containing approximately 1010 nouns and 300 verbs has been obtained from the Explanatory Dictionary of Czech (Explanatory Dictionary of Czech and also Czech Dictionary of Literary Czech has been used). We have sorted out all the entries that contained genus proximum definitions (hyperonyms). This was done with a simple parser that retrieved the definitions in EDC. In the third step we have associated the tentative list with English equivalents – those were obtained from the bilingual Czech-English Lingea Lexicon in electronic form. After checking the list of Czech Base Concepts, synsets have been manually attached to the particular concepts and the equivalence links to Wordnet 1.5 have been established – in this respect the Dictionary of Czech Synonyms existing in electronic form is being used as well. 

4.2	Preparation of Subset 1
In the next phase we have started with the earlier prepared list of Czech Base Concepts containing approximately 1010 noun and 300 verb synsets. The list of the noun and verb synsets has been further checked and special attention has been paid to the more subtle differentiation of the senses within Czech synsets. Also their links to WN 1.5 senses have been examined in our working format and then imported to LWN Import-Export File Format and checked again using the Polaris tool. This work is still going on. The further work involved the following steps:
i) 	In the first step the simple pattern matching program has been used that associated Czech and English equivalents (via bilingual Lingea C-E-C Lexicon) with the corresponding WordNet 1.5 synsets – in this way a larger list of Czech synsets has been produced containing now approx. 6300 Czech noun synsets. The error rate of the program is about 20%; thus its results have been checked manually.
ii) 	The starting list of Czech verb synsets (originally 300 verbs) has been checked with regard to the senses (this represents a sort of problem since the existing Czech resources are not always reliable enough in this respect – typically they yield only a small number of the senses) and their links to WordNet 1.5 synsets. This resulted in the second version of the list of verb synsets containing now approximately 450 verbs. More attention will be paid to the verbs in the next stage: the list is going to be enlarged to contain 2500 synsets soon.
iii) 	The list of present-day Czech collocations that can be used as noun synsets or form elements of some noun synsets has been compiled (approx. 1800 items) from the Czech corpus ESO (61 mil. running words) and integrated into the present list of the noun synsets. The resources we have to work with lack reliable collocation data as well, thus we have to prepare these data both for EDC and Czech WordNet at the same time.
iv) 	The last step consisted in putting the mentioned lists of nouns and noun collocations together and converting the resulting list into LWN Import-Export File format using the Polaris tool. 
The results of the described steps can be seen in the following table:
Table 8: Overview Subset 1 for Czech
	Nouns	Verbs	Total	
Synsets	6377	466	6843	
Number of senses (variants)	7840	597	8407	
X variant per synset	1.23	1.28	1.25	
Number of entries (words)	6833	250	7083	
X senses per word	1.15	1.3	1.22	
Language Internal Relations	in prep.*	in prep.*	in prep.*	
Average per synset				
Equivalence Relations	6377	466	6843	
Average per synset	1	1	1	
Synsets without ILI	0**	0**	0**	

Remark 1:
*As it can be seen from Table 1 we so far do not offer the information about LIR (see below) – our strategy is probably slightly different from other partners: first, we want to complete the list of all the noun and verb synsets and check all their senses and equivalence links. Then LIR and other relations will be dealt with as completely as possible and included into Czech WordNet.
**We do not give the number of Czech synsets that do not have links to the ILI though there is a number of them. It would be possible to give a rough estimation: for noun synsets there will be at least 150 such synsets and for verb synsets certainly about 300 of them: that is caused by the fact that Czech verbs occur in aspect pairs, from which iteratives can be regularly derived and there are also reflexives with reciprocal and reflexive meaning. This issue deserves a deeper examination and is closely related to the solution chosen in relation to LIR (language internal relations). It is our intention to examine these problems quite soon, particularly in December 98 and January 99.

Table 9: Equivalence Relations for Czech Subset 1

	Nouns	Verbs	Total	
EQ_BE_IN_STATE	0	0	0	
EQ_HAS_HOLONYM	0	0	0	
EQ_HAS_HYPERONYM	0	0	0	
EQ_HAS_HYPONYM	0	0	0	
EQ_HAS_MERONYM	0	0	0	
EQ_INVOLVED	0	0	0	
EQ_NEAR_SYNONYM	23	46	69	
EQ_ROLE	0	0	0	
EQ_SYNONYM	6354	420	6774	
EQ_CAUSES	0	0	0	
EQ_IS_CAUSED_BY	0	0	0	
EQ_HAS_SUBEVENT	0	0	0	
EQ_IS_SUBEVENT_OF	0	0	0	
Total	6377	466	6843	

Remark 2: 
The numbers in Table 2 are only preliminary estimations. Full attention will be paid to them when the more preferred parts of the work will be finished.
For the reasons mentioned above we do not give any values for LIR: in the present moment they would represent only preliminary and rough approximations. Thus, in our opinion, it would be premature to try to fill in the data into the table that should contain the internal relations inside of subset 1 now.
5	
Estonian Subset 1

5.1	Work Process

Building Subset 1 for Estonian has been an extension of the existing database, which was developed in the following four directions:
•	creating missing base concepts 
•	determining language internal hyperonymy/hyponymy relations; Estonian WordNet included only occasionally language internal relations so far 
•	adding missing concepts to fill in gaps in hyperonymy hierarchies
•	first level hyponyms were created to base concepts
All work was done manually using Polaris. Linguistic information is based on Estonian Explanatory Dictionary (EKSS, electronic copy available) and several other (paper) dictionaries. Hierarchies depend largely on EKSS glosses and in a smaller scale to the examples of WordNet 1.5.
5.2	Problems
5.2.1	Language internal links
We have tried where possible to organize coordinates so that they are of a similar level of abstraction/concreteness. This means creating some intermediate levels and grouping concepts more systematically than in WN1.5.
In some cases we have found it useful to link a concept with more than one hyperonym instead of creating two different senses for that purpose. E. g. asutus ‘establishment’ is both a structure/construction and an organization.
Estonian misses an equivalent to the top-level hyperonym entity. Therefore, many hierarchies that are connected by this concept in WN1.5 remain separate in Estonian.

5.2.2	Linking to ILI
In many cases an Estonian concept has been linked to more than one ILI through eq_generalization relation. In those cases we have felt that distinguishing different senses would be a violation of Estonian. E.g. engine and motor match to one concept mootor 1, jıumasin 1, masin 2 in Estonian. In some similar cases we have made the Estonian concept a hyperonym.

A systematic problem has been that of causative verbs. In Estonian they are lexically differentiated but it is not so in ILI. We have created two synsets in Estonian and linked one of them to the ILI record via eq_synonym link and the other via eq_is_caused_by link as e.g. in the case of shape, form.
Table 10: Overview Subset 1 for Estonian
	Nouns	Verbs	Total	
Synsets	2281	849	3130	
Number of senses (variants)	3813	1959	5772	
X variants per synset	1.67	2.3	1.84	
Number of entries (words)	3236	1151	4386	
X senses per word	1.18	1.7	1.31	
Language Internal Relations	3646	362	4008	
Average per synset	1.59	0.42	1.28	
Equivalence Relations	2451	757	3208	
Average per synset	1.07	0.89	1.02	
Synsets without ILI	35	223	258	

Table 11: Equivalence Relations for Estonian Subset 1

	Nouns	Verbs	Total	
EQ_BE_IN_STATE	0	0	0	
EQ_HAS_HOLONYM	11	0	11	
EQ_HAS_HYPERONYM	121	12	133	
EQ_HAS_HYPONYM	121	12	133	
EQ_HAS_MERONYM	5	1	5	
EQ_INVOLVED	0	2	2	
EQ_NEAR_SYNONYM	131	181	312	
EQ_ROLE	0	0	0	
EQ_SYNONYM	1972	459	2431	
EQ_CAUSES	0	0	0	
EQ_IS_CAUSED_BY	1	9	10	
EQ_HAS_SUBEVENT	0	0	0	
EQ_IS_SUBEVENT_OF	0	0	0	
Total	2451	757	3208	

Table 12: Language Internal Relations for Estonian Subset 1
	Nouns	Verbs	Total	
Synsets	2281	849	3130	
BE_IN_STATE	0	0	0	
CAUSES 	3	18	21	
HAS_HYPERONYM	1741	136	1877	
HAS_HYPONYM 	1741	136	1877	
HAS_HOLONYM	12	0	12	
HAS_HOLO_LOCATION	0	0	0	
HAS_HOLO_MADEOF	0	0	0	
HAS_HOLO_MEMBER	4	0	4	
HAS_HOLO_PART	16	0	16	
HAS_HOLO_PORTION	0	0	0	
HAS_MERONYM	12	0	12	
HAS_MERO_LOCATION	0	0	0	
HAS_MERO_MADEOF	0	0	0	
HAS_MERO_MEMBER	4	0	4	
HAS_MERO_PART	16	0	16	
HAS_MERO_PORTION	0	0	0	
HAS_SUBEVENT	2	2	4	
HAS_XPOS_HYPERONYM	0	0	0	
HAS_XPOS_HYPONYM	0	0	0	
INVOLVED	0	12	12	
INVOLVED_AGENT	0	4	4	
INVOLVED_DIRECTION	0	0	0	
INVOLVED_INSTRUMENT	0	13	13	
INVOLVED_LOCATION	0	1	1	
INVOLVED_PATIENT	0	1	1	
INVOLVED_SOURCE_DIRECTION	0	0	0	
INVOLVED_TARGET_DIRECTION	0	1	1	
IS_CAUSED_BY	16	5	21	
IS_SUBEVENT_OF	1	3	4	
NEAR_ANTONYM	2	12	14	
NEAR_SYNONYM	14	4	18	
ROLE	12	0	12	
ROLE_AGENT	4	0	4	
ROLE_DIRECTION	0	0	0	
ROLE_INSTRUMENT	13	0	13	
ROLE_LOCATION	1	0	1	
ROLE_PATIENT	1	0	1	
ROLE_SOURCE_DIRECTION	0	0	0	
ROLE_TARGET_DIRECTION	1	0	1	
STATE_OF	0	0	0	
XPOS_NEAR_ANTONYM	2	2	4	
XPOS_NEAR_SYNONYM	9	9	18	
Total	3646	362	4008	
Average per synset	1.59	0.42	1.28	

6	Overview

The next tables summarize the results for all the 4 wordnets. We see that the quantitative goals for the first subset (the core wordnets) have been reached. The distributions of variants per synset and senses per entry are reasonably in balance and are similar to the ratios for the EuroWordNet-1 languages (Dutch, Italian and Spanish). Furthermore, in this phase of the project the main relation is hyponymy. Other types of relations are rarely assigned. Depending on the approach (to depart from WordNet1.5 or to build up a wordnet independently) we see that some wordnets have only simple equivalence relations (French and Czech) and others have various types of equivalences (German and Estonian).
Table 13: Subset2 Overview: FR, DE, CZ, EE
	French	German	
	Noun	Verb	Oth	Total	Noun	Verb	Oth	Total	
Synsets	5523	2534	0	8057	5029	2899	0	7928	
No. of senses	8376	4658	0	13036	6679	3824	0	10503	
Sens./syns.	1.51	1.83		1.61	1.33	1.32		1.32	
Entries	5914	2550	0	8466	6254	2811	0	9065	
Sens./entry	1.42	1.83		1.53	1.07	1.36		1.16	
LIRels.	10482	´4888	0	15370	11454	6268	0	17732	
LIRels/syns	1.89	1.93		1.91	2.28	2.16		2.24	
EQRels-ILI	5523	2534	0	8057	5424	3202	0	8626	
EQRels/syn 	1	1		1	1.08	1.1	-	1.09	
Synsets without ILI	0	0	0	0	0	0	0	0	

	Czech	Estonian	
	Noun	Verb	Oth	Total	Noun	Verb	Oth	Total	
Synsets	6377	466	0	6843	2281	849	0	3130	
No. of senses	7840	597	0	8407	3813	1959	0	5772	
Sens./syns.	1.23	1.28		1.25	1.67	2.3		1.84	
Entries	6833	250	0	7083	3236	1151	0	4386	
Sens./entry	1.15	1.3		1.22	1.18	1.7		1.31	
LIRels.	in prep.	in prep.	0	in prep.	3646	362	0	4008	
LIRels/syns					1.59	0.42		1.28	
EQRels-ILI	6377	466	0	6843	2451	757	0	3208	
EQRels/syn 	1	1		1	1.07	0.89		1.02	
Synsets without ILI	0	0	0	0	35	223	0	258	

Table 14: Overview of Language Internal Relations
Language Internal Relations	French	German	Czech	Estonian	
BE_IN_STATE	0	0.00%	0	0.00%			0	0.00%	
CAUSES	54	0.35%	47	0.26%			21	0.52%	
HAS_HYPERONYM	7380	48.01%	8124	45.81%			1877	46.83%	
HAS_HYPONYM	7380	48.01%	8124	45.81%			1877	46.83%	
HAS_HOLONYM	0	0.00%	488	2.75%			12	0.29%	
HAS_HOLO_LOCATION	0	0.00%	0	0.00%			0	0.00%	
HAS_HOLO_MADEOF	13	0.08%	0	0.00%			0	0.00%	
HAS_HOLO_MEMBER	40	0.26%	0	0.00%			4	0.09%	
HAS_HOLO_PART	225	1.46%	0	0.00%			16	0.39%	
HAS_HOLO_PORTION	0	0.07%	0	0,00%			0	0.00%	
HAS_MERONYM	0	0.00%	488	2.75%			12	0.29%	
HAS_MERO_LOCATION	0	0.00%	0	0.00%			0	0.00%	
HAS_MERO_MADEOF	13	0.08%	0	0.00%			0	0.00%	
HAS_MERO_MEMBER	40	0.26%	0	0.00%			4	0.09%	
HAS_MERO_PART	225	1.46%	0	0.00%			16	0.39%	
HAS_MERO_PORTION	0	0.00%	0	0,00%			0	0.00%	
HAS_SUBEVENT	0	0.00%	0	0.00%			0	0.00%	
HAS_XPOS_HYPERONYM	0	0.00%	0	0,00%			4	0.09%	
HAS_XPOS_HYPONYM	0	0.00%	0	0,00%			0	0.00%	
INVOLVED	0	0.00%	0	0.00%			12	0.29%	
INVOLVED_AGENT	0	0.00%	0	0.00%			4	0.09%	
INVOLVED_DIRECTION	0	0.00%	0	0.00%			0	0.00%	
INVOLVED_INSTRUMENT	0	0.00%	0	0.00%			13	0.32%	
INVOLVED_LOCATION	0	0.00%	0	0.00%			1	0.02%	
INVOLVED_PATIENT	0	0.00%	0	0.00%			1	0.02%	
INVOLVED_RESULT	0	0.00%	0	0.00%			0	0.00%	
INVOLVED_SOURCE_DIRECTION	0	0.00%	0	0.00%			0	0.00%	
INVOLVED_TARGET_DIRECTION	0	0.00%	0	0.00%			1	0.02%	
IS_CAUSED_BY	0	0.00%	0	0.00%			21	0.52%	
IS_SUBEVENT_OF	0	0.00%	1	0.01%			4	0.09%	
IS_MANNER_OF	0	0.00%	0	0.00%			0	0.00%	
IN_MANNER	0	0.00%	0	0.00%			0	0.00%	
NEAR_ANTONYM	0	0.00%	460	2,59%			14	0.34%	
NEAR_SYNONYM	0	0.00%	0	0.00%			18	0.44%	
ROLE	0	0.00%	0	0.00%			12	0.29%	
ROLE_AGENT	0	0.00%	0	0.00%			4	0.09%	
ROLE_DIRECTION	0	0.00%	0	0.00%			0	0.00%	
ROLE_INSTRUMENT	0	0.00%	0	0.00%			13	0.32%	
ROLE_LOCATION	0	0.00%	0	0.00%			1	0.02%	
ROLE_PATIENT	0	0.00%	0	0.00%			1	0.02%	
ROLE_RESULT	0	0.00%	0	0.00%			0	0.00%	
ROLE_SOURCE_DIRECTION	0	0.00%	0	0.00%			0	0.00%	
ROLE_TARGET_DIRECTION	0	0.00%	0	0.00%			1	0.02%	
STATE_OF	0	0.00%	0	0.00%			0	0.00%	
XPOS_NEAR_ANTONYM	0	0.00%	0	0,00%			4	0.09%	
XPOS_NEAR_SYNONYM	0	0.00%	0	0.00%			18	0.44%	
Total	15370		17732				4008		
Synsets	8057		7928				3130		
Average per synset	1.91		2.24				1.28		

Table 15: Overview of Equivalence Relations
	French	German	Czech	Estonian	
Equivalence Relations	Nouns	Verbs	Total	Nouns	Verbs	Total	Nouns	Verbs	Total	Nouns	Verbs	Total	
EQ_SYNONYM	5510	2463	7973	4272	2488	6760	6354	420	6774	1972	459	2431	
EQ_NEAR_SYNONYM	13	71	84	219	309	528	23	46	69	131	181	312	
EQ_HAS_HYPERONYM	0	0	0	594	293	887	0	0	0	121	12	133	
EQ_HAS_HYPONYM	0	0	0	226	51	277	0	0	0	121	12	133	
EQ_INVOLVED	0	0	0	0	0	0	0	0	0	0	2	2	
EQ_ROLE	0	0	0	0	18	18	0	0	0	0	0	0	
EQ_IS_CAUSED_BY	0	0	0	0	9	9	0	0	0	1	9	10	
EQ_CAUSES	0	0	0	3	21	24	0	0	0	0	0	0	
EQ_HAS_HOLONYM	0	0	0	74	0	74	0	0	0	11	0	11	
EQ_HAS_MERONYM	0	0	0	35	1	36	0	0	0	5	1	6	
EQ_HAS_SUBEVENT	0	0	0	0	1	1	0	0	0	0	0	0	
EQ_IS_SUBEVENT_OF	0	0	0	0	2	2	0	0	0	0	0	0	
EQ_BE_IN_STATE	0	0	0	1	9	10	0	0	0	0	0	0	
Total	5523	2534	8057	5424	3202	8626	6377	466	6843	2451	757	3208