Specification of German and French Wordnets
Version 0., Progress
July the 1st , 1998
Contributors:
MEMODATA
Université d’Avignon
Universität Tübingen

Deliverable 2D002
EuroWordNet, LE4-8328

Identification number
		
Type
	Document	
Title
	Preliminary report for the German and the French Wordnet 	
Status
	Final version	
Deliverable
	2D002	
Work Package
	WP1	
Task
	T1.1	
Period covered
	April - June 1998	
Date
	July 1st, 1998	
Version
	0.1	
Number of pages
	18	
Authors
	•	Dominique Dutoit, MEMODATA, MEM
•	Laurent Catherin, LIA Avignon, AVI
•	Andreas Wagner, Universität Tübingen, TUE	
WP/Task responsible	LIA	
Project contact point	 Piek Vossen
 University of Amsterdam
 Spuistraat 134
 1012 VB Amsterdam
 The Netherlands
 tel.  +31 20 525 4669
 fax. +31 20 525 4429
 e-mail: Piek.Vossen@let.uva.nl	
EC project officer	Ray Hudson	
Status
	Public	
Actual distribution
	Project Consortium, the EuroWordNet User Group, the Ad Hoc Ansii Committee for Ontology standards, the world via http://www.let.uva.nl/~ewn.
	
Suplementary notes
	n.a.	
Key words	Linguistic Resources, Ontologies, Multilingual Wordnets, Language Engineering
	
Abstract	This deliverable reports on the work carried out within task 1.1, assigned to WP1. The objectives of this task is to specify the changes of the given resources with respect to EuroWordnet database and formulate the conversions of the resources. 	
Status of the abstract
	Complete	
Received on		
Recipient’s catalogue number		

Executive summary
This deliverable reports on the work carried out within task 1.1, assigned to WP1. The objectives of this task is to specify the changes of the given resources with respect to EuroWordnet database and formulate the conversions of the resources.
The document is organised into two main parts. The first part presents the specification for the German Wordnet. It describes in the first section the resources and the tools used, and the second part deals with the specification. In the same way, the second part presents the specification for the French Wordnet, with a description of the tools and the resources, the general approach used to build the wordnet from those resources.

Table of contents

A.	Specification of German Wordnet	6
1.	Local tools and resources	6
1.1	Overview of local resources	6
1.2	Overview of local tools	6
1.3	GermaNet: Global Specification and Project Data	6
2.	Compatibility of our data with EWN	8
3.	Methodology for conversion and for generating the equivalences	9
4.	Results of experiment with test sample	10
B.	Specification of French Wordnet	11
1.	The resources available in French	11
2.	The Integral dictionary	11
3.	The three in one view of ontology	11
3.1	Wordnet and the Integral Dictionary	11
3.2	CYC and the Integral Dictionary	12
3.3	DEC dictionary and the Integral Dictionary	12
3.4	Conclusion of MEMODATA’s ontology.	13
4.	MEMODATA Integral Dictionary resources	13
4.1	MEMODATA resources in French.	13
4.2	MEMODATA resources in other languages	17
5.	Methodology of the work.	18
6.	Description of the tools	19
6.1	Lexidiom	19
6.1.1	Presentation of the software	19
6.1.2	Resources available for lexidiom	19
6.2	Semiographe	19
6.3	Data bases	19
6.3.1	Format of the data base	19
6.3.2	Microsoft Access data base	20
6.3.3	Paradox data base	20
7.	Results of experiment with test sample	21

A.	Specification of German Wordnet
1.	Local tools and resources
1.1	Overview of local resources
The following resources are employed at Tuebingen for EWN-II:
•	the GermaNet being developed at Tuebingen
machine-readable HarperCollins German-English Dictionary; an SGML version of this dictionary was developed by means of a dictionary entry parser within the COMPASS project at Tuebingen. This version allows for accessing different pieces of information within an entry, such as grammatical information, sense indicators or translations. This resource will be used for creating links to the ILI (cf. 3.).
•	the German CELEX database
•	a tagged and lemmatized online German Corpus (size: 60.000.000 words). This corpus comprises parts of the ECI Multilingual Corpus I (Frankfurter Rundschau, Donau-Kurier, VDI-Nachrichten) and the Tuebinger Newskorpus, consisting of texts collected in Tuebingen from electronic newsgroups.
•	Mannheimer Corpus I (size: 2.500.000 words).
1.2	Overview of local tools
The following tools will be used at Tuebingen for EWN-II:
•	an adaption of the ALS tool provided by AMS
•	viewers and browsing tools (gxwn, vcg, VComp)
•	data conversion tools (ewn_import, ewn_import_files)
1.3	GermaNet: Global Specification and Project Data
The German wordnet GermaNet constitutes our main local resource yielding the German input for the EuroWordNet. GermaNet has been built within the SLD project due to the lack of similar resources covering the German base vocabulary. GermaNet comprises some 20.000 nouns, verbs and adjectives. It relies on the Princeton WordNet 1.5 principles and technology with some minor extensions which facilitate the integration into EuroWordNet. GermaNet is built from scratch, taking a corpus-based approach and deriving the base vocabulary by lemmatized frequency lists from German text corpora and further resources. The relations being modelled within GermaNet are similar to WordNet 1.5 relations. In addition, linguistic principles are accounted for in a more systematic manner, e. g. w.r.t. regular polysemy and particle verbs. The following resources have been employed for building GermaNet:
•	Duden 8: Die sinn- und sachverwandten Woerter;
•	Brockhaus/Wahrig: Deutsches Woerterbuch;
•	several other monolingual dictionaries;
•	lexicographer's intuition;
•	tagged and lemmatized online German Corpus (size: 60.000.000 words);
•	Mannheimer Corpus I (size: 2.500.000 words).
The current size of GermaNet is:
•	10.652 nouns
•	6.904 verbs
•	1.657 adjectives
•	19.213 synsets
•	24.920 synonyms in synsets
There are some differences between GermaNet and WordNet 1.5:
•	GermaNet employs artificial concept nodes which are not lexicalised. They have been introduced for a more fine-grained lexical semantic organisation, especially to avoid unmotivated co-hyponomy.
•	GermaNet provides for subcategorisation frames adopted from the CELEX database.
•	adjectives are hierarchically structured within GermaNet (as opposed to the satellite approach of WordNet 1.5).
•	GermaNet integrates regular polysemy and a particle database for morphologically complex words. 
Overview: Semantic Pointers in GermaNet
Within GermaNet, semantic relations are expressed by adding pointers to other concepts to the description of a particular concept.
Note that the synonymy relation is not mentioned here, since this relation is dealt with within the description of a concept and thus does not require a pointer to other concepts.
Valid	Reverse Pointer	
Name	Symbol	Class	Name	Symbol	Lex	
		N	A	V				
Antonym	!	y	y	Y	Antonym	!	y	
Hyperonym	@	y	y	Y	Hyponym	~	n	
Hyponym	~	y	y	Y	Hyperonym	@	n	
Meronym	#	y	n	N	Holonym	%	n	
Holonym	%	y	n	N	Meronym	#	n	
Attribute	=	y	y	N	Attribute	=	n	
Entailment	*	n	n	Y				
Cause	>	n	n	Y				
Also See	^	n	y	Y				
Derived From	\	n	y	N			y	
Participle	<	n	y	N			Y	
Regular Polysemy	.	y	y	Y	Regular Polysemy	.		
Selectional					Selectional			
Restriction	|	y	y	Y	Restriction	|		
Legend
•	Symbol:	the pointer symbol used for the relation
•	Valid Class N:	is the relation valid for nouns?
•	Valid Class A:	is the relation valid for adjectives?
•	Valid Class V:	is the relation valid for verbs?
•	Reverse Pointer Name:	the name of the reverse relation
•	Reverse Pointer Symbol:	the symbol used for the reverse relation
•	Lex:	is it a lexical relation? A lexical relation is a relation that holds only for individual elements of the concept, not for the concept as a whole.
2.	Compatibility of our data with EWN
Since the architecture of GermaNet relies on WordNet 1.5 principles, there is a large amount of similarities between our data structure and the EWN data structure. Thus, it is possible to include much of the information given in GermaNet into the German part of EuroWordNet.
We can directly account for the extraction of
•	the synsets
•	the following EWN internal relation pointers:
•	antonym (including source and target variant information)
•	has_hyperonym
•	has_hyponym
•	has_meronym
•	has_holonym
•	causes
•	definitions (glosses)
•	example sentences from the GermaNet database.
Furthermore, it will be feasible to extract some of the syntactic features which are specified in the EWN format (transitivity, intransitivity etc.) from our subcategorisation frames.
We plan to enrich the actual GermaNet format so that it will be possible to capture the following information:
•	usage notes (w.r.t. sublanguage, region)
•	the 'reversed' feature for automatically generated converse relations.
These extensions will further enhance the compatibility between GermaNet and EuroWordNet.
3.	Methodology for conversion and for generating the equivalences
We have been developing tools (ewn_import, ewn_import_files) for the extraction of the information mentioned in 2. from the GermaNet data base and the conversion of our data into the EWN import format. These tools make use of retrieval procedures provided by the WordNet 1.5 C library. Furthermore, we will modify the WordNet software to realize the extensions we have envisaged. 
We employ the ALS tool developed in AMS, a very convenient and user-friendly multi-functional system for editing and viewing lexical data bases. In order to integrate our language specific data bases (GermaNet and HarperCollins German English), the ALS source code has to be modified. ALS will be used in the following respects:
•	as a convenient viewer for GermaNet and WordNet 1.5 items;
•	to create equivalence relations between German concepts and the ILI (Some heuristics for automatically creating equivalence links have been implemented in ALS.)
GermaNet is still under development and will be elaborated parallel to the EWN-II project. However, ALS will not be used directly to update the GermaNet data base. GermaNet will be distributed independently from the ALS system, so the data format and the viewers should be independent from ALS. Furthermore, the enhancement of GermaNet should obey the building principles followed so far (working on WordNet-like lexicographer files) so that outlined, but uncompleted work on particular parts of the hierarchy can be continued without the risk of loosing the overview after changing the working environment. Nevertheless, we expect that the work with ALS will help us to detect inconsistencies in GermaNet, and hence ALS will indirectly contribute to the improvement of the data base (cf. 4.).
4.	
Results of experiment with test sample
We selected the German concepts which are equivalent to the sample base concepts provided by AMS and the corresponding equivalence links manually. Then we employed the tools mentioned in 3. to retrieve the information mentioned in 2. for these concepts and to create EWN import records which contain this information and the selected equivalence links. These records could be loaded successfully into the Polaris system.
As a side effect of this procedure we detected a few minor inconsistencies in the GermaNet data. Thus, it turned out that the integration of GermaNet into EuroWordNet is helpful for eliminating yet undiscovered errors in GermaNet.
Here are statistics for this experiment:
Food concepts: 
Number of synsets: 30 
Number of entries: 42 
Number of senses: 43 
Number of internal relations: 273 
Number of equivalence relations: 36

Motion concepts: 
Number of synsets: 24 
Number of entries: 32 
Number of senses: 33 
Number of internal relations: 238 
Number of equivalence relations: 27

Emotion concepts: 
Number of synsets: 15 
Number of entries: 32 
Number of senses: 32 
Number of internal relations: 180 
Number of equivalence relations: 16

Together: 
Number of synsets: 69 
Number of entries: 106 
Number of senses: 108 
Number of internal relations: 691 
Number of equivalence relations: 79
54 internal relations could be resolved. 
637 internal relations could not be resolved because the target concepts were missing.
B.	Specification of French Wordnet
1.	The resources available in French
The French resources consist in :
•	a single complex dictionary called “ Integral dictionary ”. The “ Integral dictionary ” is the long term research MEMODATA’dictionary.
•	a preliminary work on Wordnet 1.5 from AVI.
2.	The Integral dictionary
This is a dictionary based on three complementary views of ontologies. It contains 6 languages, many words, meanings, links and relations between concepts, senses and several informations for morphology, syntax, diathese and, of course, world knowledge.
Firstly we discuss the nature of the ontology that we are creating, secondly the nature of the resources in French and thirdly the nature of the resources for other languages.
3.	The three in one view of ontology
3.1	Wordnet and the Integral Dictionary
The base structure of “ integral dictionary ” is very different from the structure of Wordnet 1.5 in three main ways
1.	in our dictionary, meanings are not just given by a single link to one concept. For example, in WN1.5, a boat that brings petrol (such as a tanker) will be link to a synset : “ tanker ”.

In our case, we will have at least two (direct) links to the words “ boat ” and “ petrol ”.

Our dictionary is strongly based on semantic components.
2.	since the begining of the project (1989), we have used many concepts (not necessarily words or meanings, but things which are structured by something else, and also structure other words) in order to relate between different parts-of-speech, and for others “ transversal relations ” which for the most part organise words.
3.	From 1989 to 1994, the links between words and concepts (there were no links between words to words) was not informative enough in terms of relations : everything was identified by “ sets of words and concepts ” and names concepts.
As a result, there are less concepts in the “ Integral dictionary ” than the number of synset in Wordnet 1.5. But in saying that, this is the comparisson of two things of a very different nature.
We are always trying to discover ways for improving the system, at the moment the total number of computing synsets is almost equal to the number of synsets in wordnet. The computing synsets are words meanings that have exactly the same deep_syntactic_ontologic_semantic representation.
If we count the numbers of these synsets, we arrive at a number of 150.000 synsets. For more or different information on this comparisson, see []
3.2	CYC and the Integral Dictionary
At the beginning of the project, the aim was not just to describe all things which exist in life and the world. But for this reason, things were modified only one year after launching the project :
•	we decided to try and forget our linguistic knowledge and to use our experience, in the future multilingual opportunity, and to obtain a final result concerning this project : linguistic information such as etymology can produce a lot of mistakes in our reasoning therefore we tried hard to pinpoint them.
•	we decided to build a “ ideal-logical-natural ” top-ontology with using a bottom-up approach.
Therefore, we have produced links between 1st Order Entity and 2nd Order Entity, also between things and happenings. 
These types of unidentified links give us the capacity to resolve semantic problems or lack of knowledge process. For example : people that sells flowers ==> florist.
3.3	DEC dictionary and the Integral Dictionary
Our team is not well acquainted with Igor Mel’Chuck, but we rate his work because of his study of lexical functions (FL), their choice, their evolution, their usage in order to produce paraphrases, allow us to think about the relations between lexical functions, ie linguistic semantic functions, and the functions which we use in ontology.
Below are two examples :
1)	the FL “ MULT ”. ex : MULT(soldier)=army. Definition : regular set of. Opposition : SING. SING(army)=soldier... Equivalent in Eurowordnet Bcs : Group (in composition).
The problem is the following : is it a good idea to put the ontology in these concepts? Perhaps the best idea would be to use the FL like the DEC which in all cases uses it as a type of link.:
•	for lexical semantic computing, we do not have to do the same thing when we are trying to find the word army, and similarly when we attempt to find the link to soldier.
•	for lexical semantic organization, our experience has shown us that after a long time it is a massive task to work on a structure of the words grouped in the FL (like army in “ group ”). This work of word’s classification would cause problems in the semantic disambiguation process, but it is probably not available also.
On the other hand, we have to consider that these FL contain inheritance properties and that the Mel’chuck’s model cannot present itself with the same efficiency as the ontologic based model.
Remark about Eurowordnet BaseConcepts : 
The relation “ Group ” exists, in French, for things of 1stOrderEntity, but also for those of 2ndOrderEntity, in the category of noun. For instance “ market ”, “ revenue ”, “ group of criminal activities = “ banditisme, criminalité, brigandisme... ” ”? 
1)	the FL “ V0-Relative to ”. Example : V0(nutritive, nourishing)=nourish.
•	Question 1 : is it a good idea to produce 2ndOrderEntity( which one) named “ relative to verb ” to detail this FL?
•	Question 2 : Where can we put the word in Eurowordnet: “ nutritive, adjective ”.
Conclusion on the DEC : the DEC's lexical function slowly describes them.There was a period of six years when most of the explanations were implicit, but now we can explain them all in a great amount of detail.
3.4	Conclusion of MEMODATA’s ontology.
We have seen that “ the Integral Dictionary ” combines the three approaches with some hesitation. Normally it is very difficult to satisfy all points of view, and these three points of view complement each other well.
4.	MEMODATA Integral Dictionary resources
4.1	MEMODATA resources in French.
All of these resources come from the industrial dictionary which is called “ the Integral dictionary ”.
The coverage of the Integral Dictionary for French is :

	Nouns	verbs	adjectives	Adverbs	preposition	conjonction	pronom	
Entries   	64 710	61 116	14 409	2 979	293	97	87	
senses    	119 527	73 555	22 530	15 153	1 311	379	175	
Entries without idioms	44 322	6 644	13 372	1 523	66	68	16	
Entries with idioms	77 444	15 396	19 325	10 539	594	91	146	

The Integral Dictionary is revolved around meanings and contains :
•	quasi-definition : definition provided by a graph of semantic concepts which is linked to the words by semantic relations (one for each word-meaning)
•	usage codes (7 types), origin and age limitation (6 types), domains (350 codes)...
•	85 well explained semantic links :
N°	SuperCategoryrelation	SubCategoryrelation	F		
All possible links : Sense->Sense, Concept->Concept, Sense->concept
Various Parts-of-speech -- APPLICATION LEVEL	
0..1000	Aucun	Aucune	4498	Relations not available for semantic inference (only syntactic information)	
1000..9999	Application	PJ rubr>Pred
PJ mot>concept
PJ mot>agent...	800	Some relations for customer applications	
Links Sense->sense  -- LINGUISTIC LEVEL
Various Parts-of-speech	
10000
..10199	Transv. Adj>N	Adj>N;rel à
Adj>N; lieu à
Adj>N; propriété de
Adj>N; rel à+ap
Adj>N; rel à+li
Adj>N; rel à+pr
Adj>N; ap+pr
Adj>N; li+pr
Adj>N; Tous
Adj>N;q cont
Adj>N;rel+cont
Adj>N;atteint d
Adj>N; sans
Adj>N; prod.pas	350

(5000)	Various lexical function from Adjective to the nouns.
Example :
1)
Pulmonary	--> related to lung (part of animal)
	--> that occured in a lung (part of animal)
	-->belonging to the lung (part of animal)
2)
acid	--> related to acid (chemical substance)
	--> that contains acidic substance or properties (chemical substance)

Note(1) : The number in brackets shows these kind of relations that exists presently only in a separate dictionary (without meaning constraint). It is essential to integrate them.
	
10200	Transv. Adj>V	Adj>V; qui V
Adj>V;qui peutV
Adj>V;quipermeV
Adj>V;quisubitV
Adj>V;qpeusubV	253

(5500)	Various lexical function from Adjective to verbs.
Example : 
computable	-->that may be computed
Note : see before note (1)	
10300	Transv. Adv>N	Adv>N;relativem	1
(2720)	Various lexical function from Adjective to adverbs.
Note : see before note (1)	
10500	Transv. N>N	Caractère de
Carac.&prop. de	157
(3000)	Various lexical function from nouns to nouns
Note : see before note (1)	
Links sense-sense -- LINGUISTIC LEVEL
Unique Part-of-speech	
19000
..19100	Other synonyms of	Other denominations
Various spelling of
Elliptic of
translate by
scientific syn. of 
regional syn. of
slang or popular syn. of
loving syn. of
child's syn. of
syntax. syn. of	142
3 349
460
7 066
3
2

318
51
2
11	
(2507)

(195.000), 80.000 senses in English + 4*29.000 for It, Sp, Po, Ge

Note : see note (1)	
19950	Antonyms of		6	(6897)
Note : see note (1)	
Links Sense->Concepts or Concepts - LIST and CLASS
Unique Part-of-speech	
20000	Cat égale	cat=, Générique	6 635	hyperonym	
20010		cat=, SemiGéné	50	hyperonym (French only)	
20020		cat=, Gén:Taxo	1 295	hyperonym (taxonomy)	
20030		cat=, Spéc. de	159 711	hyponym	
20035		cat=, Spéc suit	47 107	hyponym (the following)	
20040		cat=, encyclo	10 331	hyponym, with knowledge reference	
20045		cat=, domaine	3 181	hyponym, with domain constraint	
20050		cat=N, Caract	1 527	hyponym, form, appearance	
20055		cat=JN, Juge	130	knowledge, judgment	
20060		cat=JN, desc	3 107	knowledge, description	
20065		cat=, conséqu	58	knowledge, consequence	
20070		cat=, collocat	19	knowledge, collocation	
20100		cat=N, Ens de	2	Group, in study, see the chapter on Mel’Chuck
Note(2) : we have not really persued with this solution model; at present, there are around 3000 words in the Integral Dictionary Set named “ Group of ”.
These words belong to 1stOrderEntity and 2ndOrderEntity... What can we do???	
20110		cat=N, Tout de	1	the whole of	
20115		cat=N, Partie d	5	Part of and specific of ontologic concept	
20116		cat=N, Partie E	6 537	Part of (encyclopaedic)	
20120		cat=NV, opposit	10		
Links Sense->Concepts or Concepts - THEME, CARACTERISTICS, Associate terms
Various Part-of-speech	
26000	M/C, cat <>T	cat<>, T, ACTIV	28 043	Ontological Common point of view	
26005		cat<>, T, REF	2 240	Ontological Common point of view for static things	
26030		cat<>, T, SUIT	484	Classification in ontological point of view	
26035		cat<>, T, CONST	256	Part, without determinated part-of-speech	
26040		cat<>, T, ENCYC	2 246	Virtual knowledge	
26045		cat<>, T, domai	54	knowledge related to a domain	
26050		cat<>, T, oppos	331	opposition, antonymy between abstract concept	
26055		cat<>, T, quali	38	Part, without determinated part-of-speech, in reference to the judgment of the speaker	
26100	M/C, cat <>tl	cat<>, tl, ACTI	38 104	organisational (not conceptual) subdivision in T 	
26140		cat<>, tl, ENC	128	virtual link to an organisational (not conceptual) subdivision in T  	
26145		cat<>, tl, doma	15	domain contraint  to an organisational (not conceptual) subdivision in T  	
26200	M/C, cat <>car	cat<>, car	7 902	objective and real characterization of sth (needed to choice or describe this meaning)	
26250		cat<>, oppos	8	antonymy between the characterizations	
Links Sense->Concepts or Concepts - Concrete and Unique ENTITY of the World
Various Parts-of-speech. ALL the above relations may exist in this area. We only display the specific link.	
27000	Individu géo	apparten. géo	18 002	belonging to a place that is a proper name	
27002		app. pol.	113	political belonging to a State or a place	
27004		app. géo. part.	169	not complete belonging to a place that is a proper name	
27005		affluent de	650	affluent (of river)	
27010		se jette dans	354	affluent (of sea)	
27020	Localisation	situé dans	2	in sth (this category will be destroyed)	
27025		situé entre pl.	2	between sth (this category will be destroyed)	
27130	Individu humain	soutient	1	body (proper name), politic, support somebody	
30000	Symbolisme	symbolise	149	to symbolise	
30010		symbole représ.	4	representative of a symbol	
Divers...	
30900	Vers racine		77	to the different roots, one is reserved to "ontology"	
31000
..31100	Vérifier ou compléter	...	516	Words witch have been added to the synset	
31500
..32000	Erreur		2 013	mistakes find during manual verification for the automatic translation	

We finish here the global description of the resource for French. So, we have to precise that we focalized to the kind of links that we can compare to WN1.5. So, if we are thinking to WN1.6 that add to the model a lot of derivatives links between words-senses to word-senses (like : fisherman-->to fish, not "the fish", but as well as possible "the fishing"), we will have to complete this description with around 85.000 symmetrical links.
4.2	MEMODATA resources in other languages
The situation is given, roughly, below :

	Nouns	Verbs	adjectives	Adverbs	
	FR	GB	IT	SP	GE	FR	GB	IT	SP	GE	FR	GB	IT	SP	GE	FR	GB	IT	SP	GE	
Entries   	64710	30000	18000	18000	18000	61116	5000	4000	4000	4000	14409	4500	3000	3000	3000	2979	2500	2000	2000	2000	
senses    	119527	45000	23000	23000	23000	73555	10000	6000	6000	6000	22530	6000	5000	5000	5000	15153	4000	2500	2500	2500	
These words are linked to the "Integral dictionary".
5.	
Methodology of the work.
The work was divided in three parts :
1) First, we had to produce for the whole WN1.5 dictionary a relational format. This format was given by Lexidiom that can, now, manage all the data of WN1.5 and EWN.
2) Secondly, we will use our "Integral Dictionary" and its links to the English to compare the structure of the information and automatically suggest translations. Because of the schedule, this automatic translation did not use all the information we had ( for example, we did not use the domain, the detailed part-of-speech, the syntactic information about verbs, and , the most important, the external links of WN or EWN...). It's possible that we will do something more powerful and general if the third task needs it in the future.
3) Thirdly, we validated the translations by hand. All the synsets translated have to be manually verified.
6.	
Description of the tools
6.1	Lexidiom
6.1.1	Presentation of the software
Lexidiom is an interface which allows users to easily navigate , modify and enrich a semantic network. Actually this is an MS DOS interface, but we have a project to build a new one of the same kind in Java. So our semantic network could be available on the internet.
Lexidiom is based on a Paradox data base. The format we used is described below.
6.1.2	Resources available for lexidiom
Actually we have converted the entire Wordnet 1.5 to our semi relational format, so it is possible to navigate through the Princeton Wordnet with the lexidiom. We have integrated the top ontology and the basic concepts of Eurowordnet. So it is possible to begin a viewing session from the top level of EuroWordnet and to navigate along all the network by following the relations.
Lexidiom was first created, for maintaining our semantic dictionary called “dictionnaire integral” (see above for a description of this resources). Also now we have the same format for our dictionary and for Wordnet 1.5.
6.2	Semiographe
This is a compiled version of our dictionary that can be used in applications. For instance, a recent version of the semiographe is used for disambiguation.
The multilingual dictionary is part of Lexidiom, attached to the same concept as the French equivalent and linked via a relation of the type “to be translated by” in that particular sense. There are two types of information in this dictionary.
•	The conceptual network which associates words or expressions with sets of referents.
•	The linguistic level(for choosing the translation for instance, or describe derivational links)
Also we will use the semiographe for automatic translation of the concept.
6.3	Data bases
6.3.1	Format of the data base
Our data base is not entirely under a relational format. The information is divided into three main tables. The first one contains the word sense. The second one contains all the synsets. The last one contains all the relations. Relations could be between two word senses, or between two synsets, or between a synset and a word sense. As long as the two entities synset and word sense can be separated into two different tables, our data base will not be entirely relational.
To have a perfect relational format, we have to put the word sense and the synset together in an unique table. But it’s actually impossible for a multilingual dictionary. There will be too many records and so accessing information will become extremely slow. Thus we will still using this format.
For us the advantage of using such a kind of data base for the project, is that first we don’t have to create new tools for managing our data. We can still use our other tools. Also first there is an internal tool, which allows us to communicate more easily (between Memodata and Avignon). We have found to that having Wordnet and our resources under a same format was more convenient.
6.3.2	Microsoft Access data base
The information in Wordnet and EuroWordnet are all in ASCII files. The Wordnet 1.5 data base is an indexed and sequential data base which is saved in ASCII files. The information of EuroWordnet are transmitted by the Novell import format, which uses ASCII files too. All our resources are in a relational format. So we needed a tool which can convert from one format to the other.
We have also created a tool which allows us to import Wordnet 1.5 or a Novell file to our data base, to export information to a Novell file. We have added a lot of functionalities for managing the data base and for searching and viewing the information.
We have chosen to create this tool with Microsoft Access instead of Paradox, because it is more adapted for ASCII treatments. Under Microsoft Windows (NT or 95) accessing or transferring data from one data base to another causes no problems with the right drivers.
6.3.3	Paradox data base
Paradox was chosen by Memodata at the beginning of their project because it was more adapted to their needs than other system. So now all their dictionaries and information are saved under this format.
This data base is based on three main tables, a table with the synsets, another one with the word senses, and the last with the relations. A lot of other tables are used for the interface or for the SQL request.
As it said above, accessing data from Paradox to Access creates no problems. So there is no inconvenience to having two different data base managers.7.	
Results of experiment with test sample
We have applied the automatic translation method to Wordnet 1.5 just as said above (cf. 5). Then we have selected the concept send by AMS and the hyponyms. The next step was the manual verification of the translation for all the synsets witch were selected.
Because of this method we have obtained a lot of internal relations. The following results have been achieved for these three domains. All those synsets have been verified manually.
Food concepts: 
Number of synsets: 597 
Number of entries: 674 
Number of internal relations: 21927
Number of equivalence relations: 674
Number of near equivalence relations: 0
Manual verification:
Bad translation: 296 entries
Words manually added: 336

Motion concepts: 
Number of synsets: 619
Number of entries: 981
Number of internal relations: 63788
Number of equivalence relations: 967
Number of near equivalence relations: 14
Manual verification:
Bad translation: 924 entries
Words manually added: 629

Emotion concepts: 
Number of synsets: 186
Number of entries: 306
Number of internal relations: 23559
Number of equivalence relations: 302
Number of near equivalence relations: 4
Manual verification:
Bad translation: 171 entries
Words manually added: 146

Together: 
Number of synsets: 1402
Number of entries: 1961
Number of internal relations: 109274
Number of equivalence relations: 1943
Number of near equivalence relations: 18
Manual verification:
Bad translation: 1769 entries
Words manually added: 1111