Mathieu Mangeot

Sereysethy Touch

MOTÀMOT PROJECT: BUILDING A MULTILINGUAL LEXICAL SYSTEM VIA BILINGUAL DICTIONARIES

Keywords: multilingual lexicography, under resourced languages, contributive project, MotÀMot project

The MotAMot project aims to develop of a multilingual lexical network focused on languages of Southeast Asia and especially Vietnamese and Khmer. The macrostructure is a pivot structure with a monolingual volume for every language and a pivot one connecting each word sense of each monolingual volume. The microstructure is based on the explanatory and combinatorial lexicography. Contributions will be made online on the Jibiki platform by a community of volunteers constituted around serious games lexical. Each entry will be given a level of quality, as well as for each contributor.

INTRODUCTION

Economic issues related to technical processing Information is very important. The development of such technology is a key asset for developing countries such as Cambodia and Laos, or emerging ones such as Vietnam, Malaysia and Thailand.

As indicated by V. Berment in his thesis [START_REF] Berment | Méthodes pour informatiser des langues et des groupes de langues "peu dotées[END_REF], "Development of personal computers and networks make are now necessary to write and communicate in the same way as paper and printing were before. Word processing, emails, or even more advanced systems such as dictation software or speech synthesis are now widespread tools. It is then necessary to consider that computer programs must be added to the traditional tools otherwise the targeted goals can not be achieved any more. Computerization of a language has and an essential place in this broad context.'' However, among the 6,000 languages spoken around the world, only a handful of them reach a satisfactory "Level of computerization". To quantitatively assess the degree of computerization of a language, V. Berment proposes the following protocol: to each service or resource, a group of users representative of the language speakers assign a level of criticality Ck and a score Nk. The average weighted scores -called index -reflects their overall satisfaction. A poorly equipped language can be defined as a language whose index is less than 10/20. For example, the Khmer language, spoken in Cambodia obtains 6.5/20, and the Vietnamese language 10/20. This is mostly because the services related to the treatment of oral (or speech technologies, ie speech synthesis and word recognition) are not yet available for these two languages. It is also the case for a majority of languages in the world some of which are spoken by several tens of million speakers (for example, Bengali: 189 million, Tamil: 63 million), including within Europe countries (Lithuanian, Latvian, Polish ...)!

PRESENT STATE OF BILINGUAL LEXICOGRAPHY

The main difficulty at present for bilingual lexicography is the prohibitive construction cost for large amounts of data. For example, the Electronic Dictionary Research project (EDR) whose aim was to build a Japanese-English dictionary required more than 1,200 men-years of work. Its selling price of approximately € 84,000 is far below the actual costs of construction, costs that will probably never be reached. Anyway, these costs are too high for an individual. Thus, only institutions can acquire such a resource. Moreover, data provided at this price is used by some machine translation systems based on specific techniques.

Faced with these costs difficult to manage, publishing houses end up living on their laurels and do mainly propose new editions of existing dictionaries. Few publishers have the courage to embark on the implementation of a new high quality bilingual dictionary from scratch.

Moreover, even in the most complete dictionaries, there is almost always a lack of information especially on collocations. The few resources that take them into account do it not systematically.

Despite the advent of the Internet, there are currently few lexical resources available freely online in a good quality. Most are in fact small bilingual lexicons made by volunteers not specialists in lexicography.

Multilingual lexicography as such is still in its infancy. Indeed, there is no really a way to print a true "multilingual dictionary''. However, it is possible to find multilingual terminological databases (like Iate) or of small lexicons or multilingual phrases books.

It has also not been sufficiently proven that reusing a dictionary of a language couple A➔B in order to build two other language B➔language C and language A ➔language C was really advantageous. So this is what we would like to tackle in this project.

GOALS OF THE PROJECT

With the overall objective to participate in the computerization of under-resourced languages, this project aims to develop a lexical system in multiple languages by simultaneously building several bilingual dictionaries sharing at least one language between them. The construction of the bilingual dictionaries will be online on a Papillon-like site built on the Jibiki platform with a collaborative and volunteer based work like Wikipedia.

The bilingual links created during the edition of the entries are used first to generate bilingual reverse links, and second to create new interlingual links.

The three main objectives of this project are the launching of a new contribution dynamic around the construction of each bilingual dictionary involved -the success of Wikipedia shows that it is possible, provided that you have simple and easy to use tools -; moving laboratory experiments such as the DiCo database [START_REF] Mel | Dérivations sémantiques et collocations dans le dico/laf[END_REF] or the PARAX system [START_REF] Blanc | Parax-unl: a large scale hypertextual multilingual lexical database[END_REF] to a largescale and finally developing a testbed for validation of several assumptions made in previous work:

• Bijectivity of bilingual links and transitivity of interlingual ones;

• Massive contribution on the Web;

• Construction of a multilingual lexical system [START_REF] Polguère | Structural properties of lexical systems: Monolingual and multilingual perspectives[END_REF].

PROGRESS IN THE CONSTRUCTION OF ONLINE RESOURCES

On the architecture of multilingual resources: the Papillon project

A perfect solution, the holy grail of lexical resources, would be a multilingual lexical database with a pivot structure, of good quality and wide coverage with rich monolingual entries and interlingual links, used both by humans and machines, editable online and freely available. We launched in 2000 the Papillon project 1 in order to advance in this direction.

The macrostructure consists of a monolingual volume for each language and a pivot volume in the center (see Figure 4.1). When a new entry in a language A is added, it must be connected to the interlingual volume. These links are created either by reusing existing bilingual dictionaries of language A ➔language B, either by adding them manually from a translation. Link language A ➔language B becomes language A 1 http://www.papillon-dictionary.org ➔pivot ➔language B. If the language B entry is already connected to another entry of language C, then language A entry also benefit from these links.

Fig. 1. Papillon Pivot Macrostructure

The microstructure of the monolingual entries is rich and very detailed. It is based on the structure used for the DiCo lexical database [START_REF] Polguère | Towards a theoretically-motivated general public dictionary of semantic derivations and collocations for french[END_REF] from OLST, University of Montreal. The encoding method is borrowed directly from the explanatory and combinatorial lexicology part of the Meaning-Text Theory (MTT). This theory gives the information needed to go from a meaning to its realizations in a given language. The microstructure of the dictionaries is independent of the languages and can theoretically be used by humans and machines.

Each entry or lexical unit is based on the word sense or lexie. It consists of a name, grammatical properties, a semantic formula that can be seen as a formal definition -in the case of a predicative word sense, the formula describes the predicate and its arguments -and then a list of lexico-semantic functions -there are 56 basic functions applicable to any language and they can be combined between them -a list of examples and a list of idiomatic expressions.

The Papillon project specifications are directly inspired by this famous holy grail. But like any ambitious project, it cannot be acheived in one shot. Within time, the project Papillon has become a kind of framework or meta-project [START_REF] Mangeot | Papillon project: Retrospective and perspectives[END_REF] with several derivatives projects, each one corresponding to a particular aspect of our initial goal. As we will detail, the tools and systems aspects are covered by the Jibiki project and data collection by the JeuxDeMots project.

On the contributing aspects: Wikipedia and Wiktionary projects

The online contributory encyclopedia Wikipedia encountered a large and unquestionable success. We could expect a similar success for its little brother Wiktionary but it is yet to go (1.5 million entries for French and only 44,000 for Japanese). Wiktionaryz, who claimed to solve wiktionary misconception problems has not yet achieved its goal. Wiktionary is anyway not truly a bilingual dictionary even if there are some translation links (for example. indications of the translations context is missing).

One hypothesis that could explain this problem is the motivation. Indeed, when a person contributes to a Wikipedia article, it is rewarded by the fame. It will then be recognized as an expert in its field. It is not possible with a dictionary. The contributions are located on small parts of information and are therefore anonymous.

On the other hand, there is a technical aspect related to the structure. An encyclopedia article has a more or less free structure while a dictionary entry must follow a very specific one (catchword, grammatical information, semantic blocks, translation block, blocks of examples, etc..). It is not possible to reuse a wiki platform for build a dictionary with a well defined structure.

Once accepted the idea that writing entries dictionary is not as fun as working on a Wikipedia article, we must find solutions to motivate a community of volunteers to contribute to a dictionary. Serious lexical games are a first track. We should also highlight contributors suing for example an array of top contributors of the month. And finally, using community networks such as Facebook should also grist to the mill.

On the data collection via serious games: the JeuxDe-Mots project

The JeuxDeMots game [START_REF] Lafourcade | Jeuxdemots : un prototype ludique pour l'émergence de relations entre termes[END_REF] aims at building a rich and evolving lexical network, that could be compared to a certain extent to the famous WordNet [START_REF] Miller | Introduction to wordnet: an on-line lexical database[END_REF] database.

The principle is the following: a game needs two players. When player A initiates a game, an instruction is displayed concerning a type of competency corresponding to a lexical relation (synonym, antonym, domain, intensifier, etc.) and a word W is chosen randomly in the database. Player A has then a limited amount of time for giving propositions that answer the instruction applied to the word W.

The same word W with the same instruction is proposed to another player B and the process is the same. The two halfgames of player A and player B are not simultaneous but asynchronous. For each common answer in A and B propositions, the two players earn a certain amount of points and credits. For the word W, the common answers of A and B players are entered into the database. This process participates to the construction of a lexical network linking terms with typed and weighted relations, validated by pairs of players. The relations are typed by the instructions given to the players and weighted with the number of pair players that proposed them. The first version of the French game was launched in July 2007.

On the technical aspects: the platform Jibiki

Jibiki [START_REF] Mangeot | Online generic editing of heterogeneous dictionary entries in papillon project[END_REF] is a generic online platform for manipulating lexical resources with users and groups management. It is a community website developed initially for the Papillon Project. The platform is programmed entirely in Java, based on the "Enhydra" environment. All the data is stored in XML format in a database (Postgres). This website offers two main services: a unified interface to access simultaneously to many heterogeneous resources (monolingual dictionaries bilingual. multilingual databases, etc..) and an editing interface in order to contribute directly to dictionaries available on the platform.

The editor is based on a HTML template interface instantiated with the entry one wants to edit. The template can be generated automatically from a description of the entry structure using an XML schema. It may then be modified to improve screen rendering. It is possible to edit any type of dictionary provided that it is encoded in XML.

Several construction projects of lexical resources have used or still use this platform with success, like the GDEF project about a Estonian-French bilingual dictionary or the LexALP terminological database. The code for this platform is open source and available for download from the LIG laboratory forge2 .

DESCRIPTION OF THE RESOURCE TO BUILD

Microstructure of entries based on the Meaning-Text Theory

The microstructure of the entries composing the volumes monolingual is a simplification of the Papillon project one. This time, the entry is based on a whole word. A word is either a combination of lexical items (word meanings) or an idiomatic expression. To cope with different skill levels of contributors, the editing interface can adapt itsel and show an adapted granularity of information. For example, a beginner contributor will be invited to give a simple gloss to in order to characterize a word sense, while an expert linguist will describe the entire semantic formula.

Pivot macrostructure via bilingual interfaces

The macrostructure is also derived from the Papillon Project with a monolingual volume for each language and pivot volume in the center. However, in order not to confuse users, they will contribute via an interface with a classical view of bilingual dictionary. Each bilingual link language A ➔language B added via this interface will actually be translated in the background by the creating two interlingual links and a pivot entry representing the initial translation link. Finally the following schema will be obtained: language A ➔pivot entry ➔language B.

Creating bilingual and interlingual links

If a contributor wants to add a translation link between a word Wa in language A and a word Wb in language B, s/he can establish this link at different levels.

The ideal solution is to connect a word sense Sa of the word Wa to another word sense Sb of the word Wb. In this case, the link is bijective and Sb is also connected to Sa (see Figure 5.3).

Fig. 2. Word Sense Linking Process

If the word Wb does not have any precise word sense or If the contributor is not able to choose the correct one, it can connect directly to the word Wb. In this case, a new word sense Sb is created with a draft quality level and the link and the word sense are labeled as to refine.

In the case of reusing existing data, it is often impossible to relate information to a specific word sense. In this case, we add at the end of the word Wa, the information that one of Wa word senses may be linked to one of the word senses of Wb, but This information will not be added to Wb. It is be of course tagged as to refine as soon as possible! With the pivot macrostructure, if two links language A ➔language B and language B ➔language C exist, then it will automatically created a link language A ➔language C which quality level will be marked as draft and revise.

Data quality and contributors levels

Each part of information for each entry will be assigned a level of quality. The levels range from 1 star for a draft (when the reused data quality is not known) to 5 stars quality certified by an expert (eg, a link translation validated by a certified translator).

Similarly, contributors will be assigned a proficiency level (1 to 5 stars also). 1 star is the level of a beginner unknown in the community and 5 stars being the level of an acknowledged expert.

Then, when a contributor of level 3 reviews an entry of level 2, the entry level rises automatically to level 3. Similarly, if the work of a contributor without corrections is systematically validated by other contributors of higher level, s/he can pass automatically to the next level after a certain threshold (eg 10 contributions). For example, Figure 5.4 shows an entry with a level of 3 stars.

To go further, we plan to analyze the work of contributors. If a person contributes heavily for example on a partic-ular area, the system can automatically send regular contribution proposals in the domain.

DATA BUILDING METHODOLOGY

The data building methodology consists in three main steps: retrieving existing data, collecting new data via serious games and finally, online contribution on the Web.

Retrieving existing data

To encourage contributions, it is preferable to propose a skeleton of existing data (even of bad quality), rather than an empty dictionary (writer's block). For each language involved, a list of words will be collected in order to create an initial list of entries. It is always possible to create a new entry, but the creations will be subject to verifications.

According to the sub-projects and languages involved, several dictionaries can be used:

• Fe* dictionary projects (French -English + other language): FeM (Malay) [START_REF] Gut | Dictionnaire francais-malais[END_REF], FET (Thai), Feb (Vietnamese);

• The DiCo database for French;

• The VietDict French-Vietnamese bilingual dictionary.

• The French-Khmer phonetic bilingual dictionary [START_REF] Richer | Dictionnaire Franc ¸ais-Khmer (en phonétique)[END_REF].

The number of stars of initial items generated from this data set is based on the quality of dictionary and the granularity of data retrieved.

Special handling for Khmer

For the Khmer language, we plan to computerize an existing French-Khmer phonetic dictionary. Its building began in the late 90s, and was completed in 2006 by a small group of researchers and computer scientists gathered in the non profit organisation "Pays perdu" created by Denis Richer, a French ethnolinguist, established in Siem Reap (Cambodia). The first version of dictionary was published in spring 2007 and includes 20,000 entries. Table 1 shows an example of what the dictionary looks like.

Fig. 3. Example entry in MotÀMot dictionary

The dictionary is in Word format and the Khmer part consists only in a phonetic transcription of the entry written in a special IPA script (SIL Sophia IPA 93) set up by the Summer Institute of Linguistics. Therefore, in order to obtain a completely XML Unicode data, we have to perform the following steps:

• convert the words in SIL IPA script into an Unicode IPA script

• obtain the equivalent words in khmer script. This step might be done semi-automatically, but we will probably need a post-edition step.

• tag the entries Most of the French entries consist in a simple word, but some of them have additional information that is not tagged, eg: a gloss jambose (fruit), a feminine jardinier, ère, a domain jauge -(techn.), etc. Unfortunately, the Khmer encoded in Unicode cannot yet be read correctly on all the current platforms. On the Apple MacOs, some characters are not rendered correctly. For example, the figure 6.1.1 shows the rendering of the first khmer word. Therefore, we may need to automatically generate an Fig. 4. Dictionary in Khmer script image for each khmer word in order to fix the problem temporarily. A better solution would be to discuss with Apple in order to fix the problem definitely, but this is another story.

Data Collection via serious games

The idea is to launch a JeuxDeMots for each language project3 . The French JeuxDeMots was launched two years ago. The Vietnamese version was launched in autumn 2009. The khmer version is being translated. We hope to find a similar success than the French JeuxDeMots. Furthermore, we should should think about games allowing bilingual data collection. People interested can contact us if they want to launch a JeuxDeMots game in their language.

Online contribution on the web

The retrieved data is collected and then merged in order to give birth to a skeleton dictionary. It is then put online for correction and enrichment.

CONCLUSION

The project is already fairly well advanced. Most technical aspects concerning the platform and online serious games are solved. It remains to gather and convert existing resources. The major challenge of the project is actually our ability to motivate communities of contributors. We hope that our experience and the attraction of such a project will allow us to make a significant step in these sociological aspects.

The benefits of such a project are numerous and will help to revive the interest in the francophonie in the Southeast Asian countries. Data generated can be used by learners of French in these countries, or francophones wishing to learn a Southeast Asian language. The dictionaries may be used by tourists or businessmen directly online or via PDAs.

The communities of contributors should launch a new dynamics of cooperation around a common humanistic purpose. Moreover, it can arouse interest for expanding the project to other languages of the region.

Figure 6 . 1 .

 61 Figure 6.1.1 shows the same example in khmer script.Unfortunately, the Khmer encoded in Unicode cannot yet be read correctly on all the current platforms. On the Apple MacOs, some characters are not rendered correctly. For example, the figure 6.1.1 shows the rendering of the first khmer word. Therefore, we may need to automatically generate an

Fig. 5 .

 5 Fig. 5. Rendering problems for Khmer

Table 1 .

 1 Excerpt of the French-Khmer dictionary

	French jarret jars jasmin jauge -(techn.)   Khmer            

http://jibiki.ligforge.imag.fr

http://www.jeuxdemots.org

http://www.ltt.auf.org

ACKNOWLEDGMENTS

The MotAMot project has been partly funded by the Action de Recherche en Réseau program of the Agence Universitaire de La Francophonie 4 .


domain: info.info-cl info shs

text:
Mathieu Mangeot
email: mathieu.mangeot@imag.fr

Hong Thai Nguyen
email: hong-thai.nguyen@imag.fr

Building lexical resources: towards programmable contributive platforms

Keywords:

Lexical resources are very important in nowadays society, with the globalization and the increase of world communication and exchanges. There are clearly identified needs, both for humans and machines. Nevertheless, very few efforts are actually done in this domain. Consequently, there is an important lack of freely available good quality resources, especially for underresourced languages. Furthermore, the majority of existing bilingual dictionaries is built with one language as English. Therefore, if one wants to translate from one language (that is not English) to another, it uses English as a pivot. And even for English native speakers, it creates a lot of misunderstandings that can be critical in many situations. In order to create and extend freely available good quality rich lexical resources for under-resourced languages online with a community of voluntary contributors, Jibiki, an online generic platform for managing (lookup, editing, import, export) any kind of lexical resources encoded in XML, has been developed. This platform is successfully used in several dictionary construction projects. Concerning the data, a serious game has been launched in order to collect precious lexical information such as collocations that will be integrated later into dictionary entries. Work is now done on extending our platform in order to reuse the resulting resources and enriching them by synchronization with the other systems (language learners and translators environments, machine translation systems, etc.).

I. INTRODUCTION

There should be no need to underline the importance of lexical resources in nowadays society, with the globalization and the increase of world communication and exchanges. There are clearly identified needs, both for humans (tourism, communication, translation) and machines (analysis, machine translation, Natural Language Processing applications in general).

Nevertheless, very few efforts are actually done in this domain. Most of the time, the expensive construction costs prohibit companies to launch new projects. Consequently, there is an important lack of freely available good quality resources, especially for under-resourced languages (Pilanguages [START_REF] Berment | Méthodes pour informatiser des langues et des groupes de langues "peu dotées[END_REF]).

Furthermore, the majority of existing bilingual dictionaries is built with English as one language and another language. Therefore, if one wants to translate from one language (that is not English) to another, it uses English as a pivot. And even for English native speakers, it creates a lot of misunderstandings that can be critical in many situations.

The main issue we are trying to solve for some time already is to find a way to develop freely available good quality rich lexical resources for under-resourced languages online with a community of voluntary contributors. Another issue is to reuse the resulting resources in NLP systems and enriching them by synchronization between the lexical database and the different systems (language learners environments, translators environments, machine translation systems, etc.)

We will first explain what should be a perfect lexical resource, considered as a kind of "graal" in our domain. The second part will present the efforts done concerning the tools for developing such resources. The third part will focus on new ways to collect lexical data via serious games. In the last part, we will discuss the ways to extend the existing lexical database system in order to exchange data with other systems via API and synchronization tools. The conclusion will try to resume what has been done and what is left in order to reach our ultimate goal.

II. THE GRAAL OF LEXICAL RESOURCES

In this section, we will present what we consider as an ultimate goal for lexical resources, starting from the analysis of what is existing for Vietnamese.

A. Current situation for Vietnamese

The table I briefly shows the situation of online lexical resources for Vietnamese. We can see that most of the languages pairs available are Vietnamese-English or Vietnamese-French. There is no resource available for language pairs with neighbouring countries (Thai, Lao, Khmer, etc.). Concerning the coverage, only Bamboo indicates the number of entries, and it is far from a broad coverage of the languages, except for English (¿ 400,000 entries). Furthermore, this is the only resource that allows contributors to edit and add entries. Concerning the copyrights, only two resources are open-source while the other are protected. Concerning the information available for each entry, most of the resources are very limited. They should be considered as lexicons rather that real dictionaries.

B. Lack of lexical resources

The situation for Vietnamese is roughly the same for every other language except English. There is a lack of freely available online lexical resources of good quality and broad coverage, especially for under resourced languages (Pilanguages [START_REF] Berment | Méthodes pour informatiser des langues et des groupes de langues "peu dotées[END_REF]). It is very frequent that if one wants to translate from one language to another, it uses English as a pivot, therefore increasing misunderstandings and errors.

Nowadays, the costs of building a new bilingual dictionary from scratch is too high. This is why, most of the time, the dictionary publishing houses prefer to edit new versions of existing dictionaries instead of creating new language pairs. There is also a problem of IPR 1 . A company that invested so much in building a dictionary cannot give it for free. Consequently, we should look into the LINUX community paradigm: building dictionaries with a community of voluntary contributors.

C. Ideal situation

These problems where identified and detailed in M. Mangeot's Ph.D. thesis [START_REF] Mangeot | Environnements centralisés et distribués pour lexicographes et lexicologues en contexte multilingue[END_REF]. A perfect solution, the graal of lexical resources would be a broad coverage multilingual pivot database with rich detailed monolingual entries and interlingual links usable by humans and machines, editable online and freely available.

Starting in 2000, Papillon [START_REF] Boitet | The papillon project: cooperatively building a multilingual lexical data-base to derive open source dictionaries and lexicons[END_REF] project was launched, a new multilingual database construction project that began to address these problems.

The macrostructure consists in one monolingual volume for every language of the dictionary and one pivot volume in the middle (see Figure 1).

When a new entry in a language A is added, it must be then linked to the interlingual volume. These links are created either by reusing existing bilingual dictionaries lang A -lang B, or by entering manually the link from an existing translation. The link lang A -¿ lang B becomes lang A -¿ pivot -¿ lang B. If the entry of lang B was already linked to other languages, automatically, the entry of lang A will also benefit from these links: lang A -¿ pivot -¿ lang B, lang C, lang D, etc. This idea 1 Intellectual Property Rights The microstructure of the monolingual entries is very rich and detailed. It is is based on the structure used for the formal lexical database DiCo [START_REF] Polguère | Towards a theoretically-motivated general public dictionary of semantic derivations and collocations for french[END_REF] of the OLST laboratory in Universit de Montral. The encoding methodology is directly borrowed from the Explanatory and Combinatorial Lexicology (ECL) [START_REF] Mel'čuk | Introduction à la lexicologie explicative et combinatoire, ser. Universites francophones et champs linguistiques[END_REF], which is part of the Meaning-Text Theory. This theory gives the necessary information to go from a meaning to its realizations in a given language. The resulting dictionary microstructure is independent from the languages and the information is theoretically usable by humans and machines. Therefore, it is very suitable for our purpose.

Each entry or lexical unit is made of a name, grammatical properties (mainly a part of speech), a semantic formula which can be seen as a formal definition. In the case of a predicative lexie, it describes the entire predicate and its arguments, a government pattern which describes the syntactic realization of the arguments of the predicate, a list of lexico-semantic functions. There is a fixed number of 56 basic functions that can be applied in any language. These functions can be combined to create more elaborated ones; a list of examples; a list of full idioms.

The specifications of Papillon project refer to this graal of lexical resources. But just like high quality, broad coverage fully automatic machine translation of any text, it is not reachable in one shot. With the time, Papillon project has become a kind of meta-project [START_REF] Mangeot | Papillon project: Retrospective and perspectives[END_REF] with many sub-projects, each one corresponding to one particular aspect of the initial goal. As we will detail later, the tool aspects are covered by the Jibiki project, the data collection by the JeuxDeMots project and finally, the construction of new dictionaries by the Motamot project.

III. HOW TO BUILD LEXICAL RESOURCES, ESPECIALLY FOR UNDER-RESOURCED LANGUAGES

A. Specifications and requirements of the system

As we already mentioned, nowadays, the construction costs of a new resource are too high. There is a need of a community-driven resource building process like Wikipedia. The platform needs to be online in order for many people to access it. Unfortunately, it is not possible to use an existing wiki platform because the entry structure of a dictionary is not free. It has to be the same for all the entries. Some commercial environments exist like Tshwanelex2 or the IDM Dictionary Production System3 but our goal is to develop dictionaries for under-resourced languages. Hence, there is a need of a freely available and customisable platform.

Another key problem is how to deal with heterogeneous entry structures if we want to query, edit and reuse them all at once. Therefore, there is a need of an online platform with users/groups management, heterogeneous dictionary lookup interface and generic entry structure edition interface.

B. Jibiki: an online generic platform for managing lexical resources

The Jibiki platform [START_REF] Sérasset | Papillon lexical database project: Monolingual dictionaries and interlingual links[END_REF], [START_REF] Mangeot | Frameworks, implementation and open problems for the collaborative building of a multilingual lexical database[END_REF], [START_REF] Mangeot | Construction collaborative d'une base lexicale multilingue[END_REF] was developed to answer these needs. It is a community web site primarily developed for the Papillon project. This platform is entirely written in Java using the "Enhydra" web development framework. All XML data is stored in a standard relational database (Postgres). This community web site proposes mainly two services: a unified interface to simultaneously access many lexical resources at once (monolingual, bilingual dictionaries, multilingual databases, etc.) and a specific edition interface to contribute to the dictionaries stored on the platform.

1) The unified lookup interface: This service4 currently gives access to thirteen (13) multilingual, bilingual and monolingual dictionaries, representing more than one million entries. Every available dictionary will be queried according to its own structure from a multi-criteria search interface (see 2). Moreover, all results will be displayed in a form that fits the structure. Any monolingual, bilingual or multilingual dictionary may be added in this collection, provided that it is available in XML format. With the Jibiki platform, giving access to a new, unknown, dictionary is a matter of writing two XML files: a dictionary description and an XSL stylesheet. For currently available dictionaries, this took an average of about one hour per dictionary.

The description file gathers dictionary meta-information and a minimum set of information in the dictionarys XML structure. The Jibiki platform defines a standard structure of an abstract dictionary containing the most frequent subset of information found in most dictionaries. This abstract structure is called the Common Dictionary Markup [START_REF] Mangeot | An xml markup language framework for lexical databases environments: the dictionary markup language[END_REF]. To describe a new dictionary, one has to write an XML file that associate CDM elements to pointers in the original dictionary structure.

Along with this description, one has to define an XSL style sheet that will be applied on requested dictionary elements to produce the HTML code that defines the final form of the result. If such a style sheet is not provided, the Jibiki platform will itself transform the dictionary structure into a CDM structure and apply a generic style sheet on this structure.

The entire process of writing all the meta data files for one dictionary and importing the data takes around one hour. It is then immediately possible to lookup and edit the newly imported dictionary.

2) The key feature: an online generic editor: The main purpose of the Jibiki platform is to gather a community around the development of one or several dictionaries. Thus, the crucial challenge that has been faced was to provide a way to edit the dictionary entries directly on the platform. It was specifically difficult because we wanted to be able to edit any kind of dictionary entry (the editor had to adapt itself to the structure of the entries) and to edit them online with a simple browser (it had to be bult only with a combination of HTML forms and simple javascripts). Java applets could not be used because of compatibility problems.

The editor works with a template XHTML interface that is instanciated with the entry that the user wants to edit. This template can be generated automatically from a description of the entry structure in XML schema. It can be modified afterwards for improving the rendering on the screen. Thus, the only data needed to edit a dictionary entry on the jibiki platform (apart from the dictionary metadata described previously) is the XML schema of the structure of the entry and furthermore, any type of dictionary entry as long as it is encoded in XML.

HTML forms are very limited. The available interactors are text fields, radio buttons, check boxes and pop up menus. It was not enough to be able to edit complex entries. Thus, there was a necessity to build more complex interactors from the combination of the previous ones in order to handle lists (adding,deleting, moving an item on a list) and links (links to entries in the same volume or other ones). These elements can be themselves complex objects containing lists of other objects, etc.

Any user, who is registered and logged in to the jibiki platform web site, may contribute to the stored dictionaries by creating or editing an entry. Moreover, when a user asks for an unknown word, s/he is encouraged to contribute it to the dictionary. Contribution is made through a standard HTML interface (see Figure 3). The source files of the jibiki software platform are available freely online via subversion on the laboratory sourceforge 5 .

C. Dictionary building projects with Jibiki Platform

In this section, we present some dictionaries building projects that use the jibiki platform and detail each one 5 http://jibiki.ligforge.imag.fr specificity.

1) GDEF Estonian-French bilingual dictionary: The GDEF project [START_REF] Mangeot | Dictionary building with the jibiki platform: the gdef case[END_REF] aims at building a high quality bilingual Estonian-French dictionary for professional translators. The figure 4 shows an example entry and lookup interface. In order to obtain high quality, the building process is very strict. First, the Estonian monolingual volume has been bootstrapped with the Estonian part of an Estonian-Russian bilingual dictionary. The French monolingual volume has been filled with the freely available Morphalou6 lexicon.

The entry writing and revision process must go into the following steps:

• First a payed contributor completes an existing Estonian entry of creates a new one if it does not exist;

• The entry is next revised by a reviewer;

• It is then validated by a validator. These process is implemented via the entries status. First, every entry has a draft status. Then, when a contributor opens it in the edition interface, the status becomes "not finished". It stays in this status until the contributors decides that s/he has finished the work and clics on the "finish entry" button. Then, the status of the entry becomes "finished" and it appears in the reviewer interface. It follow the same kind of steps until it is marled as "validated" by the validator. It is them available to the public. The dictionary is available on the web 7 .

2) LexALP multilingual terminological Database: The aim of the LexALP8 project [START_REF] Sérasset | Multilingual legal terminology on the jibiki platform: The lexalp project[END_REF] was to build a multilingual (English, French, German, Italian and Slovene) terminological database on the legal terms of the alpine convention.

The particularity of this project is that each term of the domain can have several translations in the same language. For example, a specific term will not be translated in Swiss, Austria and Germany in the case of German and France and Swiss in the case of French. Furthermore, it can happen that a term has several equivalents in one language in one country. Therefore, in order to take this into account, the chosen macrostructure is a pivot one as seen in figure 5. The database is available on the web 9 .

3) MOTAMOT multi-bilingual dictionary for underresourced languages: The MOTAMOT project is the newest one. It consists in building a multilingual database via bilingual dictionaries. The languages considered are the followings: French, English, Khmer and Vietnamese. It it planned in the future to add other languages. It is targeting communities of voluntary contributors. Anybody will contribute. In order to have an idea of the quality of the data, every contributor and every entry will have a number of stars that will represent his/her level. The figure 6 shows that the French entry "abaisser" has received 3 stars.

Every people will contribute to a given bilingual dictionary, e.g. an entry with langA-langB. In the backgroud, an interlingual link will be built and added to the pivot volume. If the entry langB is linked to another entry lang C, a draft link with only one star will be added to the langA-langC dictionary and will wait for validation. A preliminary version of the platform is available on the web 10 . This is ongoing work and some results should be available after one year.

A. Remaining issues for collecting data

The success of the Wikipedia project could make anybody think that it should not be very difficult to build a dictionary online. But this is not the case. Even if their size is regularly increasing, the dictionary Wiki projects (wiktionary, wik-tionaryZ), did not meet the same success as their elder brother Wikipedia. Early experiments showed some problems when building a dictionary online with a community of voluntary contributors.

The first problem is the motivation. when people contribute to an encyclopedia entry, they are recognized as experts in their field. Their work is rewarded by the fame. But, for a dictionary entry, it is not the case because the entries are rather small compared to encyclopedias and every entry has the same structure. The contributions are much more anonymous. Therefore, it is more difficult to motivate people to contribute to writing dictionary entries compared to encyclopedia ones.

Another problem is that only specialized lexicographers can contribute to complex and detailed entries. Non specialists people that are willing to contribute cannot help very much. For example, the DiCo database [START_REF] Polguère | Towards a theoretically-motivated general public dictionary of semantic derivations and collocations for french[END_REF] contains around 1,000 entries. Nevertheless, any native speaker has an in-depth knowledge of the language albeit implicit. Anybody is able for example to distinguish correct or wrong utterances of their language.

Lets confess it, writing dictionaries is not fun! But wouldn't there be a more funny way to exploit the implicit knowledge that each one has about her/his mother tongue? All these observations led us to think about collecting important specialized lexical data through online word games.

B. JeuxDeMots: collecting data through serious word games

JeuxDeMots game tries to address the latter issues. It aims at building a rich and evolving lexical network, that could be compared to a certain extend to the famous WordNet [START_REF] Miller | Introduction to wordnet: an on-line lexical database[END_REF] database.

The principle is the following: a game needs two players. When a player A initiates a game, an instruction is displayed concerning a type of competency corresponding to a lexical relation (synonym, antonym, domain, intensifier, etc.) and a word W is chosen randomly in the database. The player A has then a limited amount of time for giving propositions that answer the instruction applied to the word W.

The same word W with the same instruction is proposed to another player B and the process is the same. The two half-games the one of the player A and the one of the player B are not simultaneous but asynchronous. For each common answer in A and B propositions, the two players earn a certain amount of points and credits. For the word W, the common answer of A and B players are entered into the database. It participates to the construction of a lexical network linking terms with typed and weighted relations, validated by pairs of players. The relations are typed by the instructions given to the players and weighted with the number of pair players that proposed them.

The structure of the lexical network that should be obtained is built upon the notions of nodes and relations between nodes like in [START_REF] Polguère | Structural properties of lexical systems: Monolingual and multilingual perspectives[END_REF]. Every node of the network is a lexical unit gathering all its lexies or word senses. The relations between nodes come from lexical functions as the ones presented by [START_REF] Mel'čuk | Introduction à la lexicologie explicative et combinatoire, ser. Universites francophones et champs linguistiques[END_REF]. The figure 7 shows the relations gathered for the French word "dictionnaire". The first version of the game for French was launched in July 2007. There are also existing versions in English, Japanese and Thai. They are available on the web 11 . The game is easily translatable into another language. People interested can contact us if they want to launch a game in their language.

C. Intermediate results of the jeuxdemots projects

After 18 months, there are more than 1,000 active players that created around 150,000 games with 20,000 of them are pending (waiting for another player). There are now more than 170,000 relation occurrences created. The players entered 10,000 new terms or forms mainly linked to the news. There are now more than 160,000 terms in the database.

The relation results obtained with JeuxDeMots (JDM) were compared to the French Euro WordNet (EWF). JDM has around 6 times more terms than EWF that has 23,000 terms. Concerning the relations, JDM has now more than twice more relations than EWF. Furthermore, these numbers are constantly increasing in the case of JDM. A sample of 100 terms the most frequently used by the JDM users was analyzed. It appears that in 97% of the cases, the associations are correct. The remaining 3% of the cases correspond to errors (most of the time typos and spelling errors) or misunderstandings. Data collected with JDM bring a lot of originality, but the precision rate is less important that the data obtained with EWF. This lack of precision is rapidly decreasing when the relations have more weight.

Another study tried also to compare the results of lexical functions obtained with JDM with the manually entered lexical functions found in the DiCo database [START_REF] Polguère | Towards a theoretically-motivated general public dictionary of semantic derivations and collocations for french[END_REF]. The Magn function (intensifier) was selected and compared the two corpora. There are 14 words in common with Magn function available. On 81 function results, there are 11 results in common. Then, the results were gathered following their weight: w=50: 0/50; w=60: 3/12; w=70 : 2/9; w=80 : 0/2; w=90 : 2/2; w=100: 1/1; w=110: 1/2; w=130: 0/1; w=340: 1/1; w=350: 1/1. If we consider the results with a weight bigger than 80, there are 6 results in common out of 8.

While the corpus is still to small to give solid conclusions, we can see that when there is a significant weight, the results of JDM are very similar to the ones of DiCo database. This intermediate result is very promising for the future. There are plans to tweak the JDM game in order to propose more often words that are also in the DiCo database for a certain lexical function. It will then be possible to obtain a bigger corpus for comparison.

V. HOW TO INTEGRATE THESE RESOURCES WITH OTHER SYSTEMS

A. Description of the needs

We showed previously that the jibiki platform is ready to be used in many resources building projects. Nevertheless, there lack of the possibility to interact with other systems (like Machine Translation) in order to mutualize the entry building efforts. There is also a need to give possibility to use semiautomatic techniques to detect inconsistencies and enhance the quality of the data. The following part will describe an extension of the Jibiki platform in order to answer to these needs.

B. PIVAX: an extension for synchronization and data handling

PIVAX is the first online 12 contributive lexical database system allowing to create, maintain and manage the lexical resources of Machine Translation systems using a "lexical pivot". These resources can be heterogeneous because their language-specific components are developed at different places, with different linguistic approaches and computational tools. Only the most basic language-specific lexical information must be stored in PIVAX, so that developers can use their own tools and protect proprietary information. In order to use the resources between different systems, a new macrostructure with three layers is proposed (figure 8). For each natural Figure 8. PIVAX Macrostructure language (NL) supported, and each formal interlingua, there are:

• One or more volumes for lexies (and associated information). Lexies correspond to word senses in dictionaries.

• A unique volume for axemes ("monolingual acceptions"). An axeme links synonymous lexies of the same language.

• A unique volume for axies (interlingual acceptions), linking synonymous axemes With this structure, the solution to collect all lexical Vietnamese resources becomes relative simple:

• for monolingual Vietnamese dictionaries, a volume is created for each dictionary, e.g. the dictionary of abbreviations or vietnamese dictionaries from Bamboo;

• for bilingual dictionaries, they are separated into two monolingual volumes and resulting relations are put into axeme and axie volumes.

This generic structure and platform gives bases to mutualize (and normalize) all resources available. For each entry, like Jibiki platform, it gives possibilities to define any kind of microstructure. In most cases, some common information are separated: lemma, POS, example, etc... All other private, nonsharing or encoding information specific for each system are commented in order to hide them from user interface. For example, it could be possible to define a microstructure for VDict dictionary, and then, the editing interface is generated automatically.

Navigation and research interface are inspired from PARAX, an environment created and used by a computational linguist [START_REF] Blanc | Parax-unl: a large scale hypertextual multilingual lexical database[END_REF]. In the following screen capture 9, we can see the French word tester from Ariane MT system volume. It is connected to other words tester from Systran MT system.

C. Usages of PIVAX for integration with other systems

We observed some problems from the first application of PIVAX for U++C 13 during the creation of Universal Words for UNL 14 system. It is not really used efficiently for some reasons:

• Subjective: each partner has its own private tools, and don't want to re-begin a new environment;

• Objective: lack of synchronization tools between the center database and the local systems The solutions are using and integrating PIVAX in our different projects, giving accessibilities via constraints and contexts. One of the projects is EOLSS 15 /UNL-FR, translation of 1/300 of the EOLSS on-line encyclopedia (25 documents, about 220,000 words) from English into the 5 other Unesco languages in 6 months. To support this project, a post-edition environment called SECTra w [START_REF] Huynh | Sectra w.1: an online collaborative system for evaluating, post-editing and presenting mt translation corpora[END_REF] has been developed. In SECTra ws interface, 3 panels are added for terminology consultation and contribution. When the user post-edits a segment, all translations of the occurrences in the segment will be displayed in the first panel. A robot collects this draft resource automatically. When the user submits a translation, by default, used translations will be taken from the first panel to the second panel. If the user changes words directly on the dictionary panel or adds some new words, theses contributions are considered stable and are copied to the third panel. In another project called OMNIA, image research & indexing via ontology, PIVAX is used like an environment for presenting a multilingual ontology [START_REF] Rouquet | Multilinguïsation d'une ontologie par des correspondances avec un lexique pivot[END_REF].

Towards a more programmable platform, our own lexical resource would be considered as a lexical graph, and the refinement work in lexical resource becomes graph manipulation. Then, a type of generic SLLP (Specialized Language for Linguistic Programming) allows users to define themselves some kinds of constraints on the lexical graphs and associated actions for lexical graph manipulation.

VI. CONCLUSION

The road to the perfect lexical resource is long and difficult. However, some progress has been done step by step. Concerning the tools, the Jibiki online lexical database managing platform is a success. It is now used in very different projects, from bilingual dictionaries to multilingual termbanks. Concerning the data, the intermediate results of the JeuxDeMots projects for French are very promising. It would be very interesting now to launch other languages versions in order to experiment with multilingual data. The Motamot project seems also very promising. There is a plan to start the contribution phase before two years from now. The project will also be a way to experiment the validity of our theory developed around the monolingual word senses and interlingual links. It will then be possible to say if the next step has been achieved successfully. Anybody who is motivated and wants to join the project is welcomed. It is mainly based on voluntary work and aims to build a reference lexical resource. Concerning the development APIs and the synchronization between databases, the internal collaborations in our team has showed interesting possibilities. Lets meet again in two years for a new conclusion.

Figure 1 .

 1 Figure 1. Multilingual Pivot Macrostructure

Figure 2 .

 2 Figure 2. Advanced search interface

Figure 3 .

 3 Figure 3. Editing interface for Papillon entry

Figure 4 .

 4 Figure 4. GDEF entry

Figure 5 .

 5 Figure 5. LexALP entry

Figure 6 .

 6 Figure 6. Motamot draft entry

Figure 7 .

 7 Figure 7. JeuxDeMots network for "dictionnaire"

Figure 9 .

 9 Figure 9. Displaying search results in columns

http://tshwanedje.com/tshwanelex/

http://www.idm.fr/

http://papillon-dictionary.org

http://www.cnrtl.fr/lexiques/morphalou

http://www.estfra.ee

LexALP: Legal Language Harmonisation System for Environment and Spatial Planning within the Multilingual Alps

http://217.199.4.152:8080/termbank/LexALP.po

http://papillon.imag.fr

urlhttp://www.jeuxdemots.org

http://javalig3.imag.fr/pivax/

open and free association of researchers, bussiness entities and people with a common interest in the development of useful applications to society based in the UNL language, http://www.unl.fi.upm.es/consorcio/

Universal Networking Language: http://www.undl.org/

Encyclopedia Of Life Support Systems), http://www.eolss.net/

ACKNOWLEDGMENT

The MOTAMOT project has been partly funded by the Action de Recherche en Rseau program of the Agence Universitaire de La Francophonie 16 .