Motaz Saad
email: motaz.saad@loria.fr

David Langlois

Kamel Smaïli

on Corpus Linguistics (CILC2013) Extracting Comparable Articles from Wikipedia and Measuring their Comparabilities

Keywords: computational linguistics, comparable corpora, comparability measure

Parallel corpora are not available for all domains and languages, but statistical methods in multilingual research domains require huge parallel/comparable corpora. Comparable corpora can be used when the parallel is not sufficient or not available for specific domains and languages. In this paper, we propose a method to extract all comparable articles from Wikipedia for multiple languages based on interlanguge links. We also extract comparable articles from Euro News website. We also present two comparability measures (CM) to compute the degree of comparability of multilingual articles. We extracted about 40K and 34K comparable articles from Wikipedia and Euro News respectively in three languages including Arabic, French, and English. Experimental results of comparability measures show that our measure can capture the comparability of multilingual corpora and allow to retrieve articles from different language concerning the same topic.

Introduction

Parallel and comparable articles are a set of texts in multiple languages, but parallel texts are translation from each others and they are aligned sentence by sentence (aligned at sentence level), while comparable texts are not the exact translation from each others, but they talk about the same topic. So, comparable corpora are a collection of comparable articles aligned at article level.

Parallel or comparable corpora are useful in several domains such as multilingual text mining, bilingual lexicons extraction, cross-lingual information retrieval and machine translation. In these domains, a lot of works use statistical methods. Comparable corpora also can be used to build parallel corpora. Indeed, there are many works about sentences alignment in the comparable corpora [START_REF] Smith | Extracting parallel sentences from comparable corpora using document level alignment[END_REF][START_REF] Do | A Fully Unsupervised Approach for Mining Parallel Data from Comparable Corpora[END_REF][START_REF] Abdul Rauf | Parallel sentence generation from comparable corpora for improved SMT[END_REF][START_REF] Hewavitharana | Extracting parallel phrases from comparable data[END_REF]. For these works, comparable corpora must be available. Morevover, statistical methods need huge data, so the more data you have, the better models you can build. Unfortunately, parallel corpora are not available for all domains and languages. They can be extended using human translators, but this is expensive and require a lot of efforts. So, comparable corpora are the best alternative in this case, because they are less expensive and more productive.

Comparable corpora can be obtained easily from multilingual textual contents on the web like newspapers websites, but aligning articles is a challenging task. Wikipedia can be considered as a good and large source for comparable corpora, because it covers many languages and topics.

Our objective is to help a media trackers such as journalists to find web documents related to a same given topic, in several languages. We focus in this work on Arabic, French and English. For that, we need to collect French/English/Arabic comparable corpora, and we need to define comparability measures (CM) between documents.

In fact, there are no available Arabic/other languages pair comparable corpora. Therefore, in Section 2, we propose a method to collect corpora from Wikipedia and Euro News. Collected articles are in Arabic, French, and English languages.

Recent work on comparability measures include [START_REF] Otero | Measuring comparability of multilingual corpora extracted from wikipedia[END_REF] who proposed a comparability measure for Wikipedia corpus. They considered internal links in articles as the vocabulary that they use to make the comparison. Internal links in Wikipedia's articles are titles for other articles. So, their degree of comparability is defined based on the amount of internal links that can be translated into the target language. In other words, their comparability measure inspects the amount of common internal links between source and target articles. Also, [START_REF] Li | Improving corpus comparability for bilingual lexicon extraction from comparable corpora[END_REF] defined the degree of comparability for the whole corpus as the expectation of finding, for each source word in the vocabulary of the source corpus, its translation in the vocabulary of the target corpus. They measured the comparability of parallel corpora, Then, they showed how the comparability degree decreased as noisy text added to the parallel corpora.

For our work in this paper, we propose in Section 3, two different comparability measures, which are based on binary and cosine similarity measure. The binary measure requires source/ target texts to be represented as bag of words, while the cosine measure requires source/target text to be represented as vectors. To represent text in vector space model, we use a multilingual document representation model based on wordNet dictionary. We also apply Latent Semantic Indexing (LSI) [START_REF] Rehurek | Subspace tracking for latent semantic analysis[END_REF]Sojka, 2010, Rehurek, 2011). Therefore, to compare documents, unlike [START_REF] Otero | Measuring comparability of multilingual corpora extracted from wikipedia[END_REF], we take into account the whole contents of the documents. Our work is closer to [START_REF] Li | Improving corpus comparability for bilingual lexicon extraction from comparable corpora[END_REF] but our methods are not based on a translation table, but on bilingual dictionaries. Moreover, [START_REF] Li | Improving corpus comparability for bilingual lexicon extraction from comparable corpora[END_REF] work is at corpus level while we propose comparability measure at document level.

The rest of this paper is organized as follows, Section 2 describes our comparable corpora extraction method. Section 3 presents the comparability measures we propose. Section 4 describes the bilingual dictionary. Section Section 5 gives experimental results. Finally, conclusions and future works are stated.

Extracting Comparable Corpora

In this section, we present two comparable corpora. The first one is extracted from Wikipedia, and the second one is extracted from Euro News website (http://www.euronews.com).

Regarding Wikipedia corpus, we extract it by parsing Wikipedia dumps of December 2011. We extract Arabic, French, and English comparable articles based on interlanguage links. In a given Wikipedia article written in a specific language, "interlanguage links" lead to corresponding articles in other languages. The form of these links is "[[languagecode : T itle]]". For example, the interlanguage links of the English language article "Rain" are presented in Figure 1. We name these corpora as AFEWC. From wiki markup, we extract the article's summary (abstract), categories, and the plain text. We structure all extracted information into XML files as shown in Figure 2. The reason of keeping wiki markup is to extract additional useful information later such as info boxes, image captions. The extraction steps are described below.

For each English article in Wikipedia dump do the following:

1. If the article contains Arabic and French interlanguage links, then extract the titles from interlanguage links for the three comparable articles. 2. Search by titles for the three comparable articles in the Wikipedia dump, and then extract them. 3. Extract the plain-text from wiki markup of the three comparable articles. 4. Write comparable articles into plain-texts and xml files.

Wikipedia December 2011 dumps contain 4M English articles, 1M French articles, and 200K Arabic articles. The extracted comparable articles in AFWEC include 40,290 aligned articles.

Regarding Euro News corpus, we extract it by parsing the html of each English news article, which contains hyperlinks to Arabic and French news articles. Then, we strip html tags for the three comparable articles, and write them into plain text files. We collected 34,442 comparable articles, we name this corpus as eNews. Same as AFEWC, eNews is aligned at article level.

The number of articles, sentences, average sentences per article, average words per article, words, and vocabulary of AFEWC and eNews are presented in Table 1. Both AFEWC and eNews are available online at http://crlcl.sf. net. These resources are interesting for many applications such as statistical machine translation, bilingual lexicons extraction, cross-language information retrieval, and comparing multilingual corpora. AFEWC is also interesting for text summarization because article's summaries (abstracts) are written by Wikipedia contributors which make them high quality summaries, they can be also used as benchmarks for text summarization researches.

Comparability Measures

As we stated in the introduction, comparable articles are nearly equivalent text in different languages. But we do not know the comparability degree of these articles. So, we propose in this section two comparability measures (CM) for comparable articles. The CM ranges from 1 (fully parallel) to 0 (not parallel nor comparable). When the measure is near 0, then the articles talk about different topics.

Binary and cosine measures are common methods for measuring similarity. In our work, we measure the comparability of articles using these measures. Binary comparability measure (binCM) requires source/target text to be represented as bag of words (BOW), while cosine comparability measure (cosineCM) requires source/target text to be represented as vectors. A bilingual dictionary can be used to align source/target words of comparable articles, but texts must be adapted to dictionaries in order to obtain good coverage (lemmatization,. . . ). In the following, we start first by defining our measures and multilingual document representation models, then, we describe the bilingual dictionary that we use, and the applied morphological analysis for words.

Regarding binCM, we first define the binary function trans(w s , d t ) that returns 1 if a translation is found in a bilingual dictionary for the source word w s in the target document d t , and 0 otherwise. So, binCM for source and target articles, d s and d t , is defined as follows:

binCM(d s , d t ) = w s ∈d s ∩V s trans(w s , d t ) |d s ∩ V s | (1)
where V s is the source vocabulary of the bilingual dictionary, d s and d t are the source and target documents, considered as bags of words. Because binCM(d s , d t ) is not symmetric, the actual value used for measuring the comparability between d s and d t is as follows:

binCM(d s , d t ) + binCM(d t , d s ) 2 (2)
Regarding cosineCM, we need to represent source/target texts as vectors. In our work, we represent multilingual document in the Vector Space Model (VSM). This is done by using a bilingual dictionary to align words in source/target vectors. Additionally, Latent Semantic Indexing (LSI) [START_REF] Rehurek | Subspace tracking for latent semantic analysis[END_REF]Sojka, 2010, Rehurek, 2011) can be used to reduce the dimensionality of VSM. We start first by defining cosine comparability measure, then we describe the VSM in details later.

Cosine similarity is a measure of similarity between two vectors by measuring the cosine of the angle between them. Cosine similarity is often used to compare documents in text mining. Given two vectors A and B of attributes representing the source and target documents, the cosine similarity cosineCM(A, B) between A and B is represented using a dot product and magnitude:

cosineCM(A, B) = A • B A × B = n i=1 A i × B i n i=1 (A i ) 2 × n i=1 (B i ) 2
(3)

The resulting similarity ranges from -1 which means exactly opposite, to 1 which means exactly the same, with 0 usually indicating independence, and in-between values indicating intermediate similarity or dissimilarity. For text matching, the attribute vectors A and B are usually the weights of words in the comparable articles. Since the weights of words are positive values, then the cosine of two documents ranges from 0 to 1: that matches with our definition of a comparability measure.

To represent multilingual articles in the vector space model (VSM), we build the source and target vectors with the following method: we use a bilingual dictionary; for each translation a → b in this dictionary, the vectors have one attribute. For the source vector this attribute is equal to the frequency of a in the source document (0 if a is not in the document), and for the target vector this attribute is equal to the frequency of b in the target document (0 if b is not in the target document). We name this representation as VSM-DIC.

Latent Semantic Indexing (LSI) can be applied to build VSM and to make mapping into a new reduced space. We start with a matrix. There is one column for each source and one column for each target word in the corpora. There is one line for each source document, and one line for each target document. The intersection between a column and a line contains the frequency of the corresponding word in the corresponding document. Note that this matrix is sparse. Then we apply LSI to map this matrix in a more compact vector space model. It is then possible to compute the vector corresponding to a document into this vector space model and apply formula 3. We name this representation as VSM-LSI.

Dictionary and coverage

Regarding the bilingual dictionary that we use for word alignment, we use Open Multilingual WordNet (OMWN) [START_REF] Bond | A survey of wordnets and their licenses[END_REF] which is available in many languages. From OMWN, we extracted 148,730 English words and 14,595 Arabic words. All these words are listed in different sets of synonyms (named 'synsets'). These sets are very useful because we use them to look for possible translations from source to target.

Because the bilingual dictionary does not cover all word variations and morphologies, we apply morphological analysis to words in documents in order to increase the coverage of dictionary between source and target texts. We first remove stopwords from all comparable articles before processing them.

There are many word reduction techniques which are language dependant. For English, stemming and lemmatization are widely used and known techniques in the community. Stemming [START_REF] Porter | Snowball: A language for stemming algorithms[END_REF] truncates a word into a stem, which is a part of the word, and may not be in the dictionary, while lemmatization [START_REF] Stark | Wordnet: An electronic lexical database[END_REF] retrieves the dictionary form (lemma) of an inflected word. Arabic morphology is rich because Arabic words have a root and prefixes, infixes, and suffixes and we can inflect many words from this root. For Arabic, rooting and light stemming [START_REF] Saad | Arabic morphological tools for text mining[END_REF] following, we describe rooting and light stemming for Arabic language. Rooting removes word's affixes, then convert to root, while light stemming just removes word's affixes. In Arabic language, word variants usually do not have similar meanings or semantics although these word variants are generated from the same root. Thus, root extraction algorithms affect the meanings of words if we consider their meanings in other languages. Arabic light stemming aims to retain the words meanings. To see how Arabic light stemming retains words meaning, consider the following Arabic word examples, the words ( ālmktbh "the library"), ( ālkātb "the author"), ( ālktāb "the book") belong to the same root ( ktb "to write"), in spite they have different meanings in English language. Arabic rooting reduces their semantics by converting them to their root. Arabic light stemming, on the other hand, maps the word ( ālmktbh "the library") to ( mktbh "library"), and the word ( ālktāb "the book") to ( ktāb "book"), and the word ( ālkātb "the author") to ( kātb "author"). Another example is the Arabic root word ( sfr "to travel") which can be inflected to two plural forms ( ālmsāfrwn ) in nominative form, and ( ālmsāfryn ) in accusative/genitive form, which both mean "the travelers". Arabic light stemmer reduces them both to ( msāfr "traveler"). Also, the word ( sysāfr "he will travel"), its light stem is ( sāfr "travel"). The reader can find these examples and another ones in Table 2 which shows the richness of Arabic morphology and how rooting can change the corresponding meanings in other languages.

In order to increase the dictionary coverage of such rich morphology while not losing words meaning, we have developed a reduction technique for Arabic words, which combines rooting and light stemming techniques. We name this technique as morphAr. The idea of morphAr is to try to reduce a word using light stemming first. If we obtain a reduced form, present in the dictionary we stop, and if not, we apply rooting to reduce the word.

In Figure 3, we measure the Out of Vocabulary (OOV) rate for all the presented word reduction techniques. If we consider word reduction techniques for each language separately, then rooting for Arabic and lemmatization for English have the lowest OOV rate. But we do not aim to just reduce OOV independently for each language, we aim to increase alignment rate for source/target words by finding the appropriate translation for these words in the bilingual dictionary. We experienced different combinations of word reduction techniques in both Arabic and English as presented in Figure 4, and we find that morphAr for Arabic and lemmatization for English lead to the best coverage in the bilingual dictionary. So, we use this combination of techniques in the following experiments.

Experimental results

We applied our comparability measures on parallel and comparable corpus. For comparable corpus, we merged AFEWC and eNews. For parallel corpus, we merged several corpora: AFB and ANN which are provided by LDC http://ldc.upenn.edu.

First we show that our comparability measures give better score to parallel corpora than to comparable corpora. This is a way to check that our measures work well. Indeed, articles in parallel corpus tend to be more close in terms of contents than comparable corpus, because parallel corpora are aligned at sentence level. Table 3 presents the results of binCM, and cosineCM using VSM-DIC and VSM-LSI. For each comparability measure, we compute the average score of comparability across article pairs in the corresponding corpus. First, results confirm that CM for parallel corpus is larger than the one for comparable corpora. Second, results also show that cosineCM based is better than binCM. This was expected because cosineCM uses richer information from the documents than binCM. Last, VSM-DIC and VSM-LSI obtained the same results, this is due to presentation of English/Arabic articles to build VSM matrix, that is, presenting English and Arabic articles separately in the matrix makes LSI not able to find the semantic relation between Arabic/English words. However, this evaluation deals only with average scores. We recall that the objective is to retrieve target from comparable articles from a given source article. So, to measure this capability of our CM, we compute the recall. Multilingual information recall is computed by providing each source article as query. For this source article, we evaluate all the target articles with the CM. Then we select the best target articles according to their scores. Then, we check if the correspondent target article is in the 1-top list (first recall R1), the 5-top list (fifth recall R5), and the 10-top list (tenth recall R10). To perform this evaluation, we select 100 source (English) articles from comparable corpora to retrieve corresponding target (Arabic) comparable articles.

57%

Figure 5 presents the recall for each size of top-list (percentage of times the correspondent Arabic article is in the top list) for our measures. The figure shows that cosineCM achieves the highest performance, while binCM achieves the lowest performance. However, R10 is perfect for all measures, this is due to the fact that we make retrieval among 100 articles only. This confirms the advantage of VSM for multilingual articles. We also note here that VSM-DIC and VSM-LSI has the same performance due to the reasons described earlier.

Conclusions

In this paper, we introduced comparable corpora extracted from Wikipedia and Euro News in Arabic, French, and English which can be considered as an interesting linguistic resources for statistical machine translation, text summarization, multilingual text mining, information retrieval lexicons extraction. Besides extracting the comparable corpora, we also proposed two comparability measures for the comparable corpora which measure the degree of comparability of multilingual articles. Experimental results showed that our proposed measures are reliable and can capture the comparability degree for our comparable corpora. In the future work, we will improve multilingual documents representation for enable LSI to find semantic relations of words from different languages. we will also investigate statistical machine translation approach to measure comparability. In addition, we will discover opinions across comparable articles in our corpora.

[Fig. 1 .

 1 Fig. 1. The form of interlanguage links of Wikipedia

Fig. 2 .

 2 Fig. 2. The Extracted Wikipedia Comparable Corpora AFEWC

  Fig. 5. CM evaluation on Comparable corpora

Table 1 .

 1 AFEWC and eNews corpora information

			AFEWC			eNews
		English	French	Arabic	English	French	Arabic
	Articles	40290	40290	40290	34442	34442	34442
	Sentences	4.8M	2.7M	1.2M	744K	746K	622K
	Average #sentences/article	119	69	30	21	21	17
	Average #words/article	2266	1435	548	198	200	161
	Words	91.3M	57.8M	22M	6.8M	6.9M	5.5M
	Vocabulary	2.8M	1.9M	1.5M	232K	256K	373K

Table 2 .

 2 Morphology richness for Arabic language

	Arabic word	English meaning	Description
	ktb	to write	the root
	kātb	an author	a name of the subject
	yktb	he writes	the verb
	ktāb	a book	the outcome of the verb
	mktbh	library	the place of the verb (to put the outcome)
	mktb	office	the place of the verb (to write)
	t . yr	to fly	the root
	yt . yr	he flies	the verb
	t . ā' r	a bird	a name of the subject
	t . yār	a pilot	a name of the subject
	t . ā' rh	an air plane	a name of the subject

  are widely used. In the

		Rooting	16%
	Arabic	25% 30% Light Stemming morphAr
	English	Lemmatization Stemming	32% 37%
	0%	20%	40%	60%	80%	100%
	Fig. 3. OOV rate of Word reduction techniques for Arabic and English for parallel corpora

Table 3 .

 3 CM computed for parallel and comparable corpora

	cosineCM


Chiraz Latiri
email: <chiraz.latiri@gnet.

Sma Kamel
email: <kamel.smaili@loria.fr>

Ïli

Caroline Lavecchia
email: <caroline.lavecchia@loria.fr>

Cyrine Nasri
email: <cyrine.nasri@gmail.com>

David Langlois
email: <david.langlois@loria.fr>

Kamel Tn>

Smaïli

Phrase-based Machine Translation based on Text Mining and Statistical Language Modeling Techniques

Keywords: Statistical machine translation, Sequence mining, Inter-lingual triggers, Inter-lingual association rules, Bilingual corpora

In this paper, we introduce two new methods dedicated to phrasebased machine translation. Both are based on mining a parallel corpus in order to find out the couples of linguistic units which are translation of each other. The presented methods do not rely on any alignment in contrast to what is done usually by the statistical machine translation community. Each of them proposes a complete translation table containing translations of single words and phrases. The first method is inspired from the well-known trigger language model while the second one is inspired from the association rules mining technique. All experiments are conducted on a large part of EUROPARL corpus and highlight the utility of both proposed approaches.

INTRODUCTION

Since the apparition of the pioneering work of IBM researchers [START_REF] Brown | The mathematics of statistical machine translation: parameter estimation[END_REF], almost all the proposed papers in Statistical Machine Translation (SMT) are based on their formalism. This is due to the strength of the approach and the availability of tools, such as GIZA++ for producing the translation table [START_REF] Och | Improved statistical alignment models[END_REF], CMU or SRILM for developing language model [START_REF] Rosenfeld | The CMU statistical language modeling toolkit and its use in the 1994 arpa csr evaluation[END_REF] and PHARAO [START_REF] Koehn | PHARAOH: a beam search decoder for phrase-based statistical machine translation models[END_REF] or MOSES [START_REF] Koehn | MOSES: Open source toolkit for statistical machine translation[END_REF] for decoding. These tools make developing SMT a very easy process.

In this paper, we would like to show that it is possible to investigate other issues which could constitute an alternative to IBM methods and their generalization to support phrase-based models [START_REF] Kohen | Statistical phrase-based translation[END_REF]. The proposed methods do not rely on any alignment in contrast to what is done usually by the SMT community. The first method is inspired from the well-known trigger language model which we adapted to automatically learn words and phrases equivalents from bilingual corpora. The second one is inspired from the association rules mining technique, well-known in data mining. We adapt this latter to make it supporting two different languages.

The paper is organized as follows: Section 2 recalls the basic foundations of statistical machine translation. We devote Section 3 to present the machine translation approach based on inter-lingual triggers. Section 4 introduces the second one based on inter-lingual association rules. Then, in Section 5, we present results of the mixture of the two above methods. The conclusion and future works are presented in Section 6.

PRINCIPLE OF STATISTICAL MACHINE TRANSLATION

In SMT framework, the translation process comes back essentially to the search for the most probable sentence f in the target language given a sentence e in the source language. Let e = e 1 , .., e j be the source sentence (i.e., to be translated) and f = f 1 , .., f i be the sentence generated by the translation system, namely:

f = arg max f P (f |e) (1)
By using the Bayes formula, we obtain:

f = arg max f P (f )P (e|f ) (2)
In Equation [START_REF] Och | Improved statistical alignment models[END_REF], P (f ) is estimated by a language model. Its role is to propose a sentence supposed to be correct in the target language. P (e|f ) is computed from a translation model and is supposed to reflect the truthfulness of the translation. Then, the decoder like PHARAO [START_REF] Koehn | PHARAOH: a beam search decoder for phrase-based statistical machine translation models[END_REF] or MOSES [START_REF] Koehn | MOSES: Open source toolkit for statistical machine translation[END_REF] generates the best hypothesis by making a compromise between, at least, these probability distributions.

STATISTICAL MACHINE TRANSLATION USING INTER-LINGUAL

TRIGGERS

The concept of triggers has been largely used in statistical language modeling [START_REF] Rosenfeld | Adaptive statistical language modeling: a maximum entropy approach[END_REF][START_REF] Tillmann | In: Selection criteria for word trigger pairs in language modeling[END_REF]. Roughly speaking, a statistical language model yields a probability to each potential sequence of words belonging to a vocabulary. A trigger model enhances the probability of a list of words which are correlated to a word w i . To develop such a model, all the correlated words are retrieved. Triggers are determined by computing mutual information between two linguistic units x and y, each of them takes its values in the list of words belonging to the vocabulary V . Given two words x, y, the correlation MI(x, y) is given by: MI(x, y) = P (x, y) log 2 P (x, y) P (x)P (y)

P (x, y) is the joint probability between x and y, while P (x) and P (y) are the marginal probabilities of x and y, respectively.

Inter-lingual triggers

In [START_REF] Lavecchia | Using inter-lingual triggers for machine translation[END_REF], the concept of triggers is adapted to handle relationships between words for any two different languages. This approach is called interlingual triggers. An inter-lingual trigger is henceforth a set composed of a word (or a phrase) f in a source language, and its corresponding best correlated words (or phrases) in a target language e 1 , e 2 , . . . , e n . This will be written as:

Trig(f ) -→ e 1 , e 2 , . . . , e n (4)
In inter-lingual triggers f takes its values from the source vocabulary (French) and e i from the target one (English). The translation table is obtained by assigning to each inter-lingual trigger a probability calculated as follows:

∀ f, e i ∈ Trig(f ), P (e i |f ) = MI(e i , f ) ej ∈Trig(f ) MI(e j , f ) (5)
where Trig(f ) is the set of k English linguistic units triggered by the French unit f . In [START_REF] Latiri | Mining monolingual and bilingual corpora[END_REF][START_REF] Lavecchia | Using inter-lingual triggers for machine translation[END_REF], a word-based machine translation using inter-lingual triggers is detailed and results are presented. This approach is extended in the following to achieve a phrase-based machine translation approach.

A new approach to achieve phrase-based MT

Since more than ten years, researches showed that the use of phrases in translation instead of words leads to better SMT system quality. In order to retrieve phrases, several approaches have been proposed in the literature. Most of them require word-based alignments. For instance, Och et al. in [START_REF] Och | An efficient method for determining bilingual word classes[END_REF] collect all phrase pairs that are consistent with the word alignment provided by Brown's models.

In this paper, we will show how to take advantage from inter-lingual triggers and how to make machine translation supporting phrases generated by triggers without any alignment. A sequence of n French words can trigger a sequence of m English words with n, m ∈ N. This kind of correlation is denoted by n.m-Trigger. In the remainder, we will detail the different steps of our approach. PHRASE EXTRACTION To retrieve from a corpus pertinent phrases, we use a method developed in [START_REF] Zitouni | Statistical language modeling based on variable length sequences[END_REF], to rewrite the source training corpus in terms of phrases. To achieve that, an iterative process selects phrases by grouping words which have a high Mutual Information value. Only phrases improving the perplexity are kept for the forthcoming steps. At the end of the process, we get a list of phrases and a source corpus rewritten in terms of these discovered phrases. With this source corpus expressed with pertinent phrases, we aim to find their potential phrase translations in the target corpus by using inter-lingual triggers.

LEARNING PHRASES TRANSLATIONS Since our method does not require any alignment, we assume that each source phrase of l words could be translated by several sequences of l ± ∆l words. This means, to each source phrase, we associate (2∆l + 1) sets of its k best inter-lingual triggers. Each set S i is composed of the potential translations of i words with i ∈ [l -∆l, .., l + ∆l] with l -∆l ≥ 1. Thus, we allow a source phrase to be translated by different target sequences of variable sizes. Table 1 shows the potential translations of the source phrase Porter plainte.

For the cited example, we guess that ∆l is set to 1. Consequently, "porter plainte" could be translated by a sequence of at least one word and at most by a sequence of 3 words. In this example, we have selected 9 potential translations. Obviously, only "press charges" is correct. In the general case, each phrase could be translated by k potential units. That is why we propose to select those which are pertinent and discard the noisy ones. Decrease the temperature and go to line 3 until the given low temperature is reached or until the BLEU stops increasing.

All source phrases and their sets of inter-lingual triggers constitute the set of n.m-Triggers. The main challenge is how to select the best n.m-Triggers. In other words, what are the pertinent phrases and their translations. The choice of the best sub-set phrases is a combinatorial problem. Simulated annealing (SA) algorithm is one of the algorithms which can give a good solution to this kind of problem. We have yet used SA in previous work [START_REF] Smaili | Automatic and manual clustering for large vocabulary speech recognition : A comparative study[END_REF] for automatic word clustering. Basing on this experience, we decided to choose this algorithm among all possible ones to solve our problem. To achieve that, we start with a word-based MT system based on 1.1-Triggers presented in [START_REF] Latiri | Mining monolingual and bilingual corpora[END_REF]. Then, we randomly add phrases (n.m-Triggers) into the translation table until an optimal BLEU score is reached on a development corpus. In other words, we only keep phrases which improve the quality of translation on a development corpus. This method is summarized in Algorithm 1.

For each French unit (a word or a sequence) of l words, we select from the target training corpus its 10 × (l ± ∆l) best inter-lingual triggers (translations). This means that if a sequence of l words is allowed to be translated by l -2, l -1, l, l + 1, l + 2 words, then 50 potential candidates are kept. All this inter-lingual triggers make the set of candidate phrase translations (called n.m-Triggers) required by the SA algorithm.

In the next section, we present another original method which uses association rules between terms in SMT.

MACHINE TRANSLATION WITH INTER-LINGUAL ASSOCIATION

RULES

The association rules mining problem has been introduced by Agrawal et al. [START_REF] Agrawal | Mining association rules between sets of items in large databases[END_REF]. The motivation for searching associations from texts is to discover correlations between terms that occur together as well as to look for regularities in corpora. Before presenting our method, let us give a brief review of the basic definitions related to association rule mining [START_REF] Agrawal | Mining association rules between sets of items in large databases[END_REF].

Definition 1. An extraction context (or corpus, in our case

) is a triplet K = (P, T , R) where:

-P = {s 1 , s 2 , . . . , s n } is a finite set of n distinct sentences of a cor- pus. -T = {t 1 , t 2 , • • • , t m } is a finite set of m distinct terms of a corpus.
-Both sets T and P are linked through a binary relation R such that R ⊆ P × T . That is, each sentence s ∈ P is represented a set of terms m terms T ∈ T named termset, that occur together in the sentence.

The support of X ⊆ T in K, denoted by Supp(X), is the absolute number of a randomly chosen sentences from P containing the termset X. A k-termset T ∈ T , i.e., a termset of length k, is called frequent if the k terms of T occur simultaneously in the corpus more than a user-defined frequency threshold denoted minsupp. Definition 2. An association rule R over K is an implication of the form R : X ⇒ Y , where X and Y are subsets of T , and X ∩ Y = ∅. The termsets X and Y are, respectively, called the premise and the conclusion parts of R.

The support of a rule R and its confidence are defined as:

Supp(R) = Supp(X ∪ Y ) Conf (R) = Supp(X ∪ Y ) Supp(X) (6)
An association rule R is valid if its confidence value is greater than or equal to a user-defined threshold, denoted minconf.

For word-based machine translation, we introduced in [10] the concept of Inter-lingual association rules, named ILAR. The potential translations of a French term f are obtained by selecting all the English terms e 1 , e 2 , . . . , e n which are present in the conclusion of a inter-lingual association rule for which f is its premise.

Since we are interested in phrase-based machine translation, we investigate in the following the problem of mining frequent closed sequences from highly sized bilingual corpora. Our aim is to extend the concept of inter-lingual association rules to the context of phrase-based machine translation.

Mining frequent closed sequences for phrase-based machine translation

Our approach is inspired from an efficient sequential pattern mining algorithm, called BFSM [START_REF] Chang | Efficient sequential pattern mining by breadth-first approach[END_REF]. Our choice of BFSM is argued by the fact that this latter is well adapted for handling very large corpora and, especially for low values of the support threshold. A set of frequent closed sequences is retrieved and then inter-lingual association rules are obtained from this latter as explained further.

We consider the context K = (P, T , R) (cf. Definition 1).

Definition 3.

A sequence S = t 1 , . . . , t j , . . . , t n , such that t k ∈ T and n is its length, is a n-termset for which the position of each term in the sentence is maintained. S is called a n-sequence. To illustrate this concept, let us take an example. Given a dataset of sequences depicted in Table 2 and a value of the minsupp threshold equals to 2. We can get the information positions of the frequent 1-sequences as shown in Table 3. We notice that the term tools is pruned since its support is lower than the minsupp value. For instance, the sequence Statistical machine translation evaluation is a backward super-sequence of the sequence Statistical machine translation since they share the three first terms, while assuming that they have the same support. The second sequence is then considered redundant.

Definition 4. A sequence S α = a 1 , a 2 , . . . , a n is a sub-sequence of S β = b 1 , b 2 , . . . , b m , denoted by S α ⊆ S β , if there is a set of indices 1 ≤ i 1 ≤ i 2 ≤ . . . ≤ i n ≤ m, such that a 1 = b i1 , a 2 = b i2 , . . . , a n = b in . S β is
Thus, the set of the frequent closed sequences, denoted by FCS only contains non-redundant patterns, i.e., those not covered by other ones of the same support (or equivalently, do not have a backward super-sequence, cf. Proposition 1).

AN ALGORITHM FOR DISCOVERING FREQUENT CLOSED SEQUENCES

The main idea of frequent closed sequence mining is based on the principle of sequence-extension [START_REF] Chang | Efficient sequential pattern mining by breadth-first approach[END_REF]. The sequence-extension of a k-sequence S k adds a new term to S k as a new last element. The frequent (k+1)-sequences can be produced by extending the current found frequent k-sequences. Indeed, for each frequent k-sequence S k , we pick up each frequent 2-sequence Sα whose first term is the same as the last term of S k and matches the information position of S k . The result is a new frequent (k + 1)-sequence. Algorithm 2 details how extending a frequent k-sequence by a frequent 2-sequence according to the explained process.

In the algorithm, ⊕ denotes the concatenation operator.

Algorithm 2: Sequence-Extension. Our approach proceeds in four steps, namely:

Step 1: Extraction of frequent 1-sequences and their information positions. We scan the extracted context K once to record the information position (cf. Definition 7) of each distinct term in the corpus.

Step 2: Generation of the frequent 2-sequences. The frequent 2-sequences are produced by applying join operation on the 1-sequences as in the APRIORIlike methods [START_REF] Agrawal | Mining association rules between sets of items in large databases[END_REF]. Note that, we do not need to scan the corpus to count their supports. Indeed, the information positions of the frequent 1-sequences are used to get those of frequent 2-sequences.

Let us consider the two 1-sequences text and mining given in Table 3. The 2-sequence text mining is generated as follows: the pair (1, 2) of mining is matched with the pair (1, 1) of text . By the same way, the pairs (2, 2) and (2, 1) are matched to form the pair (2, 2), and the pairs (4, 2) and (4, 1) to form the pair (4, 2). So, the information position of the 2-sequence text mining are the pairs (1, 2), (2, 2) and (4, 2) (cf. Table 4). Step 3: Generation of the frequent sequences of length greater than 2

We use the frequent 2-sequences to generate frequent sequences of length greater than two. The matching is based on the sequence-extension principle as shown in Algorithm 2.

For instance, the frequent 3-sequence text mining f or can be generated by extending text mining with the 2-sequence mining f or (cf. Table 4).

Thus, longer frequent sequences are iteratively derived starting from the frequent 2-sequences. For our running example, only one 4-sequence is found, which is text mining f or analysis .

Step 4: Pruning step We only retain frequent closed sequences since we look for compact set of term sequences by pruning redundant ones. Our pruning procedure is based on the backward super-sequence condition (cf. Proposition 1) which is tested on each candidate k-frequent sequence. Hence, if a sequence S (k+n) is a backward super-sequence of another sequence S k , i.e., they have the same support, then this latter is pruned from the set FCS. For example, the frequent 3-sequence mining f or analysis is a backward super-sequence of the 2-sequence f or analysis . Therefore, the sequence f or analysis is discarded from the set FCS.

Inter-lingual association rules based on frequent closed sequences

In what follows, we describe the way to derive from FCS the inter-lingual association rules, named ILAR-n-to-m, for phrase-based machine translation.

While considering frequent closed sequences sets S f r and S en , an ILAR is an implication of the form: R : S f r ⇒ Sen such that S f r and Sen are two frequent closed sequences of terms of lengths n and m, respectively. In machine translation, this means that the English sequence Sen is a potential translation of the French sequence S f r . Note that, we keep the same definitions for the support and the confidence of an ILAR as given in Equation [START_REF] Kohen | Statistical phrase-based translation[END_REF].

In order to use inter-lingual association rules in statistical machine translation, we need to assign to each rule R : S f r ⇒ Sen a probability computed as follows:

∀ S f ∈ S f r , Se j ∈ S en , P (Se j |S f ) = Conf (S f ⇒ Se j ) i∈[1...n] Conf (S f =⇒ Se i ) (7)
We present in the next section the experimental evaluation of the two approaches described above in the context of phrase-based machine translation.

EXPERIMENTAL STUDY

All experiments are carried out on a part of the proceedings of the European Parliament EUROPARL [START_REF] Koehn | EUROPARL: A multilingual corpus for evaluation of machine translation[END_REF]. The proposed models have been tested in a whole translation decoding system by using PHARAO decoder [START_REF] Koehn | PHARAOH: a beam search decoder for phrase-based statistical machine translation models[END_REF] and then compared to the performance of state-of-the-art both for word and phrase-based machine translation [START_REF] Och | Discriminative training and maximum entropy models for statistical machine translation[END_REF]. We use the BLEU (BiLingual Evaluation Understudy) score [START_REF] Papineni | BLEU: a method for automatic evaluation of machine translation[END_REF] for evaluation.

Material

We used a French-English parallel corpus of 596831 sentence pairs. Table 5 gives more details about the used parallel corpus EUROPARL. EXPERIMENTAL RESULTS The direction of translation is from English to French. Tests have been achieved on a corpus of 500 sentences. The phrase translation table of the state-of-the-art system, denoted in the remainder by Koehn-Och, is acquired from a word-aligned parallel corpus by extracting all phrasepairs that are consistent with the word alignment [START_REF] Och | Discriminative training and maximum entropy models for statistical machine translation[END_REF].

Our method based on inter-lingual triggers retrieves from the source language a set of 11 212 pertinent phrases which are composed of two or three words, only 8.31% of these phrases occur in the test corpus. This percentage is very low in order to hope to noticeably improve the results.

Table 6 illustrates performances of different systems on both development and test corpora. On the development corpus, the use of pertinent n.m-Triggers improved the results achieved by 1.1-Triggers by 13.27%. For the state-of-the-art methods, the use of phrases increases the performance by 19.90% compared to the word-based method. On the test corpus, both methods improve the results by respectively 5% and 25%. This difference may be explained by the fact that the state-of-art method uses a translation table of more than 21 millions of entries (of one or more words) whereas ours uses only 5.2 millions (where the number of phrases do not exceed 20 000 phrases). Consequently, our translation table has a weak coverage of the training corpus. We should thus increase the size of the translation table in order to get closer values to those of the state-of-art one. By adding 1.71 millions of phrases, we achieve a BLEU result of 34.41. This improvement reduces the gap between our results and the state-of-art one to 2.74, knowing that our translation table is very small in comparison to the one used by Koehn-Och. As illustrated in Table 6, the phrase-based MT system based on ILARs fulfilled a BLEU score of 34.18. So, by adding pertinent frequent closed sequences, we achieved an improvement of more than 12 points in terms of BLEU compared to the initial word-based MT system which only considers ILAR between single terms (22.06). Note that, we do not restrict the length of the generated frequent closed sequences, although derived sequences from EUROPARL may reach a size of 25 terms. We also experimentally observed that beyond a certain length (sequences of 14 terms), the BLEU score does not increase. This could be explained by the fact that these sequences are very rare in the training corpus, i.e., they have a very low support, and their frequency in the test corpus is even lower. Therefore, they can not improve the BLEU score.

TABLE TRANSLATION MIXTURE

In order to take advantage of the two original methods presented above, we decided to combine their two translation tables. Because the n.m-Triggers table does not contain a great number of phrases and since 1.1-Triggers achieved better results than IBM 3 model, we decided to put in a new translation table the word translations got from 1.1-Triggers and phrases obtained by association rules, i.e., ILAR-n-to-m. The result is presented in Table 7. The combination (the line referenced by Comb) outperforms both proposed methods. This result shows that we come closed to the result of Koehn-Och. We are just 1.63 below of the standard method. This illustrates that it is possible to retrieve pertinent phrases and their corresponding translations without any need of alignment which constitutes the advantage of the two original methods presented in this paper. Moreover, Figure 1 illustrates the evolution of the number of phrases in accordance to the number of words per phrase. We can see that the Koehn-Och curve has a cubic form 3 whereas ours has a decreasing power aspect 4 . The Koehn-Och curve grows until 5 and decreases for longer phrases. Whereas in our method the curve sharply decreases with the length of phrases.

Consequently, the gap between our result and the Koehn-Och one could be explained by the fact that the influence of phrases of four and five words would have a real impact. While the number of sequences of 5 words in Koehn-Och method is around 2.8 millions, in our table we got just 10500. Globally, our method produces pertinent phrases, however this number is not sufficient to reach the state-of-art result.

CONCLUSION

In this paper, we proposed two new phrase-based machine translation methods. Each of them proposes a complete translation table containing translations of single words and phrases. The advantage of these methods is their easiness to develop statistical machine translation and more important than that, the fact that our methods, in contrast to what is done by the community, do not need any alignment.

We experimented our approaches on a large part of EUROPARL English-French language pair with a vocabulary of more than 60 000 linguistic units, and we evaluated them by using BLEU measure. Obviously, our methods have been compared to the pioneer ones. The results presented here are very encouraging. They show that it is possible to consider the issue of statistical machine translation differently with the aim to improve the literature results. The advantage of our methods is that the selected phrases are pertinent but their number is not huge. In the future, we plan to improve our methods by making them selecting more sequences without losing the quality of translations.

Input: abegin 2 ifthen 3 S 4 return

 234 k-sequence S k and a 2-sequence Sα.Output: a (k + 1)-sequence 1 last-term(S k ) = first-term(Sα) ∧ id s(S k ) = id s(Sα)∧ pos seq(Sα) = pos seq(S k ) + 1 k+1 = S k ⊕ (Sα\ first-term(Sα)) ; S k+1 ;

Fig. 1 .

 1 Fig. 1. Evolution of the number of phrases in Combtable (top) and Och-Koehn table (bottom).

Table 1 .

 1 Potential translations of the source phrase "porter plainte".

		n.m-Triggers
	Source phrase 2.1	2.2	2.3
	press press charges can press charges
	porter plainte charges can press	not press charges
	easy	not press	you can press
	Algorithm 1: Simulated Annealing (SA) algorithm.
	10

1 begin 2 Start with a high temperature T ; 3 repeat 4 From the current temperature T , state i and a BLEU Bi, randomly add a subset of n.m-Triggers into the translation table which makes the system moving from state i to j. With this new table, we run a decoder. This leads to different hypotheses which are evaluated using BLEU on the development corpus. We get a new BLEU Bj. 5 if Bj -Bi >= 0 then 6 state j is kept as the new current state 7 else 8 j is accepted as the new current state with a probability random(P ) < e B i -B j T with P ∈ [0 . . . 1] 9 until BLEU equilibrium with temperature T is reached;

  Given a sequence S, its information position, denoted by POS S , is a set of pairs (id s, pos seq), where id s represents the rank of the sentence in the corpus and pos seq the position of the last term of the sequence in the sentence.

	Definition 6. A frequent closed sequence (FCS) S is a frequent sequence
	that has no frequent super-sequence S with the same support.
	Definition 7.

called a super-sequence of S α . Definition 5. Given S a sequence discovered from K. The support of S is the number of sentences in P that contain S, i.e., Supp(S) = p ∈ P s.t. S ⊆ p . S is said to be frequent if and only if its support is greater than or equal to the minimum support threshold minsupp.

Table 2 .

 2 Example of a sequences dataset.

	Sentence Sequence
	s1	S1 : text, mining, tools, f or, text
	s2	S2 : text, mining, f or, analysis
	s3	S3 : text, f or, analysis
	s4	S4 : text, mining, f or, analysis

Table 3 .

 3 The frequent 1-sequences.

	1-Sequence Information position	Support
	text	(1 , 1) (1 , 5) (2 , 1) (3 , 1) (4 , 1)	4
	mining (1 , 2) (2 , 2) (4 , 2)	3
	f or	(1 , 4) (2 , 3) (3 , 2) (4 , 3)	4
	analysis (2 , 4) (3 , 3) (4 , 4)	3

Proposition 1. Given two sequences S k and S (k+n) . Then if S (k+n) is a supersequence of S k and if they have the same k first terms and the same support, then S (k+n) is called a backward super-sequence of S k

[START_REF] Chang | Efficient sequential pattern mining by breadth-first approach[END_REF]

.

Table 4 .

 4 The frequent 2-sequences.

	Frequent 2-sequence Information position
	text mining	(1 , 2) (2 , 2) (4 , 2)
	text f or	(1 , 4) (2 , 3) (3 , 2) (4 , 3)
	text analysis (2 , 4) (3 , 3) (4 , 4)
	mining f or	(1 , 4) (2 , 3) (4 , 3)
	mining analysis (2 , 4) (4 , 4)
	f or analysis	(2 , 4) (3 , 3) ( 4 , 4)

Table 5 .

 5 Quantitative description of the used corpus.

			French English
	Train	Sentences	596K
		Words	17.3M 15.8M
		Singletons 26.6K 22.2K
		Vocabulary 77.5K 60.3K
	Development Sentences	1444
		Words	15.0K 14.0K
	Test	Sentences	500
		Words	5.2K 4.9K

Table 6 .

 6 Experimental evaluation in terms of the BLEU score on the development and the test corpora.

		Inter-lingual triggers	ILAR	State of the art
		1.1	n.m	1-to-1 n-to-m IBM model 3 Koehn-Och
	Development 31.02	35.27	19.71 32.66	29.23	35.07
	Test	30.97	32.75	22.06 34.18	29.57	37.15

Table 7 .

 7 Evaluation in terms of BLEU score on test corpus.

	Model	BLEU
	Koehn-Och	37.15
	ILAR-n-to-m 34.18
	n.mTriggers 34.41
	Comb	35.52

Koehn-Och method: y = -28953X 3 + 148491X 2 + 720014X -90643

Our method: y = 1.36E6X-3.6


Eric Descourvières
email: eric.descourvieres@insa-strasbourg.frdgendreau@ens2m.frplutz@ens2m.fr

Stéphane Debricon
email: debricon@lifc.univ-fcomte.fr

Dominique Gendreau

Philippe Lutz

Laurent Philippe
email: laurent.philippe@lifc.univ-fcomte.frbouquet@lifc.univ-fcomte.fr

Fabrice Bouquet

TOWARDS AUTOMATIC CONTROL FOR MICROFACTORIES

Keywords: Microfactory, Manufacturing Execution System (MES), technical information system, Unified Modelling Language (UML)

Microfactories are new specific and flexible systems to produce and assemble micrometric products. These systems are different of standard production platforms because they are confronted with a lot of constraints without influence at human-scale systems. This approach will lead to the development of an architecture of the technical information system adapted to a modular, reconfigurable and evolutionary microfactory. In such a context of production of microproducts, the technical information system is the spinal cord of the microfactory. The definition of the data, their architecture and their organization will build up the base of the control structure. To efficiently and surely control the set of cells, the traditional control scheme must be integrated in a global information model. This paper presents the design of an information model dedicated to microfactories and its advantages.

INTRODUCTION

Microfactories are automated units designed to produce products composed of micrometric components. Today, microfactories are made of elementary modular cells able to carry out basic operations. The size of modules participating to a microfactory is generally greater than the components they are assembling. To perform new operations, few elementary modules may be grouped in a new cell. The realization of one of these cells is still a scientific challenge but several research projects have already get significant results in this domain. These results show very promising functionalities as the ability to configure or reconfigure a cell, by changing an active element for instance. However, the set of operations carried out by a cell is still limited. Now, we need to put several cells together and make them cooperate to produce complex assembled microproducts, as done for mesoscopic productions. The next generation of microfactories will increase ability and complexity more by adding numerous elementary actuators than by developing complex robots. In this context, the control will evolve to become more cooperative and distributed. To achieve this, we have set up collaboration between two laboratories. On one hand, the "Laboratoire d'Automatique de Besançon" has a high level in production engineering and in the practical expertise of handling components at micrometric scale. Therefore this laboratory has defined the schedule of conditions of an adapted microfactory and its corresponding model. On an other hand, the "Laboratoire d'Informatique de l'Université de Franche-Comté" has an expertise in the modelling field, in model checking, in scheduling activities and distributed systems, for execution in microfactories. This paper presents the information model of multi-cells microfactories. The model is expressed with the Unified Modelling Language (UML) and is used as connecting link between the robotic and computing researchers. The context of our model is the Manufacturing Execution System (MES) level of the Computer Integrated Manufacturing (CIM) pyramid. In a first part we make precise the micro-factory context and its properties. Then, we present the information model in the third part and justify its use in the forth part.

SPECIFICITIES OF THE MICROFACTORIES

The organization of several productions into a subcontracting workshop leads to set up a flow management and a stock management because the big weight machines are established for a long time. At the opposite, microfactories allow to manufacture only one type of product, in the same time, and we can move the cells to an optimal place, in goal to facilitate the movement of the components. The consequence is to minimize the scheduling problem. However, one of the specificities of microfactory is the impossibility for the operator to have a direct (manual) access inside the celles. We have to introduce a new kind of data support. For example, to support the production followup and experimental tele-operation, the technical information system must manage important data flows (control of robots with great degrees of freedom, feedback with threedimensional real time videos, etc). In addition, the unusual indeterminist behaviour of components at this scale [START_REF] Régnier | Forces Analysis for Micromanipulation[END_REF] led us to integrate an assistance to the operator starting from a permanent management of gradually acquired knowledge. Finally, the feeding and transport of microcomponents between cells are not easy, because the problem of the position and the orientation become more difficult with a status of ultra precision. Moreover, usual linear production lines will become, in microworld, a three dimensional organization of the manufacture. Moreover, as the production takes place in a controlled environment with modularity and light weight cells, we can organize the microfactory by tuning the position and the orientation of each cell. This approach will lead to the development of modular, reconfigurable and evolutionary microfactories, as described in fig. 1. However the design of such microfactories is complex at the cell level as well as at the information system level. Due to the computing power needed to control a cell, we consider that several computers will be mandatory to achieve an automatic control of the production platform. In such a context of production of micro-products, the technical information system is the spinal cord of the microfactory due to the distributed aspect of the computing and scheduling functions. The control structure, i.e. the definition of the data, their architecture and their organization, must be clearly defined to ease the interactions between several cells and the global management. Moreover, as the complexity of the control increase with the distribution of the control and the scheduling information, it is necessary to use software engineering techniques to ensure the quality of the information model. The object of our study relates to microfactories able to produce successively various types of microproducts into small or average quantity, for which the development and the installation of a production dedicated line are not financially profitable. For this reason, we work with a modular, flexible and evolutionary architecture which make it possible to have a reconfigurable and reorganizable production equipment [START_REF] Descourvières | Specifications of technical information system dedicated to a re-organizable and reconfigurable microfactory[END_REF].

INFORMATION MODEL

We have developed the information model on the level of the MES activity (Manufacturing Execution Systems) of the technical information system of the decision model of the CIM (Computer Integrated Manufacturing) pyramid [START_REF]Computer Integrated Manufacturing. Systems Architecture. Framework for Enterprise Modeling[END_REF], [START_REF]CIMOSA: Open System Architecture for CIM (Paperback)[END_REF], as you can seen in the fig. 2. Fig. 2: context of CIM model Among the eleven functions of the MES system indexed by the ISA-95 [5], six are concerned with our framework of application: scheduling of the tasks, management and piloting of the processes, collection and data acquisition, feeding of the products and the batches, analysis and optimization of the performances, stock management, the statute and the allocation of the resources. To satisfy all these functions, this approach leads to a very dynamic technical information system ready to use tools of representation, filing, handling, exploitation, and communication of information. All the data of production and the blaming of the process will be necessary to sort and capitalize in order to extract some know-how in the microfactory or more specifically in a cell [START_REF] Descourvières | Data representation for the control of full-automated microfactories[END_REF]. The UML case diagram in the fig. 3 represented the various cases of uses for the operator who organizes the production. The modularity is defined into the cell. In first, the plateform of production has a basic cell store at one's disposal; a basic cell is composed of a physical structure to connect different type of equipment, as active elements (actuators, sensors, …) and passive elements (fingers, tools, …); each element is parameterize with its position, its action and its environment. An assembly cell is so composed (fig. 4) with some grippers to move and to put on position, a laser source to polymerize glue during the assembly operation, an optical sensor to control the precise moving of the object. Fig. 4: example of an assembly cell Our concept of microfactory is based on the design of cells of production. Each "basic cell" is the subject of thorough design engineering and research [START_REF] Rakotondrabe | Step Modelling of a High Precision 2DoF (Linear-Angular) microsystem[END_REF]. It has micro-actuators and/or microsensors arranged between them in a precise way to allow specific functionalities. The cell comprises parameters of inputs and outputs naturally accessible from the organization system and the operator. As far as possible, modeling of the behavior of the cell is provided with the cell in particular to allow its simulation. The models are enrich progressively with the acquisition of new knowledge resulting from the use the cell. In addition, if needed such a basic cell will be modified by the addition of active elements and/or passive elements. We define a "active element" as a body having input(s)-ouput(s) (example: sensor, or actuator), and a "passive element" as a body not having input-output (a simple tool). This modularity obtained by active or passive elements is illustrated under UML by the following diagrams (fig. 5). Each cell used for the implementation of a task of the assembly plan is initially the subject of a simulation and/or an experimentation in order to validate its parameter setting. After the validation of the use of various cells, the initial organization of the production is defined while being based on multiple criteria coming from the specifications of the production. The organization of the obtained cells form the Platform of the Production of the Microproducts (abbreviation PFPM). Contrary to the often linear lines of production of the mesoworld, the PFPM can be spread while following two dimensions (organization in matrix, triangular, circular, etc), or even in three dimensions (organization cubic, spherical, cylindrical, conical, pyramidal, by layers, etc). The architecture of the PFPM is articulated around three standard bodies of connection being used as interface of communication, energy and products. Three flows corresponding are based, according to cases, on a material or immaterial support. If need be, the PFPM has a specific environment (clean room, antivibratory enclosure, fluid environment, black room, etc).

Fig. 5: examples of microfactory class diagram

Lastly, the definition of an advanced "human machine interface" must amongst other things make it possible to make available the microworld to the operator for the observation, or the remote-control.

WHY USING A MODEL?

The modelling of the microfactory focus on three topics: the factory design and behaviour, the factory components and interactions between those components. Models can be used for multiple activities (to simulate normal or alternative behaviours, to validate tasks scheduling, to check intrinsic properties, to cope with functional complexity) but any model has a cost. It should be justify by its feedback to the physical level [START_REF] Selic | Using UML for modeling Complex Real Time Systems[END_REF]. The first benefit of a formal model is the ability to provide a non ambiguous definition for every elements present in the system to be modelled. But that's not enough and starting from the conception phase we must use several kinds of model to validate microfactory properties. We must focus on the specific goals associated with an activity to reduce the modelling complexity. Typically, for the microfactory, model should gather information on architecture validation, system organization and functional description. Those several activities will produce a possibly large model. To manage it, we introduce a concept called View. A view is the projection of a model for a specific activity. We use this concept to ensure coherence between microfactory activities. Furthermore, microfactories deal with really small parts that are sensible to their environment. This specificity leads us to model this environment and its interaction with the microfactory. This approach is known as dedicated model to be linked with works on DSM (Domain Specific Models). We have a view dedicated to functional specifications of the microfactory. This view should help us to verify and to validate a set of properties applicable to the factory. We want to be able to confront a factory's model to its realization by: Animating behaviours described in the system's model.

In that case, we will be able to capture actions and reactions of the system even before realization.

Validating a realization of the system. There, we will compare the realization with the model, by applying a same action on both and check the result. A script of actions can be compute by automatic test generation [START_REF] Pickin | Using UML sequence Diagrams as the Basis for a Formal Test description Language, Integrated Formal Methods, 4th international conference[END_REF]. Checking that intrinsic properties of the system are present in the model. First, we have to formulate hypothesis on the environment and we must identify intrinsic properties of the system. Those hypothesis and properties should be verified on the model. And in a second time we must be sure that they are respected or present in the realization of the system, i.e., the microfactory [START_REF] Riechters | A precise approach to validating UML Models and OCL Constraints[END_REF]. After verifying the properties of the system, the model may be used as a base to the design of the information management architecture of the physical micro factory. For instance, as the interactions between the factory components are identified in the model, this information may be used to realize the communication network between the cells and the controlling components. The model may also be used in the development of tools helping engineers during the design phase of a microfactory: simulators, automatic generation of the cells command, etc. In all these cases, the model will serve as a reference for the factory architecture and, thus, guarantee the coherency of all the works done.

CONCLUSION

The approach towards automatic control for microfactories needs the data representation. The specificities of these production system lead to a development of a new method to organise and to optimise the functionalities. In such a context of production of microproducts, the technical information system is the spinal cord of the microfactory

Fig. 1 :

 1 Fig.1: Microfactory structure

Fig. 3 :

 3 Fig. 3: UML use case diagram