3.2.1. The preprocessing part

- The MANTEX module <Conceptual diagram>: Text --> MANTEX --> term candidates --> term validation (automatic) (interactive) Many term acquisition tools (Enguehard, 1992) , (Smadja, 1993) , (Bourrigault and Condamines, 1995) , (Justeson and Katz, 1995) (Reinert, 1995) use different methods to perform term acquisition and linguistic knowledge structuring. For instance, Smadja uses statistical methods to collect relevant pairs of co-occuring words, which are then used to reconstruct n-word collocations (called n-grams). Our approach to term acquisition uses repeated word sequences and word distributions to help locate meaningful entities in text. Repeated word sequences (Lebart and Salem, 1994) are n-word strings (n > 1) occurring at least twice in the text (strings containing punctuation are not considered). The semantic hypothesis behind repeated word sequences is based on Harris (Harris, 1968) . According to Harris, language offers discrete and arbitrary linguistic units which may be clustered according to linguistic constraints (i.e., terms are made up of linguistic units chosen to cluster according to linguistic constraints). MANTEX generates a list of term candidates which may be validated by a terminologist. - The TSTRUCT module <Conceptual diagram>: Terms --> TSTRUCT module --> term tree (automatic) The TSTRUCT module produces a structured list of terms: generally terms can occur in texts with different extensions, the main term can be seen as the head of a tree and extensions as branches. For instance, in French, the first noun of a noun phrase usually expresses the main concept, while the rest expresses specification. While our system has specifically addressed only French terminology we think it can easily be transported to other languages. For instance we are working on extracting terms from a genetic engineering corpus in English. For instance, in "DNA molecules" the main concept is "DNA" and "molecules" is used to specify the main concept. example of a term tree:
(DNA) ! -------------------------------------------------- ! ! ! ! (molecules) (markers) (sequences) (strands) In English the problem is that the main noun in a noun phrase is often the last, as in (DNA STRANDS) but may sometimes be the first as in (STRANDS OF DNA). - The VERB module <Conceptual diagram>: Text, morphological rules --> VERB --> verbs (automatic) To build a list of domain verbs we use a set of morphological rules: Let (L) = (e, ait, aient, ant, er) be a list of verb endings. If a word (w) ends with a member (m) of (L) then our system supposes the string consisting of (w) without (m) is a radical candidate. If this candidate is also found with another member of (L) then it is retained as a verb (for example, "montr(ent)" and "montr(ait)" --> the verb is :"montr(er)").

3.2.1. The query part

- The LEX module <Conceptual diagram>: Text, terms, categories, formula --> LEX --> contexts (automatic) The LEX module handles lexical (words, terms, categories ...) and morphological data (the different forms of a verb, terms in a singular/plural form...). - A personalized dictionary The linguist may use a personalized dictionary which contains a few predefined linguistic categories such as: art: for article term: for term, etc.. In addition, he may interact with the system to create new categories. For instance, he may create the category: det (as determinant) which may refer to the following linguistic categories: article (a, an, the), possessive adjectives (his, her, ...). The following table resumes the predefined categories:

category	explanation	examples
abr	abbreviation	"DNA"
prep	preposition	to, for, ...
art	article	a, the, ...
artd	definite article	the
arti	indefinite article	a, an
term	domain term
period	!.:;? ...
pon	other punctuations
comma	,
verb	a verb
pron	pronoun	he, she, ...
text	a string of text

- The LEXICA formula The LEXICA formula is a list of constraints: C1, .., Cn each constraint of the formula may be a variable, a string of text, or linguistic category. Variables and categories may be followed by a list of logical predicates (a Lisp like form). For instance, the user may specifie that a constraint is the verb "to contain": verb/(="contain"). Two parts of the system have to be differentiated: - the local constraints (described above) - the global constraints: they are specified at the end of the formula (after the sign "&") and they are applied to the whole formula. For instance the user executes the formula art+x+verb+y and may describe that the number of the contexts to be found is equal to the value1. After adding the global constraints the formula is now: art+x+verb+y & (context= 1) In order to express local and global constraints, we developed a set of predefined predicates which may be used in a formula, for instance:

predicates	examples
=	x/(="contain")
	;; true if the value of x is equal to the
	word "contain"
in	x/(in list-of-words)
	;; true if x belongs to the list-of-words
context=	(context> 1)
contex<	;; checks the number of contexts to be found
contex>

- Syntax of the formula:

<symb>	::=	a Lisp symbol
<symbp>	::=	a predifined Lisp symbol
<nombre>	::=	{0|1|2|3|4|5|6|7|8|9}+
<string>	::=	a string of text
<variable>	::=	{<symb>}
<category>	::=	{<symbp>}
<element>	::=	<string> | <variable> | <category>
<formula>	::=	{<element>}*
<predicat>	::=	(<symb> {<element>}*)
<condition>	::=	({<predicat>}) | {<operator> {<predicat>}+}*
<selection>	::=	{/<condition>}
<contraint>	::=	(<element> <selection>)
<operator>	::=	and | or | not

Each symbol handled by a formula is a Lisp symbol. In order to separate the constraints we use the sign "+". To express the global constraints we use the sign "&" at the end of the formula followed by a list of predicates. A <constraint> of a formula may be described by an <element> followed by a selection (the sign "/") of a condition which is a a predicate or a list of predicates connected by a Lisp <operator>.