A(.q'ES DE COLING-92, NANTES, 23-28 hOtrV 1992 9 9 3 Automatic analysis by phrase-structure grammar is time COtlsuming. The need for fast procedures leads to grammar epresentations that are less powerful but easier to handle than general unification procedures. Pereira and Wright 1991 and Rimon and Herz 1991 proposed such approaches, that is, algorithms that perform the construction of a finite-state automaton approximation of a phrase-structure grammar, These automata are then used as simple checkers of well-formed patterns. However, parsing a sentence and only providing the information that it does (or doesn't) match the automaton description is not sufficient. One should provide (see K. Koskenniemi 19901 the readings of the text that respect exactly the constraints.We propose here an algorithm that provide all these readings. Moreover, the autonlatou of a given text can be highly ambiguous, and in order to increase its adequacy (e.g. to study given syntactic patterns), we may want to customize it. To achieve such a result, we construct automata that eliminate paths irrelevant o the given study I. Once this operation was performed, significant patterns (like Noun Adjective in French) can be extracted, Technical terms in many domains take the form of sequences uch as Noun Adjective, Noun de Noun etc. Their recognition thus leads to an efficient indexation process. This is a complementary approach to statistical treatments like those presented in K.Church, W. Gale, P. Hanks, D. Hindle 1989 or in N. Calzolari and R. Binzi 1991. Moreover, we use Finite-State Automata (FSA) at all stages of the process: for dictionary consultation, for disambiguation and for the final extraction process. This allowed the experiments to be done on-line starting with untagged corpora. One of the crucial points is that tagged text should be represented by FSA in order to be disambiguated ( isambiguated texts are already in this form in Rimon and Herz 1991 and K. Koskenniemi 1990). FSA representation for ambiguities representation is not a new approach but in our contribution, we PROC. OF COLING-92, NANTES, AUG. 23-28, 1992 systematized it for different types of ambiguities, namely: 1. Morphological features ambiguity (gender for instance), 2. Part of speech ambiguity, 3. Phrase ambiguity (compound v.s. sequence of simple words). Let us take, for instance, the French sequence (1).. le passe... Both words are ambiguous, le can either be an article (the) or a pronoun (it, him or her) and passe can either be a noun or a verb. Moreover the noun passe is still ambiguous, since it can mean either a pass key (and is then masculine) or a pass (like in a forward pass, it is then feminine). The verb form passe, ill turn, is ambiguous, it is a conjugated form of the canonical form passer (to pass) in one of the three tenses: indicative present, subjunctive present or imperative present. For the first two tenses, it can either be in the first or in tile third singular person and, for the latter, it has to be in the second person of singular. The problem is the following: the consultation of the simple form dictionary DELAF 2 (600.000 entries) first provides a sequence tagged as follows: le (pronoun, article) passe(noun-ms, noun-fs, verb-P3s:S3s:Pls:Sls:Y2s) (where the abbreviations are m: masculine, s:singular, 3: third person, P: present, S: subjunctive, Y: imperative) The compound form dictionary DELACF 3 (150.000 entries) is used, it marks sequences like pomme de terre (potato) as frozen. In a second step, we provide the automaton representation of figure 1, to be read in the following way: The first word is either a pronoun or an "article, its spelling is le, the second word is either a singular noun (ambiguous: one meaning is masculine (the pass key) and tile other feminine (the forward pass)) or else a verb conjugated at the persons, tenses and numbers pecified above. ACRES DE COLING-92, NANTES, 23-28 AOt~T 1992 9 9 4 pronoun ~ passe O Figure 1 On the other hand grammar rules provide constraints which can be described as forbidden sequences. In our example, since the clitic sequence is highly constrained (M. Gross 1968), the pronoun le can be followed either by another pronoun or by a verb. The article le cannot be followed by a verb or by a feminine noun (except for parts of compounds). This set of forbidden sequences is described by the automaton of figure 2. Figure 2. Thus the FSA representing the text according to the roles should be the FSA of figure 3. Figure 3. The problem consists in constructing the automaton of figure 3 given those of figures 1 The reader probably noticed that file rules were described as a set of forbidden sequences, which is unusual. The formal operation and the algorithm are easier to describe with negatively defined rules, it is the reason why we use this device here. However, given the grammar corresponding to the automaton representation, the procedure is equivalent to a set of rules expressed in a positive, and hence more usual way. 4.1 Formal description of the problem. The problem, informally described, can easily be specified in the following way: PROC. OF COLING-92. NANTES, AUG. 23-28. 1992 Given a text, its FSA representation (e.g. figure 1) AI is defined by the 5-tuple (Alph,QL,il,Fl,dl) which respectively denotes its alphabet, its state set, its starting state, its final state set and its transition function 4which maps (Ql*Alph) into Q1. Moreover, A1 has the property of being acyclic (it is a Directed Acyclic Graph (DAG)). The constraints are represented by the FSA A2, defined in the same way by (Alph,Q2,i2,f2,d2). These automata define respectively the regular languages LI=L(AI) (i.e. the language accepted by A1) and L2=L(A2) (i.e. the language accepted by A2) • Since L2 describes the set of sequences (or factors) forbidden in any word of L1, if A describes the text after the filtering, this means that L=L(A) follows the condition L = L1 \Alph* L2 Alph* This operation on languages will be called factor subtraction and will be noted L=L1 f- L2. At this point, we can define the related operation on automata: if LI=L(A1) and L2=L(A2) we say that A is the factor subtraction of A1 and A2 and note it A= A1 fA2 if L=L1 f- A2 and L=L(A). 4.2 Informal description of the algorithm We will first apply the algorithm on a small example. Suppose that A1 is the automaton represented in figure 4, that A2 is the automaton represented infigure 5 and that we want to compute AI f- A2. c Figure 4 c Figure 5 Each state of the automaton A=At f- A2 will be labelled with a state of A 1 and a set of states of A2 (i.e a member of the power set of Q2). More concretely the automaton A=A 1 f- A2 of figure 6 is built in the following way: The initial state is labelled (0,{0}), the first 0 refers to the state 0 of A1 (01 for short). The letter a leads, from 01 to the state 11 of AI but to nothing in A2, we construct the state AcrEs DE COLING-92, NANTES, 23-28 Ao(rr 1992 9 9 S (1,{0}) which means that, for a, 0 leads to 1 in At but that {0} leads to nothing (the empty set) in A2 to which we systematically add the initial state. On the other hand, d2({0},b)={ 1 } to which we add, as for a, the state 0; thus, in A, d((0,{O}),b) = (dl(O,b), {O,d2(0,b)})= (1,{0,1}). For each state being constructed, we list file states it could refer to in A2 and, for each of these states, their image by the letter being considered. A specific configuration is when the state of A being considered has one of his label that leads to the final state of A2, it means that a complete sequence of A2 has been recognized and should then be deleted. This is the case if we look at state (2,{0,1,2}) in A: d2({0,1,2},b)=\[1,2,3} where 3 is final, thus it has no m msition for b, which leads to delete the path bbb forbidden by A2. Figure 6 The following algorithm computes A1 f- A2 l.f\[0l=(il,{i2/) 3.n=l; 4.F;~; 5.do 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 22. 23.while (q<n) PRO(:.. oF COLING-92, NANTES, Ant. 23-28, 1992 (xt,XO=tIql; G={i2}; for each s e Alph so that dl(Xl,S)*O yl=dl(Xl,S); for each x'c X2

if d2(x',s)=f 2

G=O; goto 8;

cl~

G=G U {d2(x',s)}; elglfor if 3q'<=(n-1) so that flq'\]=(yl.G)

d(q,s)=q' else tln\]=(yl,G);d(q,s)=n; n+=l

if Yl E F l then F=F U {n}; cndfoc q+=l; 5. Experimental results Given a syntactic pattern (fL,'st Noun followed by an Adjective) and a text, we can detect its occurrences. We can search the text without applying constraints, this provides output 1 (figures 7 and 8).We can also search it after having applied constraints (output 2). We shall compare both outputs. For instance, for the sentence: L'individu n'y est pas perfu comme une valeur abstraite et universelle, mais comme un ~tre concret, comme le membre d'un ensemble particulier, Iocalisd et qui n'eJdste que darts son rapport d cet ensemble. the program provides the following matchings: (t) (2) y est valeur abstraite valeur abstraite 6tre concret ~tre concret ensemble particulier dun ensemble particulier Figure 7 DELAF Simple words dictionary 570.000 entries v I esen ti°n I-'--->i I F a pr° V n° °n I-->Q after constraints Figure 8 \] \[DELACF

\[compound word \[dictionary \[ 150.000 entries The program runs in three steps (figure 8). It first takes a text and tags it according to the two dictionaries. The text is then transformed into its FSA representation on which the constraints are applied. Given a pattern (Noun followed by an Adjective), we compare its number of ocuurences in both outputs 1 and 2. This will give us a measure of the power of the filtering. It is worthwhile to point out that the experiments were realized on untagged corpora, namely that the duration of the tagging process is included in the figures given in the tables. These experiments were done on personal computers5 and it can be seen that the ACRES DE COLING-92, NANTES, 23-28 AO~'T 1992 9 9 6 time spent is low enough to permit on-line use (for compound word enrichment for instance). 5.1 Searching Noun-Adjective patterns First, let us consider the pattern Noun Adjective (which is approximately equivalent to the English sequence adjective-noun). We first tried to search each contiguous pair of words whose first element was labelled as a noun and whose second element was an adjective. This provides the result of the first line of figure 9. The first filtering uses the fact that, in French, the word and the adjective have to agree on their gender and on their number. This gives, for the same texts, the results of the second line. Third we applied the algorithm described above as a second filter, this leads to the results of the third line. Editorial Article Novel Corpus 4185 13010b. 369292b 1738115 bytes (4p)

/1 pa~e) NAdj 66 227

15" 40" NAdj 56 198 a~eemen 15" 40" t NAdj 13 40 agreem.+ 20" 50" constr. NAdj 10 134 real number 6 Figure 97 The texts ,are in the form of ASCII files. The first one was a magazine editorial of about 1 page 8, the second one is an article of about 4 pages 8. The third one is a novel of the French 19th century writer Jules Verne: Les aventures du docteur Ox. The fourth one is a compilation of texts with a large amount of law texts. We gave, in the last line, the number of patterns that should have been detected if the filtering had been perfect; this was done by hand. 6. Conclusion PROC. OF COLING-92, NANTES, AUr. 2.3-28, 1992 (100p). 3334 19' 2651 19' 1277 41' 1150 31532 Ih25 28234 lh25 11125 3h05 not counted These experiments will be expanded on a larger amount of patterns and on various types of corpora, but we already think that those we presented here show that the method can actually be used as a practical tool for easing the construction of terminological lists.

Aho, Alfred V., John E. Hopcroft, Jeffrey D. Ullman, 1974. Tile Design and Analysis of Computer Algorithms. Addison Wesley, 467p. Calzolari, Nicoletta, Remo Bindi, 1990. Acquisition of Lexical Information from a Large Textual Italian Corpus. Coling 90, Proceedings of the Conference. Helsinki. Church, Kenneth, William Gale, Patrick Hanks, Donald Hindle, 1989. Parsing, Word Associations and Typical Predicate-Argument Relations. Internal report. Bell Laboratories, Murray Hill. Courtois, Blandine, 1984, 1989. DELAS: Dictionnaire EIectronique du LADL pour les roots simples du franqais, Paris: Rapport technique du LADL, Universit6 Paris 7. Gross, Maurice. 1968 Grammaire transformationnelle dufranqais, 1-Syntaxe du verbe. Cantil~ne, Paris, 183p. Koskenniemi, Kimmo, 1990. Finite-State Parsing and Disambiguation. Coling-90. Proceedings of the conference. Helsinki. Peireira, Fernando C.N., Rebecca N. Wright. 1991. Finite state approximation of phrase structure grammars, 29th Meeting of the A.C.L, Proceedings of the conference. University of California, Berkeley. Revuz Dominique, 1991. Dictionnaires et lexiques, m6thodes et algorithmes. PhD dissertation. Universit6 Paris 7, Paris, 130p. Rimon, Mori, Jacky Herz, 1991. The recognition capacity of local syntactic constraints. Fifth Conference of European Chapter of Association for Computational Linguistics. Proceedings of the Conference, Berlin. Silberztein Max, 1989. Dictionnaires 61ectroniques et reconnaissance lexicale ACRES DE COLING-92. NANrt'ES. 23-28 Ao(rr 1992 997 automatique. PhD dissertation, Universit6 Paris VII, Paris, 175p. 8. Annexe Some constraints We present here a sample of constraints as they are actually implemented. The following set of sequences represents paths that have to be deleted from the FSA representing the text. This set is to be compiled into a FSA before being used by the program. The ?? word means that it can be any transition of the state it is cmnpared to. \[:or instance the comparison of?? and the character a gives a as result. It leaves empty the parameters tltat are specific to the words being matched (i.e. the word itself or its canonical form). /il/unitllex/n/ /il/unit/lerdvP?l??/??/v-t/??/1/??/ /il/unitllex/vl??/'!?fl?/v-t/??/2/??/ hllunithex/v/??l??l??/v-t,t??/3/P/ hl/unitllex/pre/ne/unit/lex/v/??f??f??/v-tl??/lP. ?/ /illunitllex/pre/nelunitllex/vP. ?P. ?P. ?/v-tl??12P?/ /illunitllex/pre/ne/unil/lex/v/??P?l?. /wtl?. ?/3/P/ /il/unit/lerdprolle/unitllerdvl??f!?f~. ?/v-t/??ll/??/ /il/unilllex/pro/le/unitllex/v/??f??/??/v-tl??/2P. ?/ /il/unitllexlpro/le/unitllex/v/??f??f??/v-tf??131P/ /il/unit/lex/v/?. ?f~. ?/??/v-tP?/lf??/ /il/unitllex/v/'!?f??/??/v-tl?. /2\[??/ /il/unit/lex/vr??/??/??/v-tf??/3/P/ /il/unit/lex/det/ /il/unit/lex/adj/ Pnoc. OF COLING-92. NANTES, AUG. 23-28, 1992