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<Paper uid="C92-3153">
  <Title>TEXT DISAMBIGUATION BY FINITE STATE AUTOMATA, AN ALGORITHM AND EXPERIMENTS ON CORPORA</Title>
  <Section position="1" start_page="0" end_page="0" type="metho">
    <SectionTitle>
TEXT DISAMBIGUATION BY FINITE STATE AUTOMATA,
AN ALGORITHM AND EXPERIMENTS ON CORPORA
</SectionTitle>
    <Paragraph position="0"/>
  </Section>
  <Section position="2" start_page="0" end_page="0" type="metho">
    <SectionTitle>
1. Abstract*
</SectionTitle>
    <Paragraph position="0"> Consulting a dictionary for the words of a given text provides multiple solutions, that is, ambiguities; thus, the sequence of words pilot studies could lead for example to: pilot: N singular, V infinitive, V (conjugated) studies: N plural, V (conjugated) pilot studies: N plural (compound).</Paragraph>
    <Paragraph position="1"> These informations could be organized in the form of a finite automaton such as: pilot studies N plural  |&amp;quot;'&amp;quot; (compound) | The exploration of the context should provide clues that eliminate the non-relevant solutions. For this purpose we use local grammar constraints represented by finite automata. We have designed and implemented an algorithm which performs this task by using a large variety of linguistic constraints. Both the texts and the rules (or constraints) are represented in the same formalism, that is finite automata.</Paragraph>
    <Paragraph position="2"> Performing subtraction operations between text automata and constraint automata reduce the ambiguities. Experiments were performed on French texts with large scale dictionaries (one dictionary of 600.000 simple inflected forms and one dictionary of 150.000 inflected compounds). Syntactic patterns represented by automata, including shapes of compound nouns such as Noun followed by an Adjective (in gender-number agreement) (Cf 5. I), can be matched in texts.</Paragraph>
    <Paragraph position="3"> This process is thus an extension of the classic matching procedures because of the on-line dictionary consultation and because of the grammar constraints. It provides a simple and efficient indexing tool.</Paragraph>
  </Section>
  <Section position="3" start_page="0" end_page="0" type="metho">
    <SectionTitle>
2. Motivation
</SectionTitle>
    <Paragraph position="0"> * This work was supported by DRET and Ecole Polytechnique.</Paragraph>
    <Paragraph position="1"> ** Universit(~ Marne la Vallre. Institut Gaspard Monge. 2 Allre Jean Renoir. 93160 Noisy le Grand. France eroche@ladl.jussieu.fr Universit6 Paris 7 Automatic analysis by phrase-structure grammar is time COtlsuming. The need for fast procedures leads to grammar representations 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.</Paragraph>
    <Paragraph position="2"> 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.</Paragraph>
    <Paragraph position="3"> 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 to 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 such 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.</Paragraph>
    <Paragraph position="4"> Calzolari and R. Binzi 1991.</Paragraph>
    <Paragraph position="5"> 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.</Paragraph>
    <Paragraph position="6"> One of the crucial points is that tagged text should be represented by FSA in order to be disambiguated (disambiguated texts are already in this form in Rimon and Herz 1991 and K.</Paragraph>
    <Paragraph position="7"> Koskenniemi 1990). FSA representation for ambiguities representation is not a new approach but in our contribution, we  solutions.</Paragraph>
    <Paragraph position="8"> A(.q'ES DE COLING-92, NANTES, 23-28 hOtrV 1992 9 9 3 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).</Paragraph>
    <Paragraph position="9"> 3. Presentation of an example  Let us take, for instance, the French sequence (1).. le passe...</Paragraph>
    <Paragraph position="10"> 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.</Paragraph>
    <Paragraph position="11"> 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 &amp;quot;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 specified above. 2DELAF: LADL's inflected forms dictionary for simple words: B. Courmis 1984,1989.</Paragraph>
    <Paragraph position="12"> 3DELACF: LADL's inflected forms dictionary for compound words: M. Silbcrztein 1989.</Paragraph>
    <Paragraph position="13"> 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.</Paragraph>
    <Paragraph position="14"> Figure 2.</Paragraph>
    <Paragraph position="15"> Thus the FSA representing the text according to the roles should be the FSA of figure 3.</Paragraph>
    <Paragraph position="16"> Figure 3.</Paragraph>
    <Paragraph position="17"> 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.</Paragraph>
  </Section>
  <Section position="4" start_page="0" end_page="0" type="metho">
    <SectionTitle>
4. The algorithm
</SectionTitle>
    <Paragraph position="0"/>
    <Section position="1" start_page="0" end_page="0" type="sub_section">
      <SectionTitle>
4.1 Formal description of the problem.
</SectionTitle>
      <Paragraph position="0"> The problem, informally described, can easily be specified in the following way: ACRES DE COLING-92, NANTES, 23-28 AOt~T 1992 9 9 4 PROC. OF COLING-92. NANTES, AUG. 23-28. 1992 Given a text, its FSA representation (e.g.</Paragraph>
      <Paragraph position="1"> 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 4 which 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 f-</Paragraph>
      <Paragraph position="3"/>
    </Section>
    <Section position="2" start_page="0" end_page="0" type="sub_section">
      <SectionTitle>
4.2 Informal description of the
</SectionTitle>
      <Paragraph position="0"> 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 in figure 5 and that we want to compute AI f- A2.</Paragraph>
      <Paragraph position="2"> 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 4The automata are assumed to be detotministic, which is not an additional constraint since one can determinize them (see Aho, HoperopfL Huffman 1974 for instance). (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)=\[1,2,3} where 3 is final, thus it has no m msition for b, which leads to delete the path bbb forbidden by A2.</Paragraph>
      <Paragraph position="3"> Figure 6 The following algorithm computes A1 f- A2</Paragraph>
      <Paragraph position="5"> 6. (xt,XO=tIql; 7. G={i2}; 8. for each s e Alph so that dl(Xl,S)*O 9. yl=dl(Xl,S); 10. for each x'c X2 11. if d2(x',s)=f 2 12. G=O; goto 8; 13. cl~ 14. G=G U {d2(x',s)}; 15. elglfor 16. if 3q'&lt;=(n-1) so that flq'\]=(yl.G) 17. d(q,s)=q' 18. else 19. tln\]=(yl,G);d(q,s)=n; n+=l; 20. if Yl E F l then F=F U {n}; 21. cndfoc 22. q+=l;  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:</Paragraph>
      <Paragraph position="7"> 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.</Paragraph>
      <Paragraph position="8"> 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 5Experiments were done with an IBM PS2 386 25Mhz with an OS/2 V1.3 and 8Mb ram. The program is in C. time spent is low enough to permit on-line use (for compound word enrichment for instance).</Paragraph>
    </Section>
    <Section position="3" start_page="0" end_page="0" type="sub_section">
      <SectionTitle>
5.1 Searching Noun-Adjective patterns
</SectionTitle>
      <Paragraph position="0"> 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.</Paragraph>
      <Paragraph position="1">  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.</Paragraph>
    </Section>
  </Section>
  <Section position="5" start_page="0" end_page="0" type="metho">
    <SectionTitle>
6. Conclusion
</SectionTitle>
    <Paragraph position="0"/>
  </Section>
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