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<?xml version="1.0" standalone="yes"?> <Paper uid="C96-1009"> <Title>Extracting Nested Collocations</Title> <Section position="2" start_page="0" end_page="0" type="intro"> <SectionTitle> 1 Introduction </SectionTitle> <Paragraph position="0"> Tim increased inl;erest in collocation ext;raetion comes from t;hu faeI; l;hal, t;hey can be used for many NLP at)plical;ions such as machine transla(;ion, maehilw, aids R)r t;ra.nslal,ion, dictionary consl;ru(:i;ion, and secon(1 language learning, t.o mmm a few.</Paragraph> <Paragraph position="1"> Recently, large scale textual corpora give the potential of working with the real data, (!ither fin' grammar inferring, or for enriching the le.xicon. These corlms-based at)preaches have also been used for the extract, ion of collocal,ions.</Paragraph> <Paragraph position="2"> In this t)al)er we are concerned wil;h nested collocations. Collocations Lhat are subst;rings of oLher longer ones. I{egar(ling l;his l;ypu of (:olloeation, the approaches till ilOW could be divi(led inl;o t;wo groups: those thai; do uo(, refer to s'ttbstrings of colloco, l, ions as a l)arti(:ular problem, (Church and lla.nks, t99(); Kim and Cho, 1993; Nagao and Mori, 1994), and those t.hat; do (Kita et al., t994; Smadja, 1993; lkchara et al., 1995; Kjelhner, 11994). \[towew;r, (well the lal;t, er, deal wiLh only 1)arl; of the probh;m: they l,ry not to extract the mlwanl;cd substrings of collocations. In favour of this, l;hcy leave a large number of nested colloc.ations unextracted.</Paragraph> <Paragraph position="3"> ht section 2 collocations arc briefly discussed and the. l)roblem is determined. In section 3 our approach to t;he probl0an, 1;he algorithm and an examl)le are given. In section d the experimeld, S are discussed and t;he Inethod is (;olnpare(t with t, hat proposed by (Kita et a.l., 199d). In sectioll 5 I;tlel'e are conlmenl;s on relal;ed work and tinally Section 6 eonl;ains I;he conc, hlsions and 1;he fill;life work.</Paragraph> </Section> class="xml-element"></Paper>