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Proceedings - Natural Language Server - IJS

Proceedings - Natural Language Server - IJS

Proceedings - Natural Language Server -

  • Page 2 and 3: Zbornik 15. mednarodne multikonfere
  • Page 4 and 5: PREDGOVOR MULTIKONFERENCI INFORMACI
  • Page 6 and 7: KONFERENČNI ODBORI CONFERENCE COMM
  • Page 8 and 9: KAZALO / TABLE OF CONTENTS Jezikovn
  • Page 10: Zbornik 15. mednarodne multikonfere
  • Page 13 and 14: RECENZENTI • doc. dr. Simon Dobri
  • Page 15 and 16: language similarity as a feature of
  • Page 17 and 18: ually annotating 345 sentences and
  • Page 19 and 20: Disambiguating vectors for bilingua
  • Page 21 and 22: Language POS Source word Slovene se
  • Page 23 and 24: 4. Evaluation and discussion of the
  • Page 25 and 26: , Iztok Kosem**, Berginc*** * Troj
  • Page 27 and 28: vseh besed, se pravi, da bi ta
  • Page 29 and 30: 3. Spletni vmesnik za dostop do kor
  • Page 31 and 32: SPOOK.Sem: semantično označevanje
  • Page 33 and 34: uporabili samo za angleški del kor
  • Page 35 and 36: Vsekakor pa nas je pri označevanju
  • Page 37 and 38: Sistem vsebinskega priporočanja do
  • Page 39 and 40: poljubno, avtor pa svetuje vrednost
  • Page 41 and 42: Tabela 5: Filtriranje z dinamično
  • Page 43 and 44: Luka *, * Artificial Intelligence
  • Page 45 and 46: 4. Technical approaches and algorit
  • Page 47 and 48: Tehnologije govorjenega jezika v pa
  • Page 49 and 50: zma. Možno jih je namre uporabiti
  • Page 51 and 52: Kaja Dobrovoljc, 1 Simon Krek, 2 Ja
  • Page 53 and 54:

    3.1.4. fazah: v prvi fazi s

  • Page 55 and 56:

    (del, dol, vez, skup) in pri poveza

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    ˇSirjenje slovarja in dvoprehodni

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    Uporabili smo orodje s katerim smo

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    zbirka besedil, korpus, slovar Toma

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    -c.org/ns/1.0" type="pb" xml:">-8"?> -c.org/ns/1.0" type="pb" xml:

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    6. Dostopnost virov dejanska možn

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    prinašajo 185 milijonov besed oz.

  • Page 69 and 70:

    Ugotovimo lahko, da po številu pol

  • Page 71 and 72:

    predlog, zadeva, podatek, podlaga,

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    Their main differences between them

  • Page 75 and 76:

    The corpus was PoS-tagged and lemma

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    5. Conclusions In this paper we pre

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    2000) is an English computer tutor

  • Page 81 and 82:

    Table 5: Classifier performance wit

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    forms of Croatian proper names, but

  • Page 85 and 86:

    on the output of the first CRF. We

  • Page 87 and 88:

    Table 4: CroNER performance dependi

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    Table 1: Description of the picture

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    syntactic movement out of the objec

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    and other agreement markers are use

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    Figure 2. A FST representing a simp

  • Page 97 and 98:

    encoded text representation as seen

  • Page 99 and 100:

    Izhodišče segmentacijsko-tokeniza

  • Page 101 and 102:

    3. Učni korpus ssj500k 4 Učni kor

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    azreševanje stavčne ali celo meds

  • Page 105:

    težavnosti; pri oceni berljivosti

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    Kako dobro programi popravljajo vej

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    okviru projekta ESS Uspešno vklju

  • Page 114 and 115:

    Besana opozarja na manjkajoče veji

  • Page 116 and 117:

    Umetno tvorjenje slovenskega govora

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    Tabela 1: Analiza čistopisa zbirke

  • Page 120 and 121:

    Distributional Semantics Approach t

  • Page 122 and 123:

    3.2.1. Random indexing Random index

  • Page 124 and 125:

    Table 2: Accuracy for all considere

  • Page 126 and 127:

    Avtomatsko luščenje leksikalnih p

  • Page 128 and 129:

    3.1. Metodologija in zaporedje post

  • Page 130 and 131:

    začetku povedi in upoštevanje t.

  • Page 132 and 133:

    Izdelava XML-shem za slovarske proj

  • Page 134 and 135:

    standardi še niso bili vzpostavlje

  • Page 136 and 137:

    nepričakovan zapis ali napačen za

  • Page 138 and 139:

    Building Named Entity Recognition M

  • Page 140 and 141:

    corpus token transfer type transfer

  • Page 142 and 143:

    morphological information, for Slov

  • Page 144 and 145:

    Luščenje terminoloških kandidato

  • Page 146 and 147:

    3.1. Enobesedni terminološki kandi

  • Page 148 and 149:

    Rezultati priklica so dobri. Od 109

  • Page 150 and 151:

    Event and Temporal Relation Extract

  • Page 152 and 153:

    Table 1: Event annotation summary E

  • Page 154 and 155:

    Table 3: Event extraction performan

  • Page 156 and 157:

    Korpusna analiza slovenskega delež

  • Page 158 and 159:

    Vrsta besedila Izbrana področja (i

  • Page 160 and 161:

    primerih (11) in (12), ne pa za del

  • Page 162 and 163:

    Identifying Fear Related Content in

  • Page 164 and 165:

    not present” by 23 annotators. Th

  • Page 166 and 167:

    A Web Service Implementation of Lin

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    equired to install the specific sof

  • Page 170 and 171:

    In both figures the same workflow i

  • Page 172 and 173:

    Termania - prosto dostopni spletni

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    informacije so poleg same vsebine g

  • Page 176 and 177:

    Izdelava slovensko-srbskega vzpored

  • Page 178 and 179:

    dovoljeno odstopanje do 1 sekunde.

  • Page 180 and 181:

    Poleg navedenih težav so se pojavl

  • Page 182 and 183:

    Topic ontology construction from En

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    Fig. 2: English topic ontology afte

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    4. Topic ontologies constructed fro

  • Page 188 and 189:

    Translating news to CycL using the

  • Page 190 and 191:

    and the Cyc concept, which correspo

  • Page 192 and 193:

    without parsing. One type of such s

  • Page 194 and 195:

    Guessing the Correct Inflectional P

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    noted that the distribution of LPPs

  • Page 198 and 199:

    Table 1: Feature selection analysis

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    Razpoznavanje imenskih entitet v sl

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    N − log Z(x (i) ) − i=1 K k=1

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    F1 1.0 0.8 0.6 0.4 0.2 0.0 0.63 0.4

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    sloWCrowd: orodje za popravljanje w

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    2.2. Uporabniški vmesnik Osnovna f

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    uporabnikov z večanjem stopnje zan

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    Korpus slovenskega znakovnega jezik

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    Posnetki se v anonimizirani obliki

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    ZEN: zasnova glasovnih e-storitev v

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    Rešitev je bila izvedena na podlag

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    predpisane aktivnosti v poteku zdra

  • Page 222 and 223:

    Indeks avtorjev / Author index Agi

Eksperiment Čarovnik iz Oza - Natural Language Server
Ruben Sipoš MODELIRANJE SOPOJAVITEV BESED Z ... - IJS