Eckhard Bick - VISL

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in the sequential “understanding” of an utterance, - and the extra safety tax imposed on right hand contexts. c) Close context conditions are more common than distant context conditions This final observation matches one's intuition about structural cohesion, close contexts have a higher probability of being structurally linked to the target than distant ones. Also, when looking at a distant context, it is mandatory to check the closer context in between, too, - for potential clause boundaries or other blocking elements. So the -1 position will be checked both for its own sake, and (also) every time the -2, -3 or -4 contexts are addressed. All of the above findings are true of unbounded contexts, too: - 178 -
(5b) context position, polarity (±NOT) and certainty (±C) [unbounded contexts] ¡ ¢ number of contexts morf syn map all morf0 morf1 morf2 morf3 syn0 syn1 syn2 syn3 *1 117 48 9 6 409 70 14 - 238 911 *1C 48 14 - 1 107 24 1 - 2 197 NOT *1 61 22 2 4 33 2 - 1 10 135 all *1 226 549 260 1243 C-percent 48.2 25.5 4.6 26.7 *-1 147 44 12 2 565 190 16 - 349 1325 *-1C 64 23 - 2 275 65 - - 8 437 NOT *-1 61 27 2 1 64 9 - - 16 180 all *-1 272 904 373 1922 C-percent 46.0 37.5 6.4 31.1 *2 30 7 - - 10 2 - - 20 69 *2C 8 - - - 3 - - - - 11 NOT *2 4 2 - - - - - - - 6 all *2 42 13 20 86 C-percent 28.6 23.1 - 19.8 *-2 62 13 4 - 58 2 - - 77 216 *-2C 9 4 3 - 3 - - - - 11 NOT *-2 4 1 - - 6 3 - - 26 14 all -2 75 67 103 241 C-percent 17.3 13.4 34.2 10.4 *3 5 2 - - - - - - - 7 *3C - - - - - - - - - - NOT *3 1 - - - - - - - - 1 all *3 6 - - 8 C-percent 16.7 - - 12.5 *-3 8 2 - - 5 1 - - 16 32 *-3C 1 1 - - - - - - - 2 ¢ NOT ¢ £ *-3 - - - - 2 - - - - 2 all *-3 9 7 16 36 C-percent 11.1 28.6 - 11.1 all *+ 152 57 9 6 419 72 14 - 258 987 all *+C 56 18 - 1 120 24 1 - 2 208 all NOT *+ 66 25 - 4 33 2 - 1 10 142 all *+ 274 572 270 1337 C-percent 44.5 26.7 4.1 26.2 all *- 217 59 16 2 628 193 16 - 442 1573 all *-C 74 28 3 2 278 65 - - 8 450 all NOT *- 65 28 2 1 72 12 - - 42 196 - 179 -
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Eckhard Bick ♠ THE PARSING SYSTEM
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Abstract The dissertation describes
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3.7.2. Making the most of the lexic
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Inflexion tags 451 Syntactic tags 4
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first, - but soon, it would move th
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disambiguated), same level tags (to
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chain, but rather the linguistic co
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2 The lexicomorphological level: St
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The core of PALMORF is written in C
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(d) as part of words. For instance,
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(1) binary search technique: a . .
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acapitã#=#####B(orn)###413 acara#a
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Words with graphical accents often
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2.2.3.2 The inflexional endings lex
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2.2.3.3 The suffix lexicon (1) 1
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Suffix combination rules is also us
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an V a DERP a- [ANT] brad Vi as HV
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"inimigo" ADJ M P "inimigo" N M P
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variable word forms receive '_'- li
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The remaining 2 words of the 4-word
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appear truncated or not, depending
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comparison to the substring consist
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My present linguistic solution 28 i
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In some non-personal proper nouns,
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(i.e., not too complex) derivationa
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2.2.4.5 Abbreviations and sentence
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Another case, where meaning bearing
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2.2.4.6 The human factor: variation
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circumflex-) accented words without
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the tagger tries to identify a word
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xxxar-#1##AaiD######54578 endings-s
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"sombrancelha" N F P '=sobrancelha
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(6) Word class distribution and par
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VFIN 24.96 16 7.77 9 2.46 - - 25 3.
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the present participle by derivatio
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Inflexion tags combine with word cl
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2.2.5.2 The individual word classes
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SPEC "specifiers": independent pron
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WORD FORM and LEXEME CATEGORIES gen
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WORD FORM and LEXEME CATEGORIES dea
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mood VFIN finite IND indicative SUB
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prefixal derivation in verbs comple
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graph. def. characteristics: hyphen
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PP prepositional group de=aluguel
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2.2.5.3 Portuguese particles By ‘
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Deictic adverbs refer to discourse
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algo, algum=tanto, nada, nadinha?,
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quando når, da hvornår quanto [QU
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(1) Language distribution and error
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3 Morphosyntactic disambiguation: T
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(4) "quando" conjunction or adverb
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oth for the simple ambiguities intr
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case of a 3.person possessor, the u
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The fact that these semantic distin
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In (Karlsson et. al, 1995:19ff) an
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meaning- scope someone loves everyb
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• 5. Tags can be integrated as me
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19 0 0 - - - - >= 20 15 1 - - - - t
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Portuguese non-name words. Apart fr
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words: 40.8 10.5 17.9 2.9 0.5 3.1 8
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quite distinct, and few irregular e
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3.3 Borderline ambiguity: The limit
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(5b) They [saw(see) [the girl with
Page 128 and 129: Choose 'saw', if within the same cl
Page 130 and 131: ambiguous words), moving them to "l
Page 132 and 133: (3) modernização "modernizar" N
Page 134 and 135: 3.5.1 Probabilistics: The 'fire a l
Page 136 and 137: In a brute force approach, for an a
Page 138 and 139: While undeniably involving more con
Page 140 and 141: Generative Grammar, introduced and
Page 142 and 143: node without branches matching the
Page 144 and 145: example is a grammar of one termina
Page 146 and 147: wide coverage tagging and parsing t
Page 148 and 149: ambiguity, Constraint Grammar can a
Page 150 and 151: syntactic reach of ordinary hand-cr
Page 152 and 153: “base form-1” .. .. WORD CLAS
Page 154 and 155: Base forms ("...") are tags like al
Page 156 and 157: play after base form rules targetin
Page 158 and 159: The above sequences are examples of
Page 160 and 161: 3.7.2 Making the most of the lexico
Page 162 and 163: (1) number of CG rules containing a
Page 164 and 165: Intuitively, one might assume (a) t
Page 166 and 167: constructions. Something similar is
Page 168 and 169: are shaded, as well as the sum-colu
Page 170 and 171: For global context syntactic rules
Page 172 and 173: In table (4), three types of contex
Page 174 and 175: ules appear to be slightly more com
Page 176 and 177: (5a), continued ¡ ¢ number of con
Page 180 and 181: all - 356 978 492 2219 C-percent 39
Page 182 and 183: 3.8 Mapping: From word class to syn
Page 184 and 185: @ADVL [N-temp ADV PRP #ICL #FS #AS]
Page 186 and 187: @#AS-A< [ADV-rel] @#AS- @#AS-
Page 188 and 189: During the project period I have do
Page 190 and 191: syntactic errors around it, by prov
Page 192 and 193: 1. Orthography and layout normalisa
Page 194 and 195: Of course, the use of dishesion mar
Page 196 and 197: S2 para aquele ... está perfeitame
Page 198 and 199: Parser performance on speech data (
Page 200 and 201: 4 The syntactic level: A dependency
Page 202 and 203: subjects, but the second subject ca
Page 204 and 205: (7) SUBJ o [o] DET M S @>N 'the' b
Page 206 and 207: 4.1.2 Dependency relation types: Cl
Page 208 and 209: (1) Table: Constituent structure ty
Page 210 and 211: Any further, more detailed, syntact
Page 212 and 213: discussion are inspired by Perini (
Page 214 and 215: Thus, we get ”Comeu peixe/bananas
Page 216 and 217: type-4 adverbials. Second, adverbia
Page 218 and 219: they all share one, and only one, c
Page 220 and 221: AB todos estes ambos os uns, uma,
Page 222 and 223: from personal pronouns, are not mut
Page 224 and 225: noun. Only very few adjectives (the
Page 226 and 227: 4.2.2 The adpositional group head a
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However, adnominals can point to a
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(g) isso existe (até/nem (nos Esta
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4.3 The verb chain 4.3.1 The predic
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* aspect estar/andar/continuar/segu
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Ordinarily, 'o' is understood as su
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second verb's object 152 ), with so
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different subjects of matrix and su
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case function ± fronting ÷ fronti
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Quando ”quando” ADV @#FS-ADVL
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abalançar a kaste sig ud i at acab
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fazer xt, xd xdr få til at, lade s
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4.4 Clause types and clause functio
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@#ICL- non-finite subclause (combin
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While it is one of the clausality t
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(6) ..., tudo @S< pago @NN mão @
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(1g) Não [queria [comer outro bolo
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Apart from the presence of predicat
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demais [demais] DET M/F S/P @>N ‘
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element is a comparator and the sec
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without a predicator's valency info
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quantifying) cases, indicating adve
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4.5 Under the magnifying glass: Spe
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O assunto eram as ditaduras 2. F E
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The examples show that the normal "
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focusing function to the syntactic
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the quantifying adverbs mais and me
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qual DET @SC> @#FS-KOMP< antes era
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o [o] DET M S @AS< ‘that’ de [d
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[P=predicator, O=object, A=adverbia
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mais=de [mais=de] ADV @>A ‘more
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(2k) Ficou [ficar] V PS 3S IND V
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interno [interno] ADJ M S @N< '-'
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a preposition, por, then allowing f
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4.5.3 Tagging the quantifier 'todo'
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connection with personal pronouns -
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4.5.4 Adverbial function 4.5.4.1 Ar
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explanation for this may be that a
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a [a] DET F S @>N ‘the’ pergun
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carro [carro] N M S @SUBJ> ‘car
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pass the second test ('*De seu novo
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Therefore, the @ADV-@ADVL distincti
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@PIV, @ADV and @SC are - unlike @AD
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heads a "material" PP. The followin
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4.5.4.3 Intensifier adverbs Intensi
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4.5.4.4 Complementiser adverbs: Int
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complementiser in nominal FS or ICL
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hooked AS-relatives (3) they even a
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4.5.4.5 Adverb disambiguation and o
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position: adverb class: A) set oper
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(5b) fits fairly well into the (A)
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antes/depois+de Depois [depois] AD
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While (2c-e) can be described as pr
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In (4d) ter ('have') does have mono
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When the potential subject is post-
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(1) Table: functions of pronominal
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ditador [ditador] N M S @N ‘-’
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In other contexts, para is not usua
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life that the work of different gra
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SPEC DET/SPEC ¬ @#FS/AS-N< artic
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4.6.3 Tree structures for constitue
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dono de restaurante que pilota a pr
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|-@N
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4.7 Elegant underspecification An i
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5 The uses of valency: A bridge fro
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Returning to a research view concer
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5.2 Valency instantiation: From sec
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Note that valency is treated as a f
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REMOVE (@%vt) (0 @%vK) (*1C @
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word order can't be counted on to m
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than by asking ‘is it an A?’ or
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hemisphere membership 218 , - ‘SW
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disambiguate the senses ‘suit’
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In all, the parser uses 16 atomic s
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‘+ uninferable positive feature +
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6.5 A semantic Constraint Grammar I
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capable - by “negative instantiat
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LIST = (@=c @=v @=p @=s @=t) ; LIS
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um verão ‘a summer’ * ok. * f
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An example of argument-head polysem
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The program module implementing the
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REMOVE (@=X) (*1 @%jn BARRIER @NON-
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@AUX, 'know how to, can' @MV, 'ta
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Note that the MT engine will choose
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Even where none of the semantic rul
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A casa revista PCP @N< revista PC
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O Itamarati +HUM @SUBJ The Itamara
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and a morphological CG module may c
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• V-trees - vertical tree structu
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descriptional complexity: 'full tag
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Here, each line of the CG-notation
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7.2 Grammar teaching on the Interne
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system's disposal. After acquiring
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the reach of "subject"-ness or "obj
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provided and what choices can be ma
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terminological confusion ? term not
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The tree structure of the sample se
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(5) Syntactic tree structures (VISL
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two function tags, since the studen
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7.3 Corpus research In the case of
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- 426 -
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- 428 -
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The NURC speech corpus (“Norma ur
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7.4 Machine translation 7.4.1. A te
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processors used by translators or l
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The following diagram shows how dif
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8 Conclusion: The advantages of inc
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- 440 -
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Sources: English: EngCG: Karlsson e
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(4) Table: syntactic tree structure
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lexicon which ensure removal of fal
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8.2 CG Progressive Level Parsing: G
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Methodologically, it is commonly cl
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$ the dollar sign is used to mark n
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@#ICL-AUX< argument verb in verb ch
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"unit" noun (e.g. "20 metros") att
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SEMANTIC TAGS The tables below prov
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ANIMATE NON-HUMAN, NON-MOVING bo 13
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stof 136 fabric seda ‘silke’ ma
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(-CONCRETE, - MASS, +COUNT) ‘modu
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ax 25 plus below: (-CONCRETE, -MASS
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cI 199 process (-CONTROL, imperfect
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oot search lexicon search takes a w
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translation, prepares bilingual lex
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in a typical van/von-name chain con
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Appendix: Example sentences The fol
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é [ser] V PR 3S IND VFIN @FMV quas
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será [ser] V FUT 3S IND VFIN @FMV
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julgamento [julgamento] N M S @P< $
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Os [o] DET M P @>N sabonetes [sabo
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parte [parte] N F S @P< de [de] PR
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inesquecíveis [inesquecível] ADJ
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Literature Almeida, Napoleão Mende
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Lindberg, Nikolaj, “Learning Cons
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Alphabetical index Page numbers in
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averbal subclauses;254 finite subcl
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attributive;249 nominal;248 finite
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for auxiliary definition;235;237 pa
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subject identity test;236 subjects
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ERROR: rangecheck OFFENDING COMMAND
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Eckhard Bick - VISL

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