Eckhard Bick - VISL

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(2a) A mãe foi a Berlim. (The mother went to Berlin.) (2b) A mãe foi a Maria. (The mother was Maria.) However, about 1-2% of all word forms in running text are (lexically) unknown names 22 . This percentage is so high that even without the help of the lexicon, the parser has to recognise the word forms in question at least in terms of their word class. The obvious heuristics is, of course, treating capitalised words as names. On its own, capitalisation is not a sufficient criterion, but in combination with foreign word heuristics and some knowledge about typical in-name inter-capitalisation elements (‘de’, ‘von’, ‘of’), the preprocessor can filter out at least some lexicon-wise unknown names, and fuse them into PROP polylexicals. 23 Since the morphological analyser program itself looks at one word at a time, analyses it, and then writes all possible readings to the output file, it can only look "backwards" (by storing information about the preceding word's analysis) 24 . Here four 25 cases can be distinguished, the probability for the word being a proper noun being highest in the first case, and lowest in the last: • 1. A capitalised word in running text, preceded by a another name (heuristic or not), certain classes of pre-name nouns (, e.g. 'senhor', , e.g. 'restaurante', 'rua', '-ista'-words and others) or the preposition 'de' after another name • 2. A capitalised word in running text, preceded by some ordinary lower case word • 3. A capitalised word in running text, preceded only by other capitalised words (The headline case) • 4. A sentence initial capitalised word 26 Another distinction made by the tagger is based upon whether or not the word in question can also be given some other (non-name) analysis, and upon how complex this analysis would be, in terms of derivational depth. The name reading is safest if no known root can be found, and least probable where an alternative analysis can be found without any derivation. Readings where the word's root part is short 27 in 22 The numbers given are an average across different text types. In individual news magazine texts (like VEJA), name frequency can actually be much higher. 23 This feature of the preprocessor was only activated recently (1999), and the statistics and examples in this chapter apply to corpus data analysed without preprocessor name recognition. 24 Even this minimal context sensitiveness is worth mentioning - TWOL-analysers, for instance, never look back at the preceding word. 25 In an earlier version, cases 1 and 2 were fused, resulting in a somewhat stronger "name bias": because ordinary lower case words would count as pre-name words, too, most upper case words in mid-sentence would get PROP as one of their tags. 26 The tagger assumes "Sentence initiality", if the last "word" is either a question mark, exclamation mark or a full stop not integrated into an abbreviation or ordinal numeral. 27 To avoid overgeneration, a number of very short lexemes, like the names of letters (tê, zê), have a (no derivation) tag in the lexicon. These lexemes are completely prohibited for ordinary derivation, - though some also exist in a special, for-derivation-only, orthographic variant, like letter-names (te, ze) that may combine with each other to form productive "phonetic" abbreviations. - 42 -
comparison to the substring consisting of its derivational morphemes and inflexion endings, are also regarded as less probable. The following table shows in which cases the tagger will choose a (derived) lexical analysis, a (heuristic) proper noun analysis, or both: - 43 -
Page 1 and 2: Eckhard Bick ♠ THE PARSING SYSTEM
Page 3 and 4: Abstract The dissertation describes
Page 5 and 6: 3.7.2. Making the most of the lexic
Page 7 and 8: Inflexion tags 451 Syntactic tags 4
Page 9 and 10: first, - but soon, it would move th
Page 11 and 12: disambiguated), same level tags (to
Page 13 and 14: chain, but rather the linguistic co
Page 15 and 16: 2 The lexicomorphological level: St
Page 17 and 18: The core of PALMORF is written in C
Page 19 and 20: (d) as part of words. For instance,
Page 21 and 22: (1) binary search technique: a . .
Page 23 and 24: acapitã#=#####B(orn)###413 acara#a
Page 25 and 26: Words with graphical accents often
Page 27 and 28: 2.2.3.2 The inflexional endings lex
Page 29 and 30: 2.2.3.3 The suffix lexicon (1) 1
Page 31 and 32: Suffix combination rules is also us
Page 33 and 34: an V a DERP a- [ANT] brad Vi as HV
Page 35 and 36: "inimigo" ADJ M P "inimigo" N M P
Page 37 and 38: variable word forms receive '_'- li
Page 39 and 40: The remaining 2 words of the 4-word
Page 41: appear truncated or not, depending
Page 45 and 46: My present linguistic solution 28 i
Page 47 and 48: In some non-personal proper nouns,
Page 49 and 50: (i.e., not too complex) derivationa
Page 51 and 52: 2.2.4.5 Abbreviations and sentence
Page 53 and 54: Another case, where meaning bearing
Page 55 and 56: 2.2.4.6 The human factor: variation
Page 57 and 58: circumflex-) accented words without
Page 59 and 60: the tagger tries to identify a word
Page 61 and 62: xxxar-#1##AaiD######54578 endings-s
Page 63 and 64: "sombrancelha" N F P '=sobrancelha
Page 65 and 66: (6) Word class distribution and par
Page 67 and 68: VFIN 24.96 16 7.77 9 2.46 - - 25 3.
Page 69 and 70: the present participle by derivatio
Page 71 and 72: Inflexion tags combine with word cl
Page 73 and 74: 2.2.5.2 The individual word classes
Page 75 and 76: SPEC "specifiers": independent pron
Page 77 and 78: WORD FORM and LEXEME CATEGORIES gen
Page 79 and 80: WORD FORM and LEXEME CATEGORIES dea
Page 81 and 82: mood VFIN finite IND indicative SUB
Page 83 and 84: prefixal derivation in verbs comple
Page 85 and 86: graph. def. characteristics: hyphen
Page 87 and 88: PP prepositional group de=aluguel
Page 89 and 90: 2.2.5.3 Portuguese particles By ‘
Page 91 and 92: 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
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Choose 'saw', if within the same cl
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ambiguous words), moving them to "l
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(3) modernização "modernizar" N
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3.5.1 Probabilistics: The 'fire a l
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In a brute force approach, for an a
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While undeniably involving more con
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Generative Grammar, introduced and
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node without branches matching the
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example is a grammar of one termina
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wide coverage tagging and parsing t
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ambiguity, Constraint Grammar can a
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syntactic reach of ordinary hand-cr
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“base form-1” .. .. WORD CLAS
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Base forms ("...") are tags like al
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play after base form rules targetin
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The above sequences are examples of
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3.7.2 Making the most of the lexico
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(1) number of CG rules containing a
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Intuitively, one might assume (a) t
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constructions. Something similar is
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are shaded, as well as the sum-colu
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For global context syntactic rules
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In table (4), three types of contex
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ules appear to be slightly more com
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(5a), continued ¡ ¢ number of con
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in the sequential “understanding
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all - 356 978 492 2219 C-percent 39
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3.8 Mapping: From word class to syn
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@ADVL [N-temp ADV PRP #ICL #FS #AS]
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@#AS-A< [ADV-rel] @#AS- @#AS-
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During the project period I have do
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syntactic errors around it, by prov
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1. Orthography and layout normalisa
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Of course, the use of dishesion mar
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S2 para aquele ... está perfeitame
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Parser performance on speech data (
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4 The syntactic level: A dependency
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subjects, but the second subject ca
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(7) SUBJ o [o] DET M S @>N 'the' b
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4.1.2 Dependency relation types: Cl
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(1) Table: Constituent structure ty
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Any further, more detailed, syntact
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discussion are inspired by Perini (
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Thus, we get ”Comeu peixe/bananas
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type-4 adverbials. Second, adverbia
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they all share one, and only one, c
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AB todos estes ambos os uns, uma,
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from personal pronouns, are not mut
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noun. Only very few adjectives (the
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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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