Eckhard Bick - VISL

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ules appear to be slightly more complex than non-heuristic rules, the reason for this being the fact that the heuristic level distinction is also used for non-heuristic purposes: - for all but the mapping rules it is the only way to determine in which order rules will be applied. Also, some of the hard, multi-context, cases are postponed to heuristic level 1, because there is a hope that other rules will resolve or restrict the ambiguity in question in some indirect way. This double functionality of heuristic level 1 can even be seen in the statistics in table (4), as a double peak curve in the morf1 column. The first peak (1 context) reflects pure heuristic uses, like the removal of readings with a tag, the other (4 contexts) is related to rule ordering and the postponement of hard cases. While both a high remove/select ratio and a high percentage of safe contexts (NOT and C) make a grammar more cautious (and robust), they also make the parser a little slower. Among other things, table (5) contains the data necessary to understand the second part of this trade-off, which is related to context type distribution. The relevant parameter, C-percent, measures "certainty" and is computed as the ratio between the combined number of NOT and C conditions and the number ofall contexts at a given position. For the zero position (the target itself) the current cg-compilers do not permit C-conditions, so here, the "safe" portion will consist of the NOT conditions alone. (5a) Context position, polarity (±NOT) and certainty (±C) [absolute contexts] number of contexts morf syn map all morf0 morf1 morf2 morf3 syn0 syn1 syn2 syn3 0 554 181 20 53 812 258 36 13 473 2400 NOT 0 268 92 8 2 230 48 4 - 43 695 all 0 822 1042 516 3095 C-percent 32.6 22.1 8.3 22.5 +1 250 78 2 2 97 15 8 - 220 672 +1C 310 48 1 - 28 1 - - 4 392 NOT 1 191 66 4 3 62 11 2 - 133 572 all +1 751 187 357 1636 C-percent 66.7 48.1 38.4 59.0 -1 409 103 21 12 177 29 1 - 468 1218 -1C 381 52 - 6 43 - - - 3 485 NOT -1 275 103 13 4 70 20 - - 62 547 all -1 1065 290 533 2250 C-percent 61.6 39.0 12.2 45.9 +2 42 18 - - 32 - - - 41 133 +2C 73 5 - - 24 - - - - 102 NOT 2 53 6 - - 13 1 - - 8 81 - 174 -
all +2 158 69 49 316 C-percent 79.7 53.6 16.7 57.9 -2 122 22 2 5 43 6 - - 184 384 -2C 98 16 - - 19 5 - - 15 153 NOT -2 84 17 1 4 26 3 - - 26 161 all -2 304 88 225 698 C-percent 59.8 51.1 18.2 45.0 - 175 -
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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
Page 124 and 125: 3.3 Borderline ambiguity: The limit
Page 126 and 127: (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 176 and 177: (5a), continued ¡ ¢ number of con
Page 178 and 179: in the sequential “understanding
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,
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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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