Eckhard Bick - VISL

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(3) modernização "modernizar" N F S §33620§ "moderno" N F S §33621§ Mixed prefix/suffix readings are preferred over double prefix or double suffix readings. In practice, this weeding out, unlike "true" local disambiguation (1), is first performed at the CG disambiguation level. - 132 -
3.5 Tools for disambiguation In corpus linguistics, most systems of automatic analysis can be classified by measuring them against the bipolarity of rule based versus probabilistic approaches. Thus, Karlsson (1995) distinguishes between “pure” rule based or probabilistic systems, hybrid systems and compound systems, i.e. rule based systems supplemented with probabilistic modules, or probabilistic systems with rule based “bias” or postprocessing. As a second parameter, lexicon dependency might be added, since both rules based and probabilistic systems differ internally as to how much use they make of extensive lexica, both in terms of lexical coverage and granularity of lexical information. Typically, in terms of computational viability, probabilistic systems are good at lower level analysis, especially word class (part of speech, PoS) annotation and speech recognition, while rule based systems have been preferred for higher level annotation, like constituent trees and argument structure. As a result of this polarisation, the older - linguistically motivated - term "parsing", though derived from "pars orationis" (part of speech) has come to mean, more narrowly, higher level syntactic analysis, while the newer - computationally motivated - term "tagging" has mostly been limited to lower level PoS-annotation, - which is the obvious application for at leastword based tags. Even implementationally, the bipolarity is quite distinct: The archetypal rule based systems, PSG grammars and their descendants, have embraced declarative programming languages like Prolog and Lisp, while probabilistic systems huddle together around the Hidden Markov Model using procedural programming languages like C or - for statistics proper - common UNIX-tools like sort, uniq, awk and perl. With the advent of larger, multi-million word corpora, apart from annotation speed, error rates have become more crucial, since manual post-processing is becoming less and less feasible. On the one hand, this should favour rule-based systems, since they can - at least in theory - be made more "perfect", so the high initial price in man power for writing a grammar should pay off for large corpora - the larger the corpus the better the investment. On the other hand, large corpora supply better training facilities for the "cheap" probabilistic systems and should thus make them more accurate 88 . Yet again, since what is really needed, are tagged training corpora, co-operation between systems might be the best solution. This, however, presupposes more or less compatible category definitions and tag sets, which is, in spite of normalising initiatives like the EU's EAGLES convention (Monachini and Calzolari, 1996) far from being a reality today. 88 For a tagset of 50 PoS-inflexion tags or tag chains, for example, it is as hard to train trigrams on a million word corpus as it is to train tetragrams on a 50 million word corpus, the reason being, that the number is only 8 times as high as the number of different n-grams. Training trigrams on a hundred million word corpus, however, yields on average 800 examples of each trigram combination - even when ignoring the relatively higher frequency of the more relevant trigrams -, which should be enough to do statistics on. - 133 -
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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
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
Page 93 and 94: algo, algum=tanto, nada, nadinha?,
Page 95 and 96: quando når, da hvornår quanto [QU
Page 97 and 98: (1) Language distribution and error
Page 99 and 100: 3 Morphosyntactic disambiguation: T
Page 101 and 102: (4) "quando" conjunction or adverb
Page 103 and 104: oth for the simple ambiguities intr
Page 105 and 106: case of a 3.person possessor, the u
Page 107 and 108: The fact that these semantic distin
Page 109: In (Karlsson et. al, 1995:19ff) an
Page 112 and 113: meaning- scope someone loves everyb
Page 114 and 115: • 5. Tags can be integrated as me
Page 116 and 117: 19 0 0 - - - - >= 20 15 1 - - - - t
Page 118 and 119: Portuguese non-name words. Apart fr
Page 120 and 121: words: 40.8 10.5 17.9 2.9 0.5 3.1 8
Page 122 and 123: 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 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 178 and 179: in the sequential “understanding
Page 180 and 181: 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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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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