Eckhard Bick - VISL

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In all, the parser uses 16 atomic semantic features for nouns. These features are strictly binary in the sense that every feature has only two states, plus and minus (±). Also, most of the 16 features are organised in a hierarchic binary tree with every higher level ± feature choice leading to two new (lower level) ± feature choice. In the feature tree there are 15 binary branching nodes, representing 12 atomic features (±MOVING, ±MASS and ±PERFECTIVE occur in two branching nodes each) and yielding 16 terminal categories as feature combination paths. For example, the terminal category of ‘place’ can be defined (in terms of atomic features) as +CONCRETE, -ANIMATE, - MOVING and –MOVABLE. humans Human + - animals Illustration: atomic semantic features - binary decision tree Moving + - Animate + - Moving + - vehicles Movable + - plants Mass + - substance material objects things Concrete + - place action Perfective + - - 372 - Control + - activity event Verbal Content + - Perfective + - Mass + - features Count + - process Measure + - Partitive + - quantities units stat e cognitive objects or products Apart from the semantic features shown in the decision tree, the parser uses 4 additional features:
± HUMAN EXPRESSION 220 (i.e. qualifiable by adjectives etc. usually associated with humans), e.g. política agresiva (‘aggressive politics’) ± ADJECTIVAL (FEATURE), e.g. tamanho (‘size’) ± LOCATION (as an independent feature, because -MOVE -MOVABLE alone doesn’t cover abstracta), e.g. concerto (‘concert’) ± TEMPORAL Note that the feature tree is organised as a decision tree. Therefore, it is possible to infer only those features from terminals, that lie in the decision path leading to that terminal. Animals, for example, are moving, non-human, animate and concrete, and the parser can disambiguate the concept ‘animal’ as true for a given sense by instantiating all these features, or as wrong, by disallowing at least one of the features. However, due to the way in which features are lumped into prototypes by physics or language/cognition, other atomic features may be inferred or used for disambiguation as well. Thus, all moving things are also movable, i.e. something that isn’t movable, can’t be an animal. Some prototypes, on the other hand, violate the branching rules of the feature tree because of metaphorical usage. The prototype (institutions), for instance, comprising words like igreja (‘church’) or justiça (‘justice’), routinely promotes buildings or abstract nouns to +HUM status by allowing them to act, think, decide and order. Therefore, the feature tree must be understood as a theoretical point of departure, while the real parsing system is built upon feature set intersection and disjunction for individual prototype bundles, thus relying heavily on the inclusion or exclusion of certain key features with a maximum of distinctive power. Exactly which positive or negative features can be inferred from which other positive or negative features, is shown in the table below, where all 16 atomic semantic features are plotted against the parser’s prototype classes for nouns, which are bundled according to the atomic feature bundles they match (one table row per prototype/feature bundle). The prototypes of institution (), town () and country (), for instance, form a bundle, and share the same set of atomic features, positive for CONCRETE/ENTITY (they can be touched), HUM (they can wish, decide and act), LOCATION (something can be in them), and negative for all other features. A feature X can be inferred in a given bundle, if there is a feature Y in the same bundle such that – with respect to the whole table - the set of prototype bundles with feature X is a subset of the set of prototype bundles with feature Y. In the table below, inferability is marked in the following way: 220 The difference between ±HUM and ±HUMAN EXPRESSION is to a certain degree isomorphic to the distinction between argument selection and modifier selection: ±HUM words can fill the subject slot in, for instance, speech verbs, while ±HUMAN EXPRESSION (only) can fill the head slot for human modifiers: triste (‘depressed’), otimístico (‘optimistic’). - 373 -
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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
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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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Page 332 and 333: In (4d) ter ('have') does have mono
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Page 336 and 337: (1) Table: functions of pronominal
Page 338 and 339: ditador [ditador] N M S @N ‘-’
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Page 352 and 353: 4.7 Elegant underspecification An i
Page 354 and 355: 5 The uses of valency: A bridge fro
Page 356 and 357: Returning to a research view concer
Page 358 and 359: 5.2 Valency instantiation: From sec
Page 360 and 361: Note that valency is treated as a f
Page 362 and 363: REMOVE (@%vt) (0 @%vK) (*1C @
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Page 376 and 377: 6.5 A semantic Constraint Grammar I
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Page 380 and 381: LIST = (@=c @=v @=p @=s @=t) ; LIS
Page 382 and 383: um verão ‘a summer’ * ok. * f
Page 384 and 385: An example of argument-head polysem
Page 386 and 387: The program module implementing the
Page 388 and 389: REMOVE (@=X) (*1 @%jn BARRIER @NON-
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Page 392 and 393: Note that the MT engine will choose
Page 394 and 395: Even where none of the semantic rul
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Page 398 and 399: O Itamarati +HUM @SUBJ The Itamara
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Page 406 and 407: Here, each line of the CG-notation
Page 408 and 409: 7.2 Grammar teaching on the Interne
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Page 418 and 419: The tree structure of the sample se
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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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