Proceedings of the LFG 02 Conference National Technical - CSLI ...

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¡ ¡ ¡ ¡ ¡ ¡ ¡ ¡ ¢ LFG02 – Kuhn: Corpus-based Learning in Stochastic OT-LFG PRED ‘read(x,y)’ PRED ‘PRO’ GF £ GF¨ © TNS FUT ¢ ¦ ¥ ¤ PERS NUM SG PRED ‘THING’ OP Q �� input § ¥�� ¦ � � � � � � IP NP I� she I VP will V� V NP read what CP NP C� what IP NP I� she I VP CP will V� V NP C� what C IP read will NP I� she VP V� V read ¡ ¡ ¡ ¡ ¡ ¡ ¢ ¡ ¡ ¡ ¡ ¡ ¡ ¢ ¡ ¡ ¡ ¡ ¢ PRED ‘read � S , O � ’ SUBJ � PRED ‘PRO’ � OBJ © TNS FUT PRED ‘PRO’ OP Q � PRED ‘read � S , O � ’ SUBJ � PRED ‘PRO’ � OBJ © TOPIC � � TNS FUT PRED ‘PRO’ OP Q � PRED ‘read � S , O � ’ SUBJ � PRED ‘PRO’ � OBJ © TOPIC � � TNS FUT candidates from ¡¤£¦¥§¡©¨�� (c-structure, f-structure) PRED ‘PRO’ OP Q � Figure 1: A sketch of the OT-LFG architecture (expressive optimization) � ¥ � ¦ � � � ¥ � � � { *OP-SPEC} � � { *OB-HD, *STAY } ¦ � ¥ � � � � � { *STAY, *STAY } ¦ (2) a. OP-SPEC (Bresnan 2000) An operator must be the value of a DF [discourse function] in the f-structure. b. OB-HD (Bresnan 2000, (21)) Every projected category has a lexically filled [extended, JK] head. c. STAY (Bresnan 2000, (24)) Categories dominate their extended heads. Given the language-specific ranking of constraint importance, different structures from the set of candidates arise as optimal in the sense of violating the fewest of the most important constraints (see section 3 for some more discussion). In English (3), it is more important to mark the scope of wh-elements overtly than to realize arguments in their canonical position; in a wh-in situ language the situation is different: (4). Only the optimal candidate is defined to be a grammatical realization of the underlying part of the f-structure (“input”). 242 ☞ constraint profile
LFG02 – Kuhn: Corpus-based Learning in Stochastic OT-LFG (3) a. R1: OP-SPEC OB-HD STAY: English b. Candidate set: OP-SPEC OB-HD STAY [¡£¢ [¤¥¢ [¡£¢ [¤¨¢ [¦§¢ [¡£¢ [¤¨¢ [¦§¢ she will read what]] *! what she will read]]] *! * ☞ what will she read]]] ** (4) a. R2: STAY OP-SPEC OB-HD: wh in situ language b. STAY [¡£¢ [¤¥¢ [¡£¢ [¤¨¢ [¦©¢ [¦§¢ [¤¨¢ [¡£¢ Candidate set: ☞ “she” “will” “read” “what”]] * “what” “she” “will” “read”]]] *! * “what” “will” “she” “read”]]] *!* Interpretive optimization The general architecture of standard expressive optimization is easily adapted to a slightly different formal system (Kuhn forthcoming, ch. 5): if the set of competing candidate structures is not defined by a common semantic representation, but by a common surface string, we get a system of interpretive optimization. Rather than choosing from different potential syntactic realizations of a meaning, the OT evaluation now chooses from different syntactic structures (many of which differ in semantic interpretation too) for a given surface string. This “reverse” formal system has been adapted for a variety of linguistic modeling tasks, in particular for a derivation of the discrepancy between production and comprehension in language acquisition (Smolensky 1996), and in syntax to model word order freezing effects (Lee 2001, Kuhn 2001b). Interpretive optimization may also be assumed in the learning procedure for a standard expressive OT grammar, which will be discussed in section 5.1. 3 Ranking vs. weighting The previous discussion—like most of the linguistic work in OT—took a central OT assumption for granted: The relative importance of the constraints for a specific language is determined by a strict ranking. This means that violating a high-ranking constraint is worse than arbitrarily many violations of some lower-ranking constraint. The ranking scheme is more restrictive than a summation over weighted constraints would be (which one might have chosen as a more general way of computing the joint 243 OP-SPEC OB-HD
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Proceedings of the LFG 02 Conferenc
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Table of Contents Asudeh, Ash 1-18
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The Syntax of Preverbal Particles a
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2 The Problem: Five Conflicting Cla
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(10) CP C ′ XP CP XP The outermos
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(18) CP C ′ C ∅ IP CP IP I I
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1-3, unless problematic assumptions
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(4c) *After [IP last year [IP she r
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The adjunction rule (36) does not b
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(8c) chun isteacht duit [CP[CP nuai
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work on non-projecting categories a
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Coordination and Parallelism in Glu
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Traditional Linear Duplication a, a
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(8) fred : fe see : fe ⊸ je ⊸ s
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us add the following additional con
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3.2 A General Semantic Schema for C
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approach also achieves correct resu
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In summary, to show that two deriva
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Extraction possible from initial (p
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5.3.2 Across-the-Board Extraction T
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Asudeh, Ash (in progress). Resumpti
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Montague, Richard (1974). Formal ph
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Abstract VP-chaining in Oriya (an I
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(3) haata dho-i bhaata khaa-i mun s
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Figure 4. Schematic representation
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the medicine is applied. If overtly
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The control equation in (13) establ
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intervening object and thus ‘kept
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Lexicon L(1): khi-aa-ga-laa: V L(2)
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(20) VP -> VP V' α � *α SIT-IND
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CONSTRAINT SYMMETRY IN OPTIMALITY T
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‘Juan hit that dog with a stick
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‘With what did Juan hit the dog?
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The unexpectedly bad candidate is (
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Amharic (Semitic, Ethiopia) is a pr
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31) Wïšša-w bä=Daniel bet atäg
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A functional PP must contain a head
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appear): A less well known example
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appear on the non-recursive side of
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Publications. Smith Stark, Thomas.
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Abstract The development of large c
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subj : spec : det : pred : the head
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could annotate the CFG rules extrac
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strings in the WSJ section of the P
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Annotation coverage is measured in
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Our grammars are extracted from sec
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Penn-II training set is discarded f
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grammatical functions are more akin
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forms to semi-automatically develop
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Predicate Argument Structure. In: P
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Abstract PARTITIVE NOUN PHRASES IN
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possessive partitives in (7) and (8
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Extraction of constituents of dativ
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iológiát [[az egyik-en]NP __ ]NP
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Unlike post-verbal constituent orde
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uncertainty. Adopting this idea, I
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accounts for genitive possessor nou
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(38) Hungarian TOPIC/SCOPE/FOCUS pa
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The rule in (41) shows that the top
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Payne, John and Erika Chisarik. (20
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��
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��
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��
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��
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��
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��
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��
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GETARUN PARSER Rodolfo Delmonte Abs
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vital aid in the actual development
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levels of representations required
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sentence starts with a verb we let
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adjunct_cp negat pro=CLI, {Case=acc
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4. Parsing Strategies and P
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qualified. The following list of pr
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structure rules, after the head has
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subordinators, and to arguments of
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Hobbs J.R., D.E.Appelt, J.Bear, M.T
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SECTION II: ITALIAN TEXTS o La stor
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Mario criticava luigi perché ha ro
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1. Introduction Since the beginning
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. ⎡FOCUS ⎤ ⎢PRED ‘wonder (
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(12) *ng- oingerang a mlarngii a be
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(17) a. Hine ha- simla še kaniti.
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have resumptive pronouns. Under the
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way to license the same f-structure
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(33) a. Robin met the man that/who
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licensed in the normal way by funct
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description of the situation. Borer
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http://cslipublications.stanford.ed
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A (Discourse) Functional Analysis o
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A multi-factorial LFG analysis of a
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(9) CP (↑ SUBJ)= ↓ ↑=↓ NP C
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Word Order Properties SGF coordinat
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4.1 Asymmetric Analyses Heycock and
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(25) (a) S 〈 der Jäger ging in d
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In sum, the Subject Functor Lineari
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first and second conjunct are disti
Page 195 and 196: In the Glue Semantics approach (see
Page 197 and 198: In the optimality-based model, mism
Page 199 and 200: complementiser (COMPL) may be exten
Page 201 and 202: BULGARIAN WORD ORDER AND THE ROLE O
Page 203 and 204: Bulgarian word order and the role o
Page 225 and 226: 1 Introduction This paper deals wit
Page 227 and 228: (4) To nifiko itan raf-to (*apo ton
Page 229 and 230: That is, in example (6) the partici
Page 231 and 232: (22) To spiti fenotan egataleli-men
Page 233 and 234: state denoted by the adjective appe
Page 235 and 236: in Modern Greek bears the intrinsic
Page 237 and 238: In addition, the lexical representa
Page 239 and 240: In this way, we capture the intuiti
Page 241 and 242: (57) To fagito ine magire-meno/*mag
Page 243 and 244: Corpus-based Learning in Stochastic
Page 245: LFG02 - Kuhn: Corpus-based Learning
Page 249 and 250: LFG02 - Kuhn: Corpus-based Learning
Page 263 and 264: 1. Introduction In this paper I wil
Page 265 and 266: This strategy has been outlined by
Page 267 and 268: The essence of the new approach is
Page 269 and 270: 3.2. Control relations in Hungarian
Page 271 and 272: complements of the postposition ut
Page 273 and 274: offered a more detailed discussion,
Page 275 and 276: lexical form which argument of this
Page 277 and 278: Berkeley. pp. 18. (On-line publicat
Page 279 and 280: 0. Abstract This paper attempts to
Page 281 and 282: 'understand', oppgi 'state', opplev
Page 283 and 284: This property of raising to object
Page 285 and 286: (32) ved/fra å svømme by/from to
Page 287 and 288: (45) *Til å sove ser han ut to (PR
Page 289 and 290: (57) Å gjøre det er blitt tenkt p
Page 291 and 292: Second argument: The preposition do
Page 293 and 294: In Lødrup (2002), I show that most
Page 295 and 296: Teleman, Ulf, Staffan Hellberg and
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Abstract Majority of Bantu language
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No Object Agreement Bresnan and Mch
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(7) a. N-ä-ï- `m-lyì-á k-èlyá
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epresent 72% of the total of 312 se
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We can characterize more concretely
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(19) Markedness Subhierarchies a. *
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4.1 Hierarchy of Morphological Feat
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(30) Harmonic Alignment of (28) wit
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topic-anaphoricity (as a bound pron
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the present analysis highlights the
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Grimshaw, Jane. 2001. Economy of st
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CLITICS AND PHRASAL AFFIXATION IN C
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(2) (a) ...wedi eu gweld nhw. ASP C
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(7) IP (↑GF) = ↓ ↑ = ↓ NP I
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(14) (a) Jovan je čitao knjigu. Jo
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The Principle of Morphological Comp
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3.2 Serbian Pronominal Clitics A se
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Example (35) has the c-structure in
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(43) (a) čovek-: N (↑PRED) = ‘
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(52) (a) jučer: N (ADJ↑) (↑PRE
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Case Marking and Subject Extraction
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objects occur in VP or in I (“obj
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Coordinated structures (14) Peter o
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(20) Han hadde de trodd e ville kom
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(28) a. * Peter forventer [I’ [I
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One disadvantage of this approach i
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6.2 Case Assignment and Empty Categ
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Lets us consider the binding condit
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claims that subject extraction is n
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ham: N ( (SUBJ £ ) ¡ ((COMP SUBJ
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[Vikner and Schwartz, 1996] Vikner,
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. Mary-ka ttal-lul sakwa-lul mek-I-
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In (4a), the object of the simple d
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3. Morphological Causatives in Kore
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. Mary-ka ttal-pakkey sakwa-ul an m
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Firstly, objects in MC are asymmetr
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The asymmetries of double accusativ
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Abstract The paper describes a pres
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construction - in good style. • I
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Figure 4: Attributed adjectives (
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Aspects of the syntax of psychologi
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Despite the syntactic differences e
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e. (exists independently of the eve
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(10) Poco a poco, me enfadó que pe
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to (3), we can thematically charact
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we can derive the grammatical funct
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‘This book interests him (John).
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unaccusative verb, while interesar
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Roegiest, E. (1999). Objet direct p
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Abstract The aim of this paper is t
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(6) Pierre lui a fait écrire une l
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dative NPs that the examples (4) to
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verb. In some languages, like Engli
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(15) *J’ai convaincu de lui tél
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VP � V + V + (NP) + (PP) (��
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a number of signs (words or phrases
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��
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DAVIS, A. & P.-J. KOENIG (2000) Lin
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1. Introduction Subsumption and Equ
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an XCOMP attribute whose value is t
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(8) a. S|VP Æ NP* (V’) ( S|VP )
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inconsistency with erzählen. But i
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We revise the fronted S|VP expansio
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5. The interaction between PVPF and
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(28) Ein Aussenseiter versuchte hie
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. * Ein Aussenseiter gewonnen haben
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The use of subsumption addresses fu
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TIGER TRANSFER Utilizing LFG Parses
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is an additional way of ensuring co
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"tiger_4548: Hier herrscht Demokrat
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y a multi-set of OT marks, thereby
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sumably they cannot be fixed in pri
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¢¡¤£¦¥¦§©¨�£¦§�¥
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Macros and Templates The system all
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Ambiguous Predicates A simple examp
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HD−OUT TI−FORM herrscht, TI−M
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(ignoring PP-attachment for the eva
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Kameyama, Megumi, Ryo Ochitani, and
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