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

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(2) a. [Die Stadt] ¢¡¤£ verkaufte [das Haus]¥§¦¨¦ [der Stiftung]©�¥�� . ‘The town sold the house to the foundation.’ b. [Die Stadt] ¢¡¤£ verkaufte [das Haus [der Stiftung]�§� ]¥§¦¨¦ . ‘The town sold the house of the foundation.’ Figure 4: Packed f-structure-chart of Die Stadt verkaufte das Haus der Stiftung. Automatic Disambiguation Example 1 is even more ambiguous. Das Haus and die Stadt can either be nominative or accusative, i.e. subject or direct object of the clause, cf. the readings in given in (3). In this case, one reading is rather improbable, namely that with the object occupying the first position (the ‘Vorfeld’). When like in this example, the case marking is ambiguous (but not when the accusative is overtly marked), then there is a strong dispreference against the reading with the object in the Vorfeld position. This phenomenon is sometimes called the word order freezing effect (compare (Kuhn 2001, sec. 4.2), Lee (2001)). This dispreference, as well as other biases, is exploited by a (non-statistical) mechanism that XLE provides for suppressing ambiguities automatically. The mechanism consists of a constraint ranking scheme inspired by Optimality Theory (OT), see Frank et al. (2001). Each rule and each lexicon entry may be marked by ‘OT marks’. When a sentence is parsed, each analysis is annotated 432
y a multi-set of OT marks, thereby keeping a record of all OT-marked rules and lexicon entries that were used for the respective parse. The grammar contains a ranked list of all OT marks. When an ambiguous sentence is parsed, the OT mark multi-sets of all readings compete with each other. A multiset containing a higher ranked OT mark than another multi-set is filtered out, thus suppressing highly improbable or marked readings, and reducing the number of ambiguities the human disambiguator has to deal with. (Note that no suppressing of parses happens in the absence of ambiguity.) (3) a. [Die Stadt]¥§¦¨¦ verkaufte [das Haus] ¢¡¤£ [der Stiftung]©�¥�� . [improbable] ‘The house sold the town to the foundation.’ b. [Die Stadt]¥§¦¨¦ verkaufte [das Haus [der Stiftung]�§� ] ¢¡¤£ . [improbable] ‘The house of the foundation sold the town.’ A mark ‘ObjInVorfeld’, for example, forces to disprefer a direct or indirect object in the ‘Vorfeld’ (rather than subject or adjunct). Thus the improbable readings in (3) are suppressed. For Sentence (1), the German LFG grammar reduces the eight possible readings 7 to two by means of the OT filter mechanism. A further option would be to integrate a probabilistic disambiguation of sentence analyses (cf. Riezler et al. 2000). 3 From LFG to TIGER This section deals with the mapping of the LFG grammar output to the TIGER format. Section 3.1 illustrates the close correspondence between LFG f-structure and TIGER graphs. Section 3.2 gives criteria for implementing the conversion procedure and motivates the particular way of splitting up the task in subtasks. Section 3.3 presents preprocessing steps that make the LFG output suited for the actual transfer component. Sections 3.4 and 3.5 deal with the actual transfer. Finally, Section 3.6 concludes the section with the postprocessing steps that complete the mapping. 3.1 LFG F-structure and Tiger Graphs The initial motivation for adopting the LFG-based annotation method was that despite many differences in details, the syntactic analyses of the LFG grammar and the TIGER graph representation are very similar at the level of functional/dependency structure: Both, LFG’s f-structure and the TIGER graph representation, model a dependency structure at a comparable degree of granularity. A further similarity is that in both representations, the dependency structure is explicitly related to the word string. In the case of LFG, this relation is mediated through the level of c-structure; in TIGER it is coded directly into the dependency graph. The TIGER format gives dependency structure priority over phrase structural constituency. It makes use of a generalized tree graph notation which allows for crossing branches. In both schemes, the situation can arise that a single f-structure/dependency-structural constituent corresponds to a set of non-adjacent terminal nodes, i.e., nodes with some intervening word material belonging to a higherlevel f-structure/constituent. For example, in Hans hat dem Bericht geglaubt, daß Maria kommt ‘Hans believed the report that Maria will come’, the embedded clause is a complement of the non-adjacent nominal Bericht, cf. the representations in Figure 5. The dependency which yields the crossing branch in the TIGER graph is encoded in the LFG f-structure as the complement clause embedded under a COMP feature of the nominal predicate. Along with these high-level similarities there are a number of differences in the representation conventions of TIGER vs. the German LFG grammar. One main difference is due to the fact that redundant 7 The discussed four readings are doubled by an additional ambiguity of the verb. It also allows a (dispreferred) subjunctive reading. 433
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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
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In the Glue Semantics approach (see
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In the optimality-based model, mism
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complementiser (COMPL) may be exten
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BULGARIAN WORD ORDER AND THE ROLE O
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Bulgarian word order and the role o
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1 Introduction This paper deals wit
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(4) To nifiko itan raf-to (*apo ton
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That is, in example (6) the partici
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(22) To spiti fenotan egataleli-men
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state denoted by the adjective appe
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in Modern Greek bears the intrinsic
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In addition, the lexical representa
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In this way, we capture the intuiti
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(57) To fagito ine magire-meno/*mag
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Corpus-based Learning in Stochastic
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LFG02 - Kuhn: Corpus-based Learning
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1. Introduction In this paper I wil
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This strategy has been outlined by
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The essence of the new approach is
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3.2. Control relations in Hungarian
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complements of the postposition ut
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offered a more detailed discussion,
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lexical form which argument of this
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Berkeley. pp. 18. (On-line publicat
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0. Abstract This paper attempts to
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'understand', oppgi 'state', opplev
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This property of raising to object
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(32) ved/fra å svømme by/from to
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(45) *Til å sove ser han ut to (PR
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(57) Å gjøre det er blitt tenkt p
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Second argument: The preposition do
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In Lødrup (2002), I show that most
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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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Page 391 and 392: unaccusative verb, while interesar
Page 393 and 394: Roegiest, E. (1999). Objet direct p
Page 395 and 396: Abstract The aim of this paper is t
Page 397 and 398: (6) Pierre lui a fait écrire une l
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Page 405 and 406: VP � V + V + (NP) + (PP) (��
Page 407 and 408: a number of signs (words or phrases
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Page 411 and 412: DAVIS, A. & P.-J. KOENIG (2000) Lin
Page 413 and 414: 1. Introduction Subsumption and Equ
Page 415 and 416: an XCOMP attribute whose value is t
Page 417 and 418: (8) a. S|VP Æ NP* (V’) ( S|VP )
Page 419 and 420: inconsistency with erzählen. But i
Page 421 and 422: We revise the fronted S|VP expansio
Page 423 and 424: 5. The interaction between PVPF and
Page 425 and 426: (28) Ein Aussenseiter versuchte hie
Page 427 and 428: . * Ein Aussenseiter gewonnen haben
Page 429 and 430: The use of subsumption addresses fu
Page 431 and 432: TIGER TRANSFER Utilizing LFG Parses
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Page 435: "tiger_4548: Hier herrscht Demokrat
Page 439 and 440: sumably they cannot be fixed in pri
Page 441 and 442: ¢¡¤£¦¥¦§©¨�£¦§�¥
Page 443 and 444: Macros and Templates The system all
Page 445 and 446: Ambiguous Predicates A simple examp
Page 447 and 448: HD−OUT TI−FORM herrscht, TI−M
Page 449 and 450: (ignoring PP-attachment for the eva
Page 451: Kameyama, Megumi, Ryo Ochitani, and
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