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This paper proposes a compositional semantics for lexicalized tree adjoining grammars (LTAG). Tree-local multicompnent derivations allow seperation of semantiv contribution of a lexical item into one component contributing to the predicate argument structure and second a component contributing to scope semantics. Based on this idea a syntx-semantics interface is presented where the compositional semantics depends only on the derivation structure. It is shown that the derivation structure allows an appropriate amount of underspecification. This is illustrated by investigating underspecified representations for quantifier scpoe ambiguities and related phenomena such as adjunct scope and island constraints.
Particles fullfill several distinct central roles in the Japanese language. They can mark arguments as well as adjuncts, can be functional or have semantic functions. There is, however, no straightforward matching from particles to functions, as, e.g., 'ga' can mark the subject, the object or the adjunct of a sentence. Particles can cooccur. Verbal arguments that could be identified by particles can be eliminated in the Japanese sentence. And finally, in spoken language particles are often omitted. A proper treatment of particles is thus necessary to make an analysis of Japanese sentences possible. Our treatment is based on an empirical investigation of 800 dialogues. We set up a type hierarchy of particles motivated by their subcategorizational and modificational behaviour. This type hierarchy is part of the Japanese syntax in VERBMOBIL.
This paper proposes a corpus encoding standard that meets the needs of linguistic research using a variety of linguistic data structures. The standard was developed in SFB 441, a research project at the University of Tuebingen. The principal concern of SFB 441 are the empirical data structures which feed into linguistic theory building. SFB 441 consists of several projects, most of which are building corpora to empirically investigate various linguistic phenomena in various languages (e.g. modal verbs in German, forms of address and politeness in Russian). These corpora will form the components of the "Tuebingen collection of reusable, empirical, linguistic data structures (TUSNELDA)". The TUSNELDA annotation standard aims at providing a uniform encoding scheme for all subcorpora and texts of TUSNELDA such that they can be processed with uniform standardized tools. To guarantee maximal reusability we use XML for encoding. Previous SGML standards for text encoding were provided by the Text Encoding Initiative (TEI) and the Expert Advisory Group on Language Engineering Standards (Corpus Encoding Standard, CES). The TUSNELDA standard is based on TEI and XCES (XML version of CES) but takes into account the specific needs of the SFB projects, i.e. the peculiarities of the examined languages and linguistic phenomena.
Existing analyses of German scrambling phenomena within TAG-related formalisms all use non-local variants of TAG. However, there are good reasons to prefer local grammars, in particular with respect to the use of the derivation structure for semantics. Therefore this paper proposes to use local TDGs, a TAG-variant generating tree descriptions that shows a local derivation structure. However the construction of minimal trees for the derived tree descriptions is not subject to any locality constraint. This provides just the amount of non-locality needed for an adequate analysis of scrambling. To illustrate this a local TDG for some German scrambling data is presented.
In this paper, we investigate the role of sub-optimality in training data for part-of-speech tagging. In particular, we examine to what extent the size of the training corpus and certain types of errors in it affect the performance of the tagger. We distinguish four types of errors: If a word is assigned a wrong tag, this tag can belong to the ambiguity class of the word (i.e. to the set of possible tags for that word) or not; furthermore, the major syntactic category (e.g. "N" or "V") can be correctly assigned (e.g. if a finite verb is classified as an infinitive) or not (e.g. if a verb is classified as a noun). We empirically explore the decrease of performance that each of these error types causes for different sizes of the training set. Our results show that those types of errors that are easier to eliminate have a particularly negative effect on the performance. Thus, it is worthwhile concentrating on the elimination of these types of errors, especially if the training corpus is large.
We present a solution for the representation of Japanese honorifical information in the HPSG framework. Basically, there are three dimensions of honorification. We show that a treatment is necessary that involves both the syntactic and the contextual level of information. The japanese grammar is part of a machine translation system.
Chunk parsing has focused on the recognition of partial constituent structures at the level of individual chunks. Little attention has been paid to the question of how such partial analyses can be combined into larger structures for complete utterances. Such larger structures are not only desirable for a deeper syntactic analysis. They also constitute a necessary prerequisite for assigning function-argument structure. The present paper offers a similaritybased algorithm for assigning functional labels such as subject, object, head, complement, etc. to complete syntactic structures on the basis of prechunked input. The evaluation of the algorithm has concentrated on measuring the quality of functional labels. It was performed on a German and an English treebank using two different annotation schemes at the level of function argument structure. The results of 89.73% correct functional labels for German and 90.40%for English validate the general approach.
Chunk parsing has focused on the recognition of partial constituent structures at the level of individual chunks. Little attention has been paid to the question of how such partial analyses can be combined into larger structures for complete utterances. The TüSBL parser extends current chunk parsing techniques by a tree-construction component that extends partial chunk parses to complete tree structures including recursive phrase structure as well as function-argument structure. TüSBLs tree construction algorithm relies on techniques from memory-based learning that allow similarity-based classification of a given input structure relative to a pre-stored set of tree instances from a fully annotated treebank. A quantitative evaluation of TüSBL has been conducted using a semi-automatically constructed treebank of German that consists of appr. 67,000 fully annotated sentences. The basic PARSEVAL measures were used although they were developed for parsers that have as their main goal a complete analysis that spans the entire input.This runs counter to the basic philosophy underlying TüSBL, which has as its main goal robustness of partially analyzed structures.
The Child Language Data Exchange System (CHILDES) consists of Codes for the Human Analysis of Transcripts (CHAT), Computerized Language Analysis (CLAN), and a database. There is also an online manual which includes the CHILDES bibliography, the database, and the CHAT conventions as well as the CLAN instructions. The first three parts of this paper concern the CHAT format of transcription, grammatical coding, and analyzing transcripts by using the CLAN programs. The fourth part shows examples of transcribed and coded data.