Sandra Kübler

MaltParser: A language-independent system for data-driven dependency parsing

Parsing unrestricted text is useful for many language technology applications but requires parsing methods that are both robust and efficient. MaltParser is a language-independent system for data-driven dependency parsing that can be used to induce a parser for a new language from a treebank sample in a simple yet flexible manner. Experimental evaluation confirms that MaltParser can achieve robust, efficient and accurate parsing for a wide range of languages without language-specific enhancements and with rather limited amounts of training data.

Is it Really that Difficult to Parse German

This paper presents a comparative study of probabilistic treebank parsing of German, using the Negra and TuBa-D/Z tree-banks. Experiments with the Stanford parser, which uses a factored PCFG and dependency model, show that, contrary to previous claims for other parsers, lexicalization of PCFG models boosts parsing performance for both treebanks. The experiments also show that there is a big difference in parsing performance, when trained on the Negra and on the TuBa-D/Z treebanks. Parser performance for the models trained on TuBa-D/Z are comparable to parsing results for English with the Stanford parser, when trained on the Penn treebank. This comparison at least suggests that German is not harder to parse than its West-Germanic neighbor language English.

A Unified Representation for Morphological, Syntactic, Semantic, and Referential Annotations

This paper reports on the SYN-RA (SYNtax-based Reference Annotation) project, an on-going project of annotating German newspaper texts with referential relations. The project has developed an inventory of anaphoric and coreference relations for German in the context of a unified, XML-based annotation scheme for combining morphological, syntactic, semantic, and anaphoric information. The paper discusses how this unified annotation scheme relates to other formats currently discussed in the literature, in particular the annotation graph model of Bird and Liberman (2001) and the pie-in-the-sky scheme for semantic annotation.

From Chunks to Function-Argument Structure: A Similarity-Based 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. 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 similarity-based 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.

Robust Chunk Parsing for Spontaneous Speech

Chunk parsing (see Abney, 1991, and Abney, 1996) offers a particularly promising approach for robust, partial parsing with the goal of broad data coverage. A chunk parser is particularly well suited for an application for spontaneous speech since it can deal robustly with fragmentary or ill-formed input.

TüSBL: a similarity-based chunk parser for robust syntactic processing

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 TuSBL 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. TuSBLs 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 TuSBL 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 TuSBL, which has as its main goal robustness of partially analyzed structures.