Wolfgang Seeker

Morphological and syntactic case in statistical dependency parsing

Most morphologically rich languages with free word order use case systems to mark the grammatical function of nominal elements, especially for the core argument functions of a verb. The standard pipeline approach in syntactic dependency parsing assumes a complete disambiguation of morphological (case) information prior to automatic syntactic analysis. Parsing experiments on Czech, German, and Hungarian show that this approach is susceptible to propagating morphological annotation errors when parsing languages displaying syncretism in their morphological case paradigms. We develop a different architecture where we use case as a possibly underspecified filtering device restricting the options for syntactic analysis. Carefully designed morpho-syntactic constraints can delimit the search space of a statistical dependency parser and exclude solutions that would violate the restrictions overtly marked in the morphology of the words in a given sentence. The constrained system outperforms a state-of-the-art data-driven pipeline architecture, as we show experimentally, and, in addition, the parser output comes with guarantees about local and global morpho-syntactic wellformedness, which can be useful for downstream applications.

Visualization, Search, and Error Analysis for Coreference Annotations

We present the ICARUS Coreference Explorer, an interactive tool to browse and search coreference-annotated data. It can display coreference annotations as a tree, as an entity grid, or in a standard textbased display mode, and lets the user switch freely between the different modes. The tool can compare two different annotations on the same document, allowing system developers to evaluate errors in automatic system predictions. It features a flexible search engine, which enables the user to graphically construct search queries over sets of documents annotated with coreference.

A Graphical Interface for Automatic Error Mining in Corpora

We present an error mining tool that is designed to help human annotators to find errors and inconsistencies in their annotation. The output of the underlying algorithm is accessible via a graphical user interface, which provides two aggregate views: a list of potential errors in context and a distribution over labels. The user can always directly access the actual sentence containing the potential error, thus enabling annotators to quickly judge whether the found candidate is indeed incorrectly labeled.

How to Train Dependency Parsers with Inexact Search for Joint Sentence Boundary Detection and Parsing of Entire Documents

We cast sentence boundary detection and syntactic parsing as a joint problem, so an entire text document forms a training instance for transition-based dependency parsing. When trained with an early update or max-violation strategy for inexact search, we observe that only a tiny part of these very long training instances is ever exploited. We demonstrate this effect by extending the ArcStandard transition system with swap for the joint prediction task. When we use an alternative update strategy, our models are considerably better on both tasks and train in substantially less time compared to models trained with early update/max-violation. A comparison between a standard pipeline and our joint model furthermore empirically shows the usefulness of syntactic information on the task of sentence boundary detection.

Stacking or Supertagging for Dependency Parsing -- What’s the Difference?

Supertagging was recently proposed to provide syntactic features for statistical dependency parsing, contrary to its traditional use as a disambiguation step. We conduct a broad range of controlled experiments to compare this specific application of supertagging with another method for providing syntactic features, namely stacking. We find that in this context supertagging is a form of stacking. We furthermore show that (i) a fast parser and a sequence labeler are equally beneficial in supertagging, (ii) supertagging/stacking improve parsing also in a cross-domain setting, and (iii) there are small gains when combining supertagging and stacking, but only if both methods use different tools. The important consideration is therefore not the method but rather the diversity of the tools involved.