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We present an architecture for the integration of shallow and deep NLP components which is aimed at flexible combination of different language technologies for a range of practical current and future applications. In particular, we describe the integration of a high-level HPSG parsing system with different high-performance shallow components, ranging from named entity recognition to chunk parsing and shallow clause recognition. The NLP components enrich a representation of natural language text with layers of new XML meta-information using a single shared data structure, called the text chart. We describe details of the integration methods, and show how information extraction and language checking applications for realworld German text benefit from a deep grammatical analysis.
This paper proposes an annotating scheme that encodes honorifics (respectful words). Honorifics are used extensively in Japanese, reflecting the social relationship (e.g. social ranks and age) of the referents. This referential information is vital for resolving zero
pronouns and improving machine translation outputs. Annotating honorifics is a complex task that involves identifying a predicate with honorifics, assigning ranks to referents of the
predicate, calibrating the ranks, and connecting referents with their predicates.
This report explores the question of compatibility between annotation projects including translating annotation formalisms to each other or to common forms. Compatibility issues are crucial for systems that use the results of multiple annotation projects. We hope that this report will begin a concerted effort in the field to track the compatibility of annotation schemes for part of speech tagging, time annotation, treebanking, role labeling and other phenomena.
We investigate methods to improve the recall in coreference resolution by also trying to resolve those definite descriptions where no earlier mention of the referent shares the same lexical head (coreferent bridging). The problem, which is notably harder than identifying coreference relations among mentions which have the same lexical head, has been tackled with several rather different approaches, and we attempt to provide a meaningful classification along with a quantitative comparison. Based on the different merits of the methods, we discuss possibilities to improve them and show how they can be effectively combined.
Der Beitrag behandelt zunächst die Frage, welche Vorteile elektronische Wörterbücher gegenüber traditionell gedruckten Wörterbüchern besitzen. Danach werden drei Online-Programme zur automatischen Übersetzung (Babelfish, Google Übersetzer, Bing Translator) vorgestellt. Beispieltexte werden mit diesen Programmen übersetzt, danach wird die jeweilige Qualität der Übersetzungen beurteilt. Schließlich diskutiert der Beitrag noch die Folgen, die durch die Möglichkeiten automatischen Übersetzens für die Auslandsgermanistik zu erwarten sind. Dabei zeigt sich, dass Programme für das automatische Übersetzen künftig durchaus ernstzunehmende Auswirkungen auf die philologischen Wissenschaften haben können.
Der Übersetzungsprozess der Technischen Dokumentation wird zunehmend mit Maschineller Übersetzung (MÜ) unterstützt. Wir blicken zunächst auf die Ausgangstexte und erstellen automatisch prüfbare Regeln, mit denen diese Texte so editiert werden können, dass sie optimale Ergebnisse in der MÜ liefern. Diese Regeln basieren auf Forschungsergebnissen zur Übersetzbarkeit, auf Forschungsergebnissen zu Translation Mismatches in der MÜ und auf Experimenten.
Prepositional phrase (PP) attachment is one of the major sources for errors in traditional statistical parsers. The reason for that lies in the type of information necessary for resolving structural ambiguities. For parsing, it is assumed that distributional information of parts-of-speech and phrases is sufficient for disambiguation. For PP attachment, in contrast, lexical information is needed. The problem of PP attachment has sparked much interest ever since Hindle and Rooth (1993) formulated the problem in a way that can be easily handled by machine learning approaches: In their approach, PP attachment is reduced to the decision between noun and verb attachment; and the relevant information is reduced to the two possible attachment sites (the noun and the verb) and the preposition of the PP. Brill and Resnik (1994) extended the feature set to the now standard 4-tupel also containing the noun inside the PP. Among many publications on the problem of PP attachment, Volk (2001; 2002) describes the only system for German. He uses a combination of supervised and unsupervised methods. The supervised method is based on the back-off model by Collins and Brooks (1995), the unsupervised part consists of heuristics such as ”If there is a support verb construction present, choose verb attachment”. Volk trains his back-off model on the Negra treebank (Skut et al., 1998) and extracts frequencies for the heuristics from the ”Computerzeitung”. The latter also serves as test data set. Consequently, it is difficult to compare Volk’s results to other results for German, including the results presented here, since not only he uses a combination of supervised and unsupervised learning, but he also performs domain adaptation. Most of the researchers working on PP attachment seem to be satisfied with a PP attachment system; we have found hardly any work on integrating the results of such approaches into actual parsers. The only exceptions are Mehl et al. (1998) and Foth and Menzel (2006), both working with German data. Mehl et al. report a slight improvement of PP attachment from 475 correct PPs out of 681 PPs for the original parser to 481 PPs. Foth and Menzel report an improvement of overall accuracy from 90.7% to 92.2%. Both integrate statistical attachment preferences into a parser. First, we will investigate whether dependency parsing, which generally uses lexical information, shows the same performance on PP attachment as an independent PP attachment classifier does. Then we will investigate an approach that allows the integration of PP attachment information into the output of a parser without having to modify the parser: The results of an independent PP attachment classifier are integrated into the parse of a dependency parser for German in a postprocessing step.
The two papers included in this volume have developed from work with the CHILDES tools and the Media Editor in the two research projects, "Second language acquisition of German by Russian learners", sponsored by the Max Planck Institute for Psycholinguistics, Nijmegen, from 1998 to 1999 (directed by Ursula Stephany, University of Cologne, and Wolfgang Klein, Max Planck Institute for Psycholinguistics, Nijmegen) and "The age factor in the acquisition of German as a second language", sponsored by the German Science Foundation (DFG), Bonn, since 2000 (directed by Ursula Stephany, University of Cologne, and Christine Dimroth, Max Planck Institute for Psycholinguistics, Nijmegen). The CHILDES Project has been developed and is being continuously improved at Carnegie Mellon University, Pittsburgh, under the supervision of Brian MacWhinney. Having used the CHILDES tools for more than ten years for transcribing and analyzing Greek child data there it was no question that I would also use them for research into the acquisition of German as a second language and analyze the big amount of spontaneous speech gathered from two Russian girls with the help of the CLAN programs. When in the spring of 1997, Steven Gillis from the University of Antwerp (in collaboration with Gert Durieux) developed a lexicon-based automatic coding system based on the CLAN program MOR and suitable for coding languages with richer morphologies than English, such as Modern Greek. Coding huge amounts of data then became much quicker and more comfortable so that I decided to adopt this system for German as well. The paper "Working with the CHILDES Tools" is based on two earlier manuscripts which have grown out of my research on Greek child language and the many CHILDES workshops taught in Germany, Greece, Portugal, and Brazil over the years. Its contents have now been adapted to the requirements of research into the acquisition of German as a second language and for use on Windows.