A hybrid architecture for robust parsing of german

This paper provides an overview of current research on a hybrid and robust parsing architecture for the morphological, syntactic and semantic annotation of German text corpora. The novel contribution of this research lies not in the individual parsing modules, each of which relies on state-of-the-art algorithms and techniques. Rather what is new about the present approach is the combination of these modules into a single architecture. This combination provides a means to significantly optimize the performance of each component, resulting in an increased accuracy of annotation

Analyzing Middle High German syntax with RDF and SPARQL

The paper presents technological foundations for an empirical study of Middle High German (MHG) syntax. We aim to analyze the diachronic changes of MHG syntax on the example of direct and indirect object alterations in the middle field. In the absence of syntactically annotated corpora, we provide a rule-based shallow parser and an enrichment pipeline with the purpose of quantitative evaluation of a qualitative hypothesis. We provide a publicaly available enrichment and annotation pipeline grounded. A technologically innovative aspect is the application of CoNLL-RDF and SPARQL Update for parsing

A one-pass valency-oriented chunker for German

International audienceNon-finite state parsers provide fine-grained information. However, they are computationally demanding. Therefore, it is interesting to see how far a shallow parsing approach is able to go. In a pattern-based matching operation, the transducer described here consists of POS-tags using regular expressions that take advantage of the characteristics of German grammar. The process aims at finding linguistically relevant phrases with a good precision, which enables in turn an estimation of the actual valency of a given verb. The chunker reads its input exactly once instead of using cascades, which greatly benefits computational efficiency. This finite-state chunking approach does not return a tree structure, but rather yields various kinds of linguistic information useful to the language researcher. Possible applications include simulation of text comprehension on the syntactical level, creation of selective benchmarks and failure analysis

A Simple Method for Resolution of Definite Reference in a Shared Visual Context

Siebert A, Schlangen D. A Simple Method for Resolution of Definite Reference in a Shared Visual Context. In: Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue. Columbus, Ohio: Association for Computational Linguistics; 2008: 84-87

Language Acquisition and Language Pedagogy

Kapitel 3 von Teil 4 ("New Directions and Applications") des Sammelbandes

Concept-Based Retrieval from Critical Incident Reports

Background: Critical incident reporting systems (CIRS) are used as a means to collect anonymously entered information of incidents that occurred for example in a hospital. Analyzing this information helps to identify among others problems in the workflow, in the infrastructure or in processes. Objectives: The entire potential of these sources of experiential knowledge remains often unconsidered since retrieval of relevant reports and their analysis is difficult and time-consuming, and the reporting systems often do not provide support for these tasks. The objective of this work is to develop a method for retrieving reports from the CIRS related to a specific user query. Methods: atural language processing (NLP) and information retrieval (IR) methods are exploited for realizing the retrieval. We compare standard retrieval methods that rely upon frequency of words with an approach that includes a semantic mapping of natural language to concepts of a medical ontology. Results: By an evaluation, we demonstrate the feasibility of semantic document enrichment to improve recall in incident reporting retrieval. It is shown that a combination of standard keyword-based retrieval with semantic search results in highly satisfactory recall values. Conclusion: In future work, the evaluation should be repeated on a larger data set and real-time user evaluation need to be performed to assess user satisfactory with the system and results. Keywords. Information Retrieval, Data Mining, Natural Language Processing, Critical Incidents Reporting

Teaching the form-function mapping of German ‘prefield’ elements using Concept-Based Instruction

This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Empirical findings in Second Language Acquisition suggest that the basic structure of German declarative sentences, described in terms of topological fields, poses certain challenges to learners of German as a foreign language. The problem of multiple prefield elements, resulting in ungrammatical verb-third sentences, figures most prominently in the literature. While the so-called V2 constraint is usually treated as a purely formal feature of German syntax both in the empirical as well as in the pedagogical literature, the present paper adopts a usage-based perspective, viewing language as an inventory of form-function mappings. Basic functions of prefield elements have already been identified in research on textual grammar and information structure. This paper presents results from a pilot study with Japanese elementary learners of German as a foreign language, where the form-function mapping of German prefield elements was explicitly taught following the guidelines of an approach called Concept-Based Instruction. The findings indicate that, with a focus on the function-function mapping, it is in fact possible to explicitly teach these rather abstract regularities of German to beginning learners. The participants’ language production exhibits a prefield variation pattern similar to that of L1 German speakers; at the same time the learners produce very few ungrammatical verb-third sentences.Peer Reviewe

Integrating deep and shallow natural language processing components : representations and hybrid architectures

We describe basic concepts and software architectures for the integration of shallow and deep (linguistics-based, semantics-oriented) natural language processing (NLP) components. The main goal of this novel, hybrid integration paradigm is improving robustness of deep processing. After an introduction to constraint-based natural language parsing, we give an overview of typical shallow processing tasks. We introduce XML standoff markup as an additional abstraction layer that eases integration of NLP components, and propose the use of XSLT as a standardized and efficient transformation language for online NLP integration. In the main part of the thesis, we describe our contributions to three hybrid architecture frameworks that make use of these fundamentals. SProUT is a shallow system that uses elements of deep constraint-based processing, namely type hierarchy and typed feature structures. WHITEBOARD is the first hybrid architecture to integrate not only part-of-speech tagging, but also named entity recognition and topological parsing, with deep parsing. Finally, we present Heart of Gold, a middleware architecture that generalizes WHITEBOARD into various dimensions such as configurability, multilinguality and flexible processing strategies. We describe various applications that have been implemented using the hybrid frameworks such as structured named entity recognition, information extraction, creative document authoring support, deep question analysis, as well as evaluations. In WHITEBOARD, e.g., it could be shown that shallow pre-processing increases both coverage and efficiency of deep parsing by a factor of more than two. Heart of Gold not only forms the basis for applications that utilize semanticsoriented natural language analysis, but also constitutes a complex research instrument for experimenting with novel processing strategies combining deep and shallow methods, and eases replication and comparability of results.Diese Arbeit beschreibt Grundlagen und Software-Architekturen für die Integration von flachen mit tiefen (linguistikbasierten und semantikorientierten) Verarbeitungskomponenten für natürliche Sprache. Das Hauptziel dieses neuartigen, hybriden Integrationparadigmas ist die Verbesserung der Robustheit der tiefen Verarbeitung. Nach einer Einführung in constraintbasierte Analyse natürlicher Sprache geben wir einen Überblick über typische Aufgaben flacher Sprachverarbeitungskomponenten. Wir führen XML Standoff-Markup als zusätzliche Abstraktionsebene ein, mit deren Hilfe sich Sprachverarbeitungskomponenten einfacher integrieren lassen. Ferner schlagen wir XSLT als standardisierte und effiziente Transformationssprache für die Online-Integration vor. Im Hauptteil der Arbeit stellen wir unsere Beiträge zu drei hybriden Architekturen vor, welche auf den beschriebenen Grundlagen aufbauen. SProUT ist ein flaches System, das Elemente tiefer Verarbeitung wie Typhierarchie und getypte Merkmalsstrukturen nutzt. WHITEBOARD ist das erste System, welches nicht nur Part-of-speech-Tagging, sondern auch Eigennamenerkennung und flaches topologisches Parsing mit tiefer Verarbeitung kombiniert. Schließlich wird Heart of Gold vorgestellt, eine Middleware-Architektur, welche WHITEBOARD hinsichtlich verschiedener Dimensionen wie Konfigurierbarkeit, Mehrsprachigkeit und Unterstützung flexibler Verarbeitungsstrategien generalisiert. Wir beschreiben verschiedene, mit Hilfe der hybriden Architekturen implementierte Anwendungen wie strukturierte Eigennamenerkennung, Informationsextraktion, Kreativitätsunterstützung bei der Dokumenterstellung, tiefe Frageanalyse, sowie Evaluationen. So konnte z.B. in WHITEBOARD gezeigt werden, dass durch flache Vorverarbeitung sowohl Abdeckung als auch Effizienz des tiefen Parsers mehr als verdoppelt werden. Heart of Gold bildet nicht nur Grundlage für semantikorientierte Sprachanwendungen, sondern stellt auch eine wissenschaftliche Experimentierplattform für weitere, neuartige Kombinationsstrategien dar, welche zudem die Replizierbarkeit und Vergleichbarkeit von Ergebnissen erleichtert