Refine
Year of publication
Document Type
- Conference Proceeding (14)
- Part of a Book (7)
- Article (1)
Language
- English (22) (remove)
Has Fulltext
- yes (22)
Is part of the Bibliography
- no (22) (remove)
Keywords
- Annotation (22) (remove)
Publicationstate
- Veröffentlichungsversion (17)
- Postprint (5)
Reviewstate
- (Verlags)-Lektorat (22) (remove)
Publisher
The Stuttgart-Tübingen Tagset (STTS) is a widely used POS annotation scheme for German which provides 54 different tags for the analysis on the part of speech level. The tagset, however, does not distinguish between adverbs and different types of particles used for expressing modality, intensity, graduation, or to mark the focus of the sentence. In the paper, we present an extension to the STTS which provides tags for a more fine-grained analysis of modification, based on a syntactic perspective on parts of speech. We argue that the new classification not only enables us to do corpus-based linguistic studies on modification, but also improves statistical parsing. We give proof of concept by training a data-driven dependency parser on data from the TiGer treebank, providing the parser a) with the original STTS tags and b) with the new tags. Results show an improved labelled accuracy for the new, syntactically motivated classification.
This study examines what kind of cues and constraints for discourse interpretation can be derived from the logical and generic document structure of complex texts by the example of scientific journal articles. We performed statistical analysis on a corpus of scientific articles annotated on different annotations layers within the framework of XML-based multi-layer annotation. We introduce different discourse segment types that constrain the textual domains in which to identify rhetorical relation spans, and we show how a canonical sequence of text type structure categories is derived from the corpus annotations. Finally, we demonstrate how and which text type structure categories assigned to complex discourse segments of the type “block” statistically constrain the occurrence of rhetorical relation types.
This paper presents an extension to the Stuttgart-Tübingen TagSet, the standard part-of-speech tag set for German, for the annotation of spoken language. The additional tags deal with hesitations, backchannel signals, interruptions, onomatopoeia and uninterpretable material. They allow one to capture phenomena specific to spoken language while, at the same time, preserving inter-operability with already existing corpora of written language.
While written corpora can be exploited without any linguistic annotations, speech corpora need at least a basic transcription to be of any use for linguistic research. The basic annotation of speech data usually consists of time-aligned orthographic transcriptions. To answer phonetic or phonological research questions, phonetic transcriptions are needed as well. However, manual annotation is very time-consuming and requires considerable skill and near-native competence. Therefore it can take years of speech corpus compilation and annotation before any analyses can be carried out. In this paper, approaches that address the transcription bottleneck of speech corpus exploitation are presented and discussed, including crowdsourcing the orthographic transcription, automatic phonetic alignment, and query-driven annotation. Currently, query-driven annotation and automatic phonetic alignment are being combined and applied in two speech research projects at the Institut für Deutsche Sprache (IDS), whereas crowdsourcing the orthographic transcription still awaits implementation.
In this contribution, we report on an effort to annotate German data with information relevant to opinion inference. Such information has previously been referred to as effect or couched in terms of eventevaluation functors. We extend the theory and present an extensive scheme that combines both approaches and thus extends the set of inference-relevant predicates. Using these guidelines to annotate 726 German synsets, we achieve good inter-annotator agreement.
Recent studies focussed on the question whether less-configurational languages like German are harder to parse than English, or whether the lower parsing scores are an artefact of treebank encoding schemes and data structures, as claimed by Kübler et al. (2006). This claim is based on the assumption that PARSEVAL metrics fully reflect parse quality across treebank encoding schemes. In this paper we present new experiments to test this claim. We use the PARSEVAL metric, the Leaf-Ancestor metric as well as a dependency-based evaluation, and present novel approaches measuring the effect of controlled error insertion on treebank trees and parser output. We also provide extensive past-parsing crosstreebank conversion. The results of the experiments show that, contrary to Kübler et al. (2006), the question whether or not German is harder to parse than English remains undecided.
Annotating Discourse Relations in Spoken Language: A Comparison of the PDTB and CCR Frameworks
(2016)
In discourse relation annotation, there is currently a variety of different frameworks being used, and most of them have been developed and employed mostly on written data. This raises a number of questions regarding interoperability of discourse relation annotation schemes, as well as regarding differences in discourse annotation for written vs. spoken domains. In this paper, we describe ouron annotating two spoken domains from the SPICE Ireland corpus (telephone conversations and broadcast interviews) according todifferent discourse annotation schemes, PDTB 3.0 and CCR. We show that annotations in the two schemes can largely be mappedone another, and discuss differences in operationalisations of discourse relation schemes which present a challenge to automatic mapping. We also observe systematic differences in the prevalence of implicit discourse relations in spoken data compared to written texts,find that there are also differences in the types of causal relations between the domains. Finally, we find that PDTB 3.0 addresses many shortcomings of PDTB 2.0 wrt. the annotation of spoken discourse, and suggest further extensions. The new corpus has roughly theof the CoNLL 2015 Shared Task test set, and we hence hope that it will be a valuable resource for the evaluation of automatic discourse relation labellers.
Integrated Linguistic Annotation Models and Their Application in the Domain of Antecedent Detection
(2011)
Seamless integration of various, often heterogeneous linguistic resources in terms of their output formats and a combined analysis of the respective annotation layers are crucial tasks for linguistic research. After a decade of concentration on the development of formats to structure single annotations for specific linguistic issues, in the last years a variety of specifications to store multiple annotations over the same primary data has been developed. The paper focuses on the integration of the knowledge resource logical document structure information into a text document to enhance the task of automatic anaphora resolution both for the task of candidate detection and antecedent selection. The paper investigates data structures necessary for knowledge integration and retrieval.
Different Views on Markup
(2010)
In this chapter, two different ways of grouping information represented in document markup are examined: annotation levels, referring to conceptual levels of description, and annotation layers, referring to the technical realisation of markup using e.g. document grammars. In many current XML annotation projects, multiple levels are integrated into one layer, often leading to the problem of having to deal with overlapping hierarchies. As a solution, we propose a framework for XML-based multiple, independent XML annotation layers for one text, based on an abstract representation of XML documents with logical predicates. Two realisations of the abstract representation are presented, a Prolog fact base format together with an application architecture, and a specification for XML native databases. We conclude with a discussion of projects that have currently adopted this framework.
The paper discusses two topics: firstly an approach of using multiple layers of annotation is sketched out. Regarding the XML representation this approach is similar to standoff annotation. A second topic is the use of heterogeneous linguistic resources (e.g., XML annotated documents, taggers, lexical nets) as a source for semiautomatic multi-dimensional markup to resolve typical linguistic issues, dealing with anaphora resolution as a case study.
Making CONCUR work
(2005)
The SGML feature CONCUR allowed for a document to be simultaneously marked up in multiple conflicting hierarchical tagsets but validated and interpreted in one tagset at a time. Alas, CONCUR was rarely implemented, and XML does not address the problem of conflicting hierarchies at all. The MuLaX document syntax is a non-XML syntax that enables multiply-encoded hierarchies by distinguishing different “layers” in the hierarchy by adding a layer ID as a prefix to the element names. The IDs tie all the elements in a single hierarchy together in an “annotation layer”. Extraction of a single annotation layer results in a well-formed XML document, and each annotation layer may be associated with an XML schema. The MuLaX processing model works on the nodes of one annotation layer at a time through Xpath-like navigation. CONCUR lives!
This paper describes a corpus of Japanese task-oriented dialogues, i.e. its data, annotations, analysis methodology and preliminary results for the modeling of co-referential phenomena. Current corpus based approaches to co-reference concentrate on textual data from English or other European languages. Hence, the emerging language-general models of co-reference miss input from dialogue data of non-European languages. We aim to fill this gap and contribute to a model of co-reference on various language-specific and language-general levels.
This paper deals with the problem of how to interrelate theory-specific treebanks and how to transform one treebank format to another. Currently, two approaches to achieve these goals can be differentiated. The first creates a mapping algorithm between treebank formats. Categories of a source format are transformed into a target format via a given set of general or language-specific mapping rules. The second relates treebanks via a transformation to a general model of linguistic categories, for example based on the EAGLES recommendations for syntactic annotations of corpora, or relying on the HPSG framework. This paper proposes a new methodology as a solution for these desiderata.
The paper discusses from various angles the morphosyntactic annotation of DeReKo, the Archive of General Reference Corpora of Contemporary Written German at the Institut für Deutsche Sprache (IDS), Mannheim. The paper is divided into two parts. The first part covers the practical and technical aspects of this endeavor. We present results from a recent evaluation of tools for the annotation of German text resources that have been applied to DeReKo. These tools include commercial products, especially Xerox' Finite State Tools and the Machinese products developed by the Finnish company Connexor Oy, as well as software for which academic licenses are available free of charge for academic institutions, e.g. Helmut Schmid's Tree Tagger. The second part focuses on the linguistic interpretability of the corpus annotations and more general methodological considerations concerning scientifically sound empirical linguistic research. The main challenge here is that unlike the texts themselves, the morphosyntactic annotations of DeReKo do not have the status of observed data; instead they constitute a theory and implementation-dependent interpretation. In addition, because of the enormous size of DeReKo, a systematic manual verification of the automatic annotations is not feasible. In consequence, the expected degree of inaccuracy is very high, particularly wherever linguistically challenging phenomena, such as lexical or grammatical variation, are concerned. Given these facts, a researcher using the annotations blindly will run the risk of not actually studying the language but rather the annotation tool or the theory behind it. The paper gives an overview of possible pitfalls and ways to circumvent them and discusses the opportunities offered by using annotations in corpus-based and corpus-driven grammatical research against the background of a scientifically sound methodology.
This article shows that the TEI tag set for feature structures can be adopted to represent a heterogeneous set of linguistic corpora. The majority of corpora is annotated using markup languages that are based on the Annotation Graph framework, the upcoming Linguistic Annotation Format ISO standard, or according to tag sets defined by or based upon the TEI guidelines. A unified representation comprises the separation of conceptually different annotation layers contained in the original corpus data (e.g. syntax, phonology, and semantics) into multiple XML files. These annotation layers are linked to each other implicitly by the identical textual content of all files. A suitable data structure for the representation of these annotations is a multi-rooted tree that again can be represented by the TEI and ISO tag set for feature structures. The mapping process and representational issues are discussed as well as the advantages and drawbacks associated with the use of the TEI tag set for feature structures as a storage and exchange format for linguistically annotated data.
Research today is often performed in collaborated projects composed of project partners with different backgrounds and from different institutions and countries. Standards can be a crucial tool to help harmonizing these differences and to create sustainable resources. However, choosing a standard depends on having enough information to evaluate and compare different annotation and metadata formats. In this paper we present ongoing work on an interactive, collaborative website that collects information on standards in the field of linguistics as a means to guide interested researchers.
Discourse segmentation is the division of a text into minimal discourse segments, which form the leaves in the trees that are used to represent discourse structures. A definition of elementary discourse segments in German is provided by adapting widely used segmentation principles for English minimal units, while considering punctuation, morphology, sytax, and aspects of the logical document structure of a complex text type, namely scientific articles. The algorithm and implementation of a discourse segmenter based on these principles is presented, as well an evaluation of test runs.