Refine
Year of publication
- 2014 (19) (remove)
Document Type
- Conference Proceeding (19) (remove)
Has Fulltext
- yes (19)
Is part of the Bibliography
- yes (19) (remove)
Keywords
Publicationstate
Reviewstate
- Peer-Review (1)
In 2010, ISO published a standard for syntactic annotation, ISO 24615:2010 (SynAF). Back then, the document specified a comprehensive reference model for the representation of syntactic annotations, but no accompanying XML serialisation. ISO’s subcommittee on language resource management (ISO TC 37/SC 4) is working on making the SynAF serialisation ISOTiger an additional part of the standard. This contribution addresses the current state of development of ISOTiger, along with a number of open issues on which we are seeking community feedback in order to ensure that ISOTiger becomes a useful extension to the SynAF reference model.
We present a novel NLP resource for the explanation of linguistic phenomena, built and evaluated exploring very large annotated language corpora. For the compilation, we use the German Reference Corpus (DeReKo) with more than 5 billion word forms, which is the largest linguistic resource worldwide for the study of contemporary written German. The result is a comprehensive database of German genitive formations, enriched with a broad range of intra- und extralinguistic metadata. It can be used for the notoriously controversial classification and prediction of genitive endings (short endings, long endings, zero-marker). We also evaluate the main factors influencing the use of specific endings. To get a general idea about a factor’s influences and its side effects, we calculate chi-square-tests and visualize the residuals with an association plot. The results are evaluated against a gold standard by implementing tree-based machine learning algorithms. For the statistical analysis, we applied the supervised LMT Logistic Model Trees algorithm, using the WEKA software. We intend to use this gold standard to evaluate GenitivDB, as well as to explore methodologies for a predictive genitive model.
Hosting Providers play an essential role in the development of Internet services such as e-Research Infrastructures. In order to promote the development of such services, legislators on both sides of the Atlantic Ocean introduced “safe harbour” provisions to protect Service Providers (a category which includes Hosting Providers) from legal claims (e.g. of copyright infringement). Relevant provisions can be found in § 512 of the United States Copyright Act and in art. 14 of the Directive 2000/31/EC (and its national implementations). The cornerstone of this framework is the passive role of the Hosting Provider through which he has no knowledge of the content that he hosts. With the arrival of Web 2.0, however, the role of Hosting Providers on the Internet changed; this change has been reflected in court decisions that have reached varying conclusions in the last few years. The purpose of this article is to present the existing framework (including recent case law from the US, Germany and France).
Part-of-speech tagging (POS-tagging) of spoken data requires different means of annotation than POS-tagging of written and edited texts. In order to capture the features of German spoken language, a distinct tagset is needed to respond to the kinds of elements which only occur in speech. In order to create such a coherent tagset the most prominent phenomena of spoken language need to be analyzed, especially with respect to how they differ from written language. First evaluations have shown that the most prominent cause (over 50%) of errors in the existing automatized POS-tagging of transcripts of spoken German with the Stuttgart Tübingen Tagset (STTS) and the treetagger was the inaccurate interpretation of speech particles. One reason for this is that this class of words is virtually absent from the current STTS. This paper proposes a recategorization of the STTS in the field of speech particles based on distributional factors rather than semantics. The ultimate aim is to create a comprehensive reference corpus of spoken German data for the global research community. It is imperative that all phenomena are reliably recorded in future part-of-speech tag labels.
Machine learning methods offer a great potential to automatically investigate large amounts of data in the humanities. Our contribution to the workshop reports about ongoing work in the BMBF project KobRA (http://www.kobra.tu-dortmund.de) where we apply machine learning methods to the analysis of big corpora in language-focused research of computer-mediated communication (CMC). At the workshop, we will discuss first results from training a Support Vector Machine (SVM) for the classification of selected linguistic features in talk pages of the German Wikipedia corpus in DeReKo provided by the IDS Mannheim. We will investigate different representations of the data to integrate complex syntactic and semantic information for the SVM. The results shall foster both corpus-based research of CMC and the annotation of linguistic features in CMC corpora.
This contribution presents the procedure used in the Handbuch deutscher Kommunikationsverben and in its online version Kommunikationsverben in the lexicographical internet portal OWID to divide sets of semantically similar communication verbs into ever smaller sets of ever closer synonyms. Kommunikationsverben describes the meaning of communication verbs on two levels: a lexical level, represented in the dictionary entries and by sets of lexical features, and a conceptual level, represented by different types of situations referred to by specific types of verbs. The procedure starts at the conceptual level of meaning where verbs used to refer to the same specific situation type are grouped together. At the lexical level of meaning, the sets of verbs obtained from the first step are successively divided into smaller sets on the basis of the criteria of (i) identity of lexical meaning, (ii) identity of lexical features, and (iii) identity of contexts of usage. The stepwise procedure applied is shown to result in the creation of a semantic network for communication verbs.
This paper reports on an ongoing lexicographical project that investigates Polish loanwords from German that were further borrowed into the East Slavic languages Russian, Ukrainian, and Belorussian. The results will be published as three separate dictionaries in the Lehnwortportal Deutsch, a freely available web portal for loanword dictionaries having German as their common source language. On the database level, the portal models lexicographical data as a cross-resource directed acyclic graph of relations between individual words, including German ‘metalemmata’ as normalized representations of diasystemic variants of German etyma. Amongst other things, this technology makes it possible to use the web portal as an ‘inverted loanword dictionary’ to find loanwords in different languages borrowed from the same German etymon. The different possible pathways of German loanwords that went through Polish into the East Slavic languages can be represented directly as paths in the graph. A dedicated in-house dictionary editing software system assists lexicographers in producing and keeping track of these paths even in complex cases where, e.g, only a derivative of a German loanword in Polish has been borrowed into Russian. The paper concludes with some remarks on the particularities of the dictionary/portal access structure needed for presenting and searching borrowing chains.