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The project Referenzkorpus Altdeutsch (‘Old German Reference Corpus’) aims to es- tablish a deeply-annotated text corpus of all extant Old German texts. As the automated part-of-speech and morphological pre-annotation is amended by hand, a quality control system for the results seems a desirable objective. To this end, standardized inflectional forms, generated using the morphological information, are compared with the attested word forms. Their creation is described by way of example for the Old High German part of the corpus. As is shown, in a few cases, some features of the attested word forms are also required in order to determine as exactly as possible the shape of the inflected lemma form to be created.
The availability of electronic corpora of historical stages of languages has been wel- comed as possibly attenuating the inherent problem of diachronic linguistics, i.e. that we only have access to what has chanced to come down to us - the problem which was memorably named by Labov (1992) as one of “Bad Data”. However, such corpora can only give us access to an increased amount ot historical material and this can essentially still only be a partial and possibly distorted picture of the actual language at a particular period of history. Corpora can be improved by taking a more representative sample of extant texts if these are available (as they are in significant number for periods after the invention of printing). But, as examples from the recently compiled GerManC corpus of seventeenth and eighteenth century German show, the evidence from such corpora can still fail to yield definitive answers to our questions about earlier stages of a language. The data still require expert interpretation, and it is important to be realistic about what can legitimately be expected from an electronic historical corpus.
Multi-faceted alignment. Toward automatic detection of textual similarity in Gospel-derived texts
(2015)
Ancient Germanic Bible-derived texts stand in as test material for producing computational means for automatically determining where textual contamination and linguistic interference have influenced the translation process. This paper reports on the results of research efforts that produced a text corpus; a method for decomposing the texts involved into smaller, more directly comparable thematically-related chunks; a database of relationships between these chunks; and a user-interface allowing for searches based on various referential criteria. Finally, the state of the product at the end of the project is discussed, namely as it was handed over to another researcher who has extended it to automatically find semantic and syntactic similarities within comparable chunks.
In this paper we present some preliminary considerations concerning the possibility of automatic parsing an annotated corpus for N-N compounds. This should in prin- ciple be possible at least for relational and stereotype compounds, if the lemmatization of the corpus connects the lemmata with lexical entries as described in Höhle (1982). These lexical entries then supply the necessary information about the argument structure of a relational noun or about the stereotypical purpose associated with the noun’s referent which can be used to establish a relation between the first and the head constituent of the compound.
The relative order of dative and accusative objects in older German is less free than it is today. The reason for this could be that speakers of the direct predecessor of Old High German organized the referents according to the Thematic Hierarchy. If one applies a Case Hierarchy Nom>Acc>Dat to this, the order Nom - Dat - Acc falls out. It becomes apparent that the status of the Thematic Hierarchy is not a factor governing underlying word order, but a factor inducing scrambling. Arguments from binding theory, whose validity is discussed, indicate that the underlying order is ‘accusative before dative’
Corpus-based identification and disambiguation of reading indicators for German nominalizations
(2010)
Corpus data is often structurally and lexically ambiguous; corpus extraction methodologies thus must be made aware of ambiguities. Therefore, given an extraction task, all relevant ambiguities must be identified. To resolve these ambiguities, contextual data responsible for one or another reading is to be considered. In the context of our present work, German -ung-nominalizations and their sortal readings are under examination. A number of these nominalizations may be read as an event or a result, depending on the semantic group they belong to. Here, we concentrate on nominalizations of verbs of saying (henceforth: "verba dicendi"), identify their context partners and their influence on the sortal reading of the nominalizations in question. We present a tool which calculates the sortal reading of such nominalizations and thus may improve not only corpus extraction, but also e.g. machine translation. Lastly, we describe successful attempts to identify the correct sortal reading, conclusions and future work.
So far, Sepedi negations have been considered more from the point of view of lexicographical treatment. Theoretical works on Sepedi have been used for this purpose, setting as an objective a neat description of these negations in a (paper) dictionary. This paper is from a different perspective: instead of theoretical works, corpus linguistic methods are used: (1) a Sepedi corpus is examined on the basis of existing descriptions of the occurrences of a relevant verb, looking at its negated forms from a purely prescriptive point of view; (2) a "corpus-driven" strategy is employed, looking only for sequences of negation particles (or morphemes) in order to list occurring constructions, without taking into account the verbs occurring in them, apart from their endings. The approach in (2) is only intended to show a possible methodology to extend existing theories on occurring negations. We would also like to try to help lexicographers to establish a frequency-based order of entries of possible negation forms in their dictionaries by showing them the number of respective occurrences. As with all corpus linguistic work, however, we must regard corpus evidence not as representative, but as tendencies of language use that can be detected and described. This is especially true for Sepedi, for which only few and small corpora exist. This paper also describes the resources and tools used to create the necessary corpus and also how it was annotated with part of speech and lemmas. Exploring the quality of available Sepedi part-of-speech taggers concerning verbs, negation morphemes and subject concords may be a positive side result.
This paper describes the application of probabilistic part of speech taggers to the Dzongkha language. A tag set containing 66 tags is designed, which is based on the Penn Treebank. A training corpus of 40,247 tokens is utilized to train the model. Using the lexicon extracted from the training corpus and lexicon from the available word list, we used two statistical taggers for comparison reasons. The best result achieved was 93.1% accuracy in a 10-fold cross validation on the training set. The winning tagger was thereafter applied to annotate a 570,247 token corpus.
In this article, we examine the current situation of data dissemination and provision for CMC corpora. By that we aim to give a guiding grid for future projects that will improve the transparency and replicability of research results as well as the reusability of the created resources. Based on the FAIR guiding principles for research data management, we evaluate the 20 European CMC corpora listed in the CLARIN CMC Resource family, individuate successful strategies among the existing corpora and establish best practices for future projects. We give an overview of existing approaches to data referencing, dissemination and provision in European CMC corpora, and discuss the methods, formats and strategies used. Furthermore, we discuss the need for community standards and offer recommendations for best practices when creating a new CMC corpus.
This paper describes a method for extracting collocation data from text corpora based on a formal definition of syntactic structures, which takes into account not only the POS-tagging level of annotation but also syntactic parsing (syntactic treebank model) and introduces the possibility of controlling the canonical form of extracted collocations based on statistical data on forms with different properties in the corpus. Specifically, we describe the results of extraction from the syntactically tagged Gigafida 2.1 corpus. Using the new method, 4,002,918 collocation candidates in 81 syntactic structures were extracted. We evaluate the extracted data sample in more detail, mainly in relation to properties that affect the extraction of canonical forms: definiteness in adjectival collocations, grammatical number in noun collocations, comparison in adjectival and adverbial collocations, and letter case (uppercase and lowercase) in canonical forms. The conclusion highlights the potential of the methodology used for the grammatical description of collocation and phrasal syntax and the possibilities for improving the model in the process of compilation of a digital dictionary database for Slovene.