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In recent years, the availability of large annotated and searchable corpora, together with a new interest in the empirical foundation and validation of linguistic theory and description, has sparked a surge of novel and interesting work using corpus-based methods to study the grammar of natural languages. However, a look at relevant current research on the grammar of the Germanic, Romance, and Slavic languages reveals a variety of different theoretical approaches and empirical foci, which can be traced back to different philological and linguistic traditions. Still, this current state of affairs should not be seen as an obstacle but as an ideal basis for a fruitful exchange of ideas between different research paradigms.
The variation of the strong genitive marker of the singular noun has been treated by diverse accounts. Still there is a consensus that it is to a large extent systematic but can be approached appropriately only if many heterogeneous factors are taken into account. Over thirty variables influencing this variation have been proposed. However, it is actually unclear how effective they can be, and above all, how they interact. In this paper, the potential influencing variables are evaluated statistically in a machine learning approach and modelled in decision trees in order to predict the genitive marking variants. Working with decision trees based exclusively on statistically significant data enables us to determine what combination of factors is decisive in the choice of a marking variant of a given noun. Consequently the variation factors can be assessed with respect to their explanatory power for corpus data and put in a hierarchized order.
Grammar and corpora 2016
(2018)
In recent years, the availability of large annotated and searchable corpora, together with a new interest in the empirical foundation and validation of linguistic theory and description, has sparked a surge of novel and interesting work using corpus-based methods to study the grammar of natural languages. However, a look at relevant current research on the grammar of the Germanic, Romance, and Slavic languages reveals a variety of different theoretical approaches and empirical foci, which can be traced back to different philological and linguistic traditions. Still, this current state of affairs should not be seen as an obstacle but as an ideal basis for a fruitful exchange of ideas between different research paradigms.
Notions such as “corpus-driven” versus “theory-driven” bring into focus the specific role of corpora in linguistic research. As for phonology with its intrinsic focus on abstract categorical representation, there is a question of how a strictly corpus-driven approach can yield insight into relevant structures. Here we argue for a more theory-driven approach to phonology based on the concept of a phonological grammar in terms of interacting constraints. Empirical validation of such grammars comes from the potential convergence of the evidence from various sources including typological data, neutralization patterns, and in particular patterns observed in the creative use of language such as acronym formation, loanword adaptation, poetry, and speech errors. Further empirical validation concerns specific predictions regarding phonetic differences among opposition members, paradigm uniformity effects, and phonetic implementation in given segmental and prosodic contexts. Corpora in the narrowest sense (i.e. “raw” data consisting of spontaneous speech produced in natural settings) are useful for testing these predictions, but even here, special purpose-built corpora are often necessary.
Our corpus study is concerned with subject-verb agreement in contemporary German, more precisely the variation in verb number. We focus on subjects consisting of noun phrases coordinated by the conjunction und (‘and’). In our samples, both nouns are in singular. Number resolution – i.e., plural verb despite of the singular nouns – can be regarded as the default choice in contemporary German. However, our data show that eliding the second determiner in the subject enhances the probability of using the singular verb. This ellipsis effect is highly significant in German and Austrian texts. It seems to be weaker in Swiss texts. Regression analyses reveal that the ellipsis effect is stronger than both the highly significant influence of subject individuation and the significant effect of subject agentivity.
In this paper, we present our approach to automatically extracting German terminology in the domain of grammar using texts from the online information system grammis as our corpus. We analyze existing repositories of German grammatical terminology and develop Part-of-speech patterns for our extraction thereby showing the importance of unigrams in this domain. We contrast the results of the automatic extraction with a manually extracted standard. By comparing the performance of well-known statistical measures, we show how measures based on corpus comparison outperform alternative methods.