Open Access

It is of interest to study sentence construction for children’s writing in order to understand grammatical errors and their influence on didactic decisions. For this purpose, this paper analyses sentence structures for various age groups of children’s writings in contrast to text taken from children’s and youth literature. While valency differs little between text type and age group, sentence embellishments show some differences. Both use of adjectives and adverbs increase with age and book levels. Furthermore books show a larger use thereof. This work presents one of the steps in a larger ongoing effort to understand children’s writing and reading competences at word and sentence level. The need to look at variable from non-variable features of sentence structures separately in order to find distinctive features has been an important finding.

This paper presents Atomic, an open-source platform-independent desktop application for multi-level corpus annotation. Atomic aims at providing the linguistic community with a user-friendly annotation tool and sustainable platform through its focus on extensibility, a generic data model, and compatibility with existing linguistic formats. It is implemented on top of the Eclipse Rich Client Platform, a pluggable Java-based framework for creating client applications. Atomic - as a set of plug-ins for this framework - integrates with the platform and allows other researchers to develop and integrate further extensions to the software as needed. The generic graph-based meta model Salt serves as Atomic’s domain model and allows for unlimited annotation levels and types. Salt is also used as an intermediate model in the Pepper framework for conversion of linguistic data, which is fully integrated into Atomic, making the latter compatible with a wide range of linguistic formats. Atomic provides tools for both less experienced and expert annotators: graphical, mouse-driven editors and a command-line data manipulation language for rapid annotation.

This paper will have a holistic view at the field of corpus-based linguistic typology and present an overview of current advances at Leipzig University. Our goal is to use automatically created text data for a large variety of languages for quantitative typological investigations. In our approaches we utilize text corpora created for several hundred languages for cross-language quantitative studies using mathematically well-founded methods (Cysouw, 2005). These analyses include the measurement of textual characteristics. Basic requirements for the use of these parameters are also discussed. The measured values are then utilized for typological studies. Using quantitative methods, correlations of measured properties of corpora among themselves or with classical typological parameters are detected. Our work can be considered as an automatic and language-independent process chain, thus allowing extensive investigations of the various languages of the world.

The paper proposes a meta language model that can dynamically incorporate the influence of wider discourse context. The model provides a conditional probability in forms of P (text|context), where the context can be arbitrary length of text, and is used to influence the probability distribution over documents. A preliminary evaluation using a 3-gram model as the base language model shows significant reductions in perplexity by incorporating discourse context.

Challenging the assumption that traditional whitespace/punctuation-based tokenisation is the best solution for any NLP application, I propose an alternative approach to segmenting text into processable units. The proposed approach is nearly knowledge-free, in that it does not rely on language-dependent, man-made resources. The text segmentation approach is applied to the task of automated error reduction in texts with high noise. The results are compared to conventional tokenisation.