A gestural repertoire of 1-2year old human children : in search of the ape gestures

This project was made possible with the generous financial help of the Baverstock Bequest to the Psychology and Neuroscience Department at the University of St Andrews.When we compare human gestures to those of other apes, it looks at first like there is nothing much to compare at all. In adult humans, gestures are thought to be a window into the thought processes accompanying language, and sign languages are equal to spoken language with all of its features. While some research firmly emphasises the difference between human gestures and those of other apes, the question about whether there are any commonalities has rarely been investigated, and is mostly confined to pointing gestures. The gestural repertoires of nonhuman ape species have been carefully studied and described with regard to their form and function – but similar approaches are much rarer in the study of human gestures. This paper applies the methodology commonly used in the study of nonhuman ape gestures to the gestural communication of human children in their second year of life. We recorded (n=13) children’s gestures in a natural setting with peers and caregivers in Germany and Uganda. Children employed 52 distinct gestures, 46 (89%) of which are present in the chimpanzee repertoire. Like chimpanzees, they used them both singly, and in sequences; and employed individual gestures flexibly towards different goals.Publisher PDFPeer reviewe

Disambiguierung von Wortbedeutungen mit GermaNet

The subject of this dissertation is boosting research on word sense disambiguation (WSD) for German. WSD is a very active area of research in computational linguistics, but most of the work is focused on English. One of the factors that has hampered WSD research for other languages such as German is the lack of appropriate resources, particularly in the form of sense-annotated corpus data. Hence, this work inevitably has to start with the preparation of resources before actual WSD experiments can be performed. The work program is fourfold. Firstly, since sense definitions are necessary to distinguish word senses (both for humans and for automatic WSD algorithms), the German wordnet GermaNet is (semi-)automatically extended with sense descriptions. This is done by automatically mapping GermaNet senses to descriptions in the online dictionary Wiktionary. Secondly, since the availability of sense-annotated corpora is a prerequisite for evaluating and developing word sense disambiguation systems, two GermaNet sense-annotated corpora are constructed. One corpus is automatically constructed and the other corpus is manually sense-annotated. Thirdly, several knowledge-based WSD algorithms are applied and evaluated -- using the newly created sense-annotated corpora. These algorithms are based on a suite of semantic relatedness measures, including path-based, information-content-based, and gloss-based methods. Experiments on gloss-based methods also employ the newly harvested definitions from Wiktionary. Fourthly, several supervised machine learning classifiers are applied to the task of German WSD, including rule-based methods, instance-based methods, probabilistic methods, and support vector machines. The classifiers rely on a wide range of machine learning features and their evaluation focuses on several aspects, including a comparison of several algorithms, a detailed analysis of the implemented features, and an investigation of the influence of syntax and semantics on the disambiguation performance for verbs

Consistency of Manual Sense Annotation and Integration into the TüBa-D/Z Treebank

<p>Since sense-annotated corpora serve as gold standards for the development, training, and evaluation of word sense disambiguation (WSD) systems, their availability is a necessary prerequisite for WSD. The purpose of the present paper is to describe the manual annotation of a selected set of lemmas in the TüBa-D/Z treebank [9, 21] with senses from the German wordnet GermaNet [7, 8]. With the sense annotation for a selected set of 109 words (30 nouns and 79 verbs) occurring together 17 910 times in the TüBa-D/Z, the treebank currently represents the largest manually sense-annotated corpus available for GermaNet.</p><p>This paper describes the annotation process, presents statistics, analyzes inter-annotator agreement (and disagreement), and documents the technical integration of the sense annotations into the most recent release 9.1 of the TüBa-D/Z. The publication of this paper is accompanied by making the described sense annotations available to the research community.</p&gt