Proceedings of the Workshop Semantic Content Acquisition and Representation (SCAR) 2007

This is the proceedings of the Workshop on Semantic Content Acquisition and Representation, held in conjunction with NODALIDA 2007, on May 24 2007 in Tartu, Estonia.</p

Multimodal Grounding for Language Processing

This survey discusses how recent developments in multimodal processing facilitate conceptual grounding of language. We categorize the information flow in multimodal processing with respect to cognitive models of human information processing and analyze different methods for combining multimodal representations. Based on this methodological inventory, we discuss the benefit of multimodal grounding for a variety of language processing tasks and the challenges that arise. We particularly focus on multimodal grounding of verbs which play a crucial role for the compositional power of language.Comment: The paper has been published in the Proceedings of the 27 Conference of Computational Linguistics. Please refer to this version for citations: https://www.aclweb.org/anthology/papers/C/C18/C18-1197

Ergativity and depth of analysis

In this paper, I argue that “depth of analysis” does not deserve the prestige that it is sometimes given in general linguistics. While language description should certainly be as detailed as possible, general linguistics must rely on worldwide comparison of languages, and this cannot be based on language-particular analyses. Rigorous quantitative comparison requires uniform measurement, and this implies abstracting away from many language-particular peculiarities. I will illustrate this on the basis of ergative patterns, starting out from I.A. Mel’čuk’s (1981) proposal for Lezgian. This proposal was not successful, but why not? And why is Baker’s (2015) theory of dependent case likewise unsuccessful? By contrast, quantitative worldwide research has found striking similarities of ergative coding patterns, which can be explained by the efficiency theory of asymmetric coding. I will argue that this success is due to a more cautious approach to understanding Human Language, which does not rely on the Mendeleyevian vision for grammar (that all grammars are made from the same innate building blocks)

Can humain association norm evaluate latent semantic analysis?

This paper presents the comparison of word association norm created by a psycholinguistic experiment to association lists generated by algorithms operating on text corpora. We compare lists generated by Church and Hanks algorithm and lists generated by LSA algorithm. An argument is presented on how those automatically generated lists reflect real semantic relations

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot

Herstellung eines Phaffia rhodozyma : Stamms mit verstärkter Astaxanthin-Synthese über gezielte genetische Modifikation chemisch mutagenisierter Stämme

Ziel dieser Arbeit war es erstmals durch eine Kombination aus chemischer Mutagenese und gezielter genetischer Modifikation (hier: „metabolic engineering“) einen Phaffia-Stamm herzustellen, welcher über die Mutagenese hinaus über eine weiter verstärkte Astaxanthin-Synthese verfügt. Die von „DSM Nutritional Products“ bereitgestellten chemischen Mutanten wurden analysiert und über einen Selektionsprozess auf Pigmentstabilität und Wachstum hin optimiert, da die Stämme aus cryogenisierter Dauerkultur starke Pigmentinstabilitäten und ein verzögertes Wachstum aufwiesen. Über eine exploratorische Phase wurde die Carotinoidsynthese analysiert und festgestellt, dass in den Mutanten keine Einzelreaktionen betroffen sind, welche für die Heraufregulierung der Carotinoidsynthese in den Mutanten verantwortlich sind. Hierbei wurden Limitierungen identifiziert und diese durch Transformation von Expressionsplasmiden mit geeigneten Genen aufgehoben, um damit eine noch effizientere Metabolisierung von Astaxanthin-Vorstufen hin zu Astaxanthin zu erreichen. Eine Überexpression der Phytoensynthase/Lycopinzyklase crtYB resultierte in einem gesteigerten Carotinoidgehalt bei gleichbleibendem Astaxanthin- Anteil. Durch eine zweite Transformation mit einer Expressionskassette für die Astaxanthin-Synthase asy konnte der Carotinoidgehalt weiter gesteigert und zusätzlich eine Limitierung der Metabolisierung von Astaxanthin-Vorstufen behoben werden, sodass die Transformante nahezu alle Intermediate der Astaxanthinsynthese zu Astaxanthin metabolisieren konnte (Gassel et al. 2013). Es konnte gezeigt werden, dass auch in den Mutanten, aus Experimenten mit dem Wildtyp bekannte, Limitierungen identifiziert und ausgeglichen werden konnten

Typological parameters of genericity

Different languages employ different morphosyntactic devices for expressing genericity. And, of course, they also make use of different morphosyntactic and semantic or pragmatic cues which may contribute to the interpretation of a sentence as generic rather than episodic. [...] We will advance the strong hypo thesis that it is a fundamental property of lexical elements in natural language that they are neutral with respect to different modes of reference or non-reference. That is, we reject the idea that a certain use of a lexical element, e.g. a use which allows reference to particular spatio-temporally bounded objects in the world, should be linguistically prior to all other possible uses, e.g. to generic and non-specific uses. From this it follows that we do not consider generic uses as derived from non-generic uses as it is occasionally assumed in the literature. Rather, we regard these two possibilities of use as equivalent alternative uses of lexical elements. The typological differences to be noted therefore concern the formal and semantic relationship of generic and non-generic uses to each other; they do not pertain to the question of whether lexical elements are predetermined for one of these two uses. Even supposing we found a language where generic uses are always zero-marked and identical to lexical sterns, we would still not assume that lexical elements in this language primarily have a generic use from which the non-generic uses are derived. (Incidentally, none of the languages examined, not even Vietnamese, meets this criterion.