What are natural concepts? A design perspective

Conceptual spaces have become an increasingly popular modeling tool in cognitive psychology. The core idea of the conceptual spaces approach is that concepts can be represented as regions in similarity spaces. While it is generally acknowledged that not every region in such a space represents a natural concept, it is still an open question what distinguishes those regions that represent natural concepts from those that do not. The central claim of this paper is that natural concepts are represented by the cells of an optimally designed similarity space

On semantic differences: a multivariate corpus-based study of the semantic field of inchoativity in translated and non-translated Dutch

This dissertation places the study of semantic differences in translation compared to non-translation at the centre of its concerns. To date, much research in Corpus-based Translation Studies has focused on lexical and grammatical phenomena in an attempt to reveal presumed general tendencies of translation. On the semantic level, these general tendencies have rarely been investigated. Therefore, the goal of this study is to explore whether universal tendencies of translation also exist on the semantic level, thereby connecting the framework of translation universals to semantics

Exploring the field of inchoativity

Although the notion of meaning has always been at the core of translation, the invariance of meaning has, partly due to practical constraints, rarely been challenged in Corpus-based Translation Studies. In answer to this, the aim of this book is to question the invariance of meaning in translated texts: if translation scholars agree on the fact that translated language is different from non-translated language with respect to a number of grammatical and lexical aspects, would it be possible to identify differences between translated and non-translated language on the semantic level too? More specifically, this books tries to formulate an answer to the following three questions: (i) how can semantic differences in translated vs non-translated language be investigated in a corpus-based study?, (ii) are there any differences on the semantic level between translated and non-translated language? and (iii) if there are differences on the semantic level, can we ascribe them to any of the (universal) tendencies of translation? In this book, I establish a way to visually explore semantic similarity on the basis of representations of translated and non-translated semantic fields. A technique for the comparison of semantic fields of translated and non-translated language called SMM++ (based on Helge Dyvik’s Semantic Mirrors method) is developed, yielding statistics-based visualizations of semantic fields. The SMM++ is presented via the case of inchoativity in Dutch (beginnen [to begin]). By comparing the visualizations of the semantic fields on different levels (translated Dutch with French as a source language, with English as a source language and non-translated Dutch) I further explore whether the differences between translated and non-translated fields of inchoativity in Dutch can be linked to any of the well-known universals of translation. The main results of this study are explained on the basis of two cognitively inspired frameworks: Halverson’s Gravitational Pull Hypothesis and Paradis’ neurolinguistic theory of bilingualism

Learning Language from a Large (Unannotated) Corpus

A novel approach to the fully automated, unsupervised extraction of dependency grammars and associated syntax-to-semantic-relationship mappings from large text corpora is described. The suggested approach builds on the authors' prior work with the Link Grammar, RelEx and OpenCog systems, as well as on a number of prior papers and approaches from the statistical language learning literature. If successful, this approach would enable the mining of all the information needed to power a natural language comprehension and generation system, directly from a large, unannotated corpus.Comment: 29 pages, 5 figures, research proposa

Enhancing information retrieval in folksonomies using ontology of place constructed from Gazetteer information

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesFolksonomy (from folk and taxonomy) is an approach to user metadata creation where users describe information objects with a free-form list of keywords (‘tags’). Folksonomy has have proved to be a useful information retrieval tool that support the emergence of “collective intelligence” or “bottom-up” light weight semantics. Since there are no guiding rules or restrictions on the users, folksonomy has some drawbacks and problems as lack of hierarchy, synonym control, and semantic precision. This research aims at enhancing information retrieval in folksonomy, particularly that of location information, by establishing explicit relationships between place name tags. To accomplish this, an automated approach is developed. The approach starts by retrieving tags from Flickr. The tags are then filtered to identify those that represent place names. Next, the gazetteer service that is a knowledge organization system for spatial information is used to query for the place names. The result of the search from the gazetteer and the feature types are used to construct an ontology of place. The ontology of place is formalized from place name concepts, where each place has a “Part-Of” relationship with its direct parent. The ontology is then formalized in OWL (Web Ontology Language). A search tool prototype is developed that extracts a place name and its parent name from the ontology and use them for searching in Flickr. The semantic richness added to Flickr search engine using our approach is tested and the results are evaluated

Semantic differences in translation

Although the notion of meaning has always been at the core of translation, the invariance of meaning has, partly due to practical constraints, rarely been challenged in Corpus-based Translation Studies. In answer to this, the aim of this book is to question the invariance of meaning in translated texts: if translation scholars agree on the fact that translated language is different from non-translated language with respect to a number of grammatical and lexical aspects, would it be possible to identify differences between translated and non-translated language on the semantic level too? More specifically, this books tries to formulate an answer to the following three questions: (i) how can semantic differences in translated vs non-translated language be investigated in a corpus-based study?, (ii) are there any differences on the semantic level between translated and non-translated language? and (iii) if there are differences on the semantic level, can we ascribe them to any of the (universal) tendencies of translation? In this book, I establish a way to visually explore semantic similarity on the basis of representations of translated and non-translated semantic fields. A technique for the comparison of semantic fields of translated and non-translated language called SMM++ (based on Helge Dyvik’s Semantic Mirrors method) is developed, yielding statistics-based visualizations of semantic fields. The SMM++ is presented via the case of inchoativity in Dutch (beginnen [to begin]). By comparing the visualizations of the semantic fields on different levels (translated Dutch with French as a source language, with English as a source language and non-translated Dutch) I further explore whether the differences between translated and non-translated fields of inchoativity in Dutch can be linked to any of the well-known universals of translation. The main results of this study are explained on the basis of two cognitively inspired frameworks: Halverson’s Gravitational Pull Hypothesis and Paradis’ neurolinguistic theory of bilingualism

Bridging the semantic gap in content-based image retrieval.

To manage large image databases, Content-Based Image Retrieval (CBIR) emerged as a new research subject. CBIR involves the development of automated methods to use visual features in searching and retrieving. Unfortunately, the performance of most CBIR systems is inherently constrained by the low-level visual features because they cannot adequately express the user\u27s high-level concepts. This is known as the semantic gap problem. This dissertation introduces a new approach to CBIR that attempts to bridge the semantic gap. Our approach includes four components. The first one learns a multi-modal thesaurus that associates low-level visual profiles with high-level keywords. This is accomplished through image segmentation, feature extraction, and clustering of image regions. The second component uses the thesaurus to annotate images in an unsupervised way. This is accomplished through fuzzy membership functions to label new regions based on their proximity to the profiles in the thesaurus. The third component consists of an efficient and effective method for fusing the retrieval results from the multi-modal features. Our method is based on learning and adapting fuzzy membership functions to the distribution of the features\u27 distances and assigning a degree of worthiness to each feature. The fourth component provides the user with the option to perform hybrid querying and query expansion. This allows the enrichment of a visual query with textual data extracted from the automatically labeled images in the database. The four components are integrated into a complete CBIR system that can run in three different and complementary modes. The first mode allows the user to query using an example image. The second mode allows the user to specify positive and/or negative sample regions that should or should not be included in the retrieved images. The third mode uses a Graphical Text Interface to allow the user to browse the database interactively using a combination of low-level features and high-level concepts. The proposed system and ail of its components and modes are implemented and validated using a large data collection for accuracy, performance, and improvement over traditional CBIR techniques