Positioning for conceptual development using latent semantic analysis

With increasing opportunities to learn online, the problem of positioning learners in an educational network of content offers new possibilities for the utilisation of geometry-based natural language processing techniques. In this article, the adoption of latent semantic analysis (LSA) for guiding learners in their conceptual development is investigated. We propose five new algorithmic derivations of LSA and test their validity for positioning in an experiment in order to draw back conclusions on the suitability of machine learning from previously accredited evidence. Special attention is thereby directed towards the role of distractors and the calculation of thresholds when using similarities as a proxy for assessing conceptual closeness. Results indicate that learning improves positioning. Distractors are of low value and seem to be replaceable by generic noise to improve threshold calculation. Furthermore, new ways to flexibly calculate thresholds could be identified

SVO triple based Latent Semantic Analysis for recognising textual entailment

Burek G, Pietsch C, De Roeck A. SVO triple based Latent Semantic Analysis for recognising textual entailment. In: Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing (WTEP). Association for Computational Linguistics; 2007: 113-118.Latent Semantic Analysis has only recently been applied to textual entailment recognition. However, these efforts have suffered from inadequate bag of words vector representations. Our prototype implementation for the Third Recognising Textual Entailment Challenge (RTE-3) improves the approach by applying it to vector representations that contain semi-structured representations of words. It uses variable size n-grams of word stems to model independently verbs, subjects and objects displayed in textual statements. The system performance shows positive results and provides insights about how to improve them further

Hybrid mappings of complex questions over an integrated semantic space

We address the issue of measuring semantic similarity between ontologies and text by means of applying Latent Semantic Analysis. This method allows ranking of vector representations describing semantic relations according to their cosine similarity with a particular query. Our work is expected to make contributions including the introduction of reasoning about uncertainty when mapping between ontologies, an algorithm that can perform automatic mapping between concepts or relations derived from text and concepts or relations belonging to different ontologies, and the capability to infer implicit similarity between concepts or relations

The Language Technologies for Lifelong Learning Project

Berlanga, A. J., Van Rosmalen, P., Trausan-Matu, S., Monachesi, P., & Burek, G. (2009). The Language Technologies for Lifelong Learning Project. In I. Aedo., N. Chen, Kinshuk, D. Sampson & L. Zaitseva (Eds.), Proceedings of the 9th IEEE International Conference on Advanced Learning Technologies (ICALT2009) (pp. 624-625). July, 14-18, 2009, Riga, Latvia: IEEE.This poster presents an ongoing European project: Language Technologies for Lifelong Learning (LTfLL). The aim of the project is to create a next-generation of support and advice services to enhance individual and collaborative building of competences and knowledge creation in educational and organizational settings. The project makes extensive use of language technologies and cognitive models in the services to face a number of learning specific problems

D2.4. Building a Personal Learning Environment with Language-Technology-based Widgets: Services v2 - integrated thread

Hoisl, B., Haley, D., Wild, F., Anastasiou, L., Buelow, K., Koblische, R., Burek, G., Loiseau, M., Markus, T., Rebedea, T., Drachsler, H., Kometter, H., Westerhout, E., & Posea, V. (2010). D2.4. Building a Personal Learning Environment with Language-Technology-based Widgets: Services v2 - integrated thread. LTfLL-project.This deliverable reports on the results achieved by the LTfLL work packages in their efforts toward interoperability of the LTfLL tools and services. There are two aspects: one is the pedagogical utility of achieving interoperability; the other aspect involves the technical features. The technical basis of the interoperability is to use Wookie widgets in Elgg and is thoroughly described here. Finally, the deliverable provides details and screen shots of each widget for each LTfLL service embedded in the Elgg environment.The work on this publication has been sponsored by the LTfLL STREP that is funded by the European Commission's 7th Framework Programme. Contract 212578 [http://www.ltfll-project.org

Current and Future Patterns of Global Marine Mammal Biodiversity

Quantifying the spatial distribution of taxa is an important prerequisite for the preservation of biodiversity, and can provide a baseline against which to measure the impacts of climate change. Here we analyse patterns of marine mammal species richness based on predictions of global distributional ranges for 115 species, including all extant pinnipeds and cetaceans. We used an environmental suitability model specifically designed to address the paucity of distributional data for many marine mammal species. We generated richness patterns by overlaying predicted distributions for all species; these were then validated against sightings data from dedicated long-term surveys in the Eastern Tropical Pacific, the Northeast Atlantic and the Southern Ocean. Model outputs correlated well with empirically observed patterns of biodiversity in all three survey regions. Marine mammal richness was predicted to be highest in temperate waters of both hemispheres with distinct hotspots around New Zealand, Japan, Baja California, the Galapagos Islands, the Southeast Pacific, and the Southern Ocean. We then applied our model to explore potential changes in biodiversity under future perturbations of environmental conditions. Forward projections of biodiversity using an intermediate Intergovernmental Panel for Climate Change (IPCC) temperature scenario predicted that projected ocean warming and changes in sea ice cover until 2050 may have moderate effects on the spatial patterns of marine mammal richness. Increases in cetacean richness were predicted above 40° latitude in both hemispheres, while decreases in both pinniped and cetacean richness were expected at lower latitudes. Our results show how species distribution models can be applied to explore broad patterns of marine biodiversity worldwide for taxa for which limited distributional data are available

Document retrieval based on intelligent query formulation

Document retrieval based on intelligent query formulation Tech Report kmi-04-1