The effects of age-of-acquisition and frequency-of-occurrence in visual word recognition: Further evidence from the Dutch language

It has been claimed that the frequency eOEect in visual word naming is an artefact of age-of-acquisition: Words are named faster not because they are encountered more often in texts, but because they have been acquired earlier. In a series of experiments using immediate naming, lexical decision, and masked priming, we found that frequency had a clear eOEect in lexical tasks when age-of-acquisition is controlled for. At the same time, age-ofacquisition was a significant variable in all tasks, whereas imageability had no effect. These results corroborate findings previously reported in English and Dutch

Straight to Shapes: Real-time Detection of Encoded Shapes

Current object detection approaches predict bounding boxes, but these provide little instance-specific information beyond location, scale and aspect ratio. In this work, we propose to directly regress to objects' shapes in addition to their bounding boxes and categories. It is crucial to find an appropriate shape representation that is compact and decodable, and in which objects can be compared for higher-order concepts such as view similarity, pose variation and occlusion. To achieve this, we use a denoising convolutional auto-encoder to establish an embedding space, and place the decoder after a fast end-to-end network trained to regress directly to the encoded shape vectors. This yields what to the best of our knowledge is the first real-time shape prediction network, running at ~35 FPS on a high-end desktop. With higher-order shape reasoning well-integrated into the network pipeline, the network shows the useful practical quality of generalising to unseen categories similar to the ones in the training set, something that most existing approaches fail to handle.Comment: 16 pages including appendix; Published at CVPR 201

Latent Relation Language Models

In this paper, we propose Latent Relation Language Models (LRLMs), a class of language models that parameterizes the joint distribution over the words in a document and the entities that occur therein via knowledge graph relations. This model has a number of attractive properties: it not only improves language modeling performance, but is also able to annotate the posterior probability of entity spans for a given text through relations. Experiments demonstrate empirical improvements over both a word-based baseline language model and a previous approach that incorporates knowledge graph information. Qualitative analysis further demonstrates the proposed model's ability to learn to predict appropriate relations in context

Linking Human Diseases to Animal Models Using Ontology-Based Phenotype Annotation

A novel method for quantifying the similarity between phenotypes by the use of ontologies can be used to search for candidate genes, pathway members, and human disease models on the basis of phenotypes alone

Context updating during sentence comprehension: The effect of aboutness topic

AbstractTo communicate efficiently, speakers typically link their utterances to the discourse environment and adapt their utterances to the listener‘s discourse representation. Information structure describes how linguistic information is packaged within a discourse to optimize information transfer. The present study investigates the nature and time course of context integration (i.e., aboutness topic vs. neutral context) on the comprehension of German declarative sentences with either subject-before-object (SO) or object-before-subject (OS) word order using offline comprehensibility judgments and online event-related potentials (ERPs). Comprehensibility judgments revealed that the topic context selectively facilitated comprehension of stories containing OS (i.e., non-canonical) sentences. In the ERPs, the topic context effect was reflected in a less pronounced late positivity at the sentence-initial object. In line with the Syntax-Discourse Model, we argue that these context-induced effects are attributable to reduced processing costs for updating the current discourse model. The results support recent approaches of neurocognitive models of discourse processing

Polishing Papers for Publication: Palimpsests or Procrustean Beds?

Portuguese academic discourse of the humanities is notoriously difficult to render into English, given the prevalence of rhetorical and discourse features that are largely alien to English academic style. The aim of this study was to test the hypothesis that some of those features might find their way into the English texts produced by Portuguese scholars through a process of pragmalinguistic and sociopragmatic transfer. If so, this would have important practical and ideological implications, not only for the academics concerned, but also for editors, revisers, teachers of EAP, translators, writers of academic style manuals and all the other gatekeepers of the globalized culture. The study involved a corpus of some 113,000 running words of English academic prose written by established Portuguese academics in the Humanities, which had been presented to a native speaker of English (professional translator and specialist in academic discourse) for revision prior to submission for publication. After correction of superficial grammatical and spelling errors, the texts were made into a corpus, which was tagged for Part of Speech (CLAWS7) and discourse markers (USAS) using WMatrix2 (Rayson 2003). The annotated corpus was then interrogated for the presence of certain discourse features using Wmatrix2 and Wordsmith 5 (Scott 2006), and the findings compared with those of a control corpus, Controlit, of published articles written by L1 academics in the same or comparable journals. The results reveal significant overuse of certain features by Portuguese academics, and a corresponding underuse of others, suggesting marked differences in the value attributed to those features by the two cultures