Sex Differences in Semantic Processing: Event-Related Brain Potentials Distinguish between Lower and Higher Order Semantic Analysis during Word Reading

Behavioral studies suggest that women and men differ in the strategic elaboration of verbally encoded information especially in the absence of external task demand. However, measuring such covert processing requires other than behavioral data. The present study used event-related potentials to compare sexes in lower and higher order semantic processing during the passive reading of semantically related and unrelated word pairs. Women and men showed the same early context effect in the P1-N1 transition period. This finding indicates that the initial lexical-semantic access is similar in men and women. In contrast, sexes differed in higher order semantic processing. Women showed an earlier and longer lasting context effect in the N400 accompanied by larger signal strength in temporal networks similarly recruited by men and women. The results suggest that women spontaneously conduct a deeper semantic analysis. This leads to faster processing of related words in the active neural networks as reflected in a shorter stability of the N400 map in women. Taken together, the findings demonstrate that there is a selective sex difference in the controlled semantic analysis during passive word reading that is not reflected in different functional organization but in the depth of processing.

Shallow Dialogue Processing Using Machine Learning Algorithms (or not)

This paper presents a shallow dialogue analysis model, aimed at human-human dialogues in the context of staff or business meetings. Four components of the model are defined, and several machine learning techniques are used to extract features from dialogue transcripts: maximum entropy classifiers for dialogue acts, latent semantic analysis for topic segmentation, or decision tree classifiers for discourse markers. A rule-based approach is proposed for solving cross-modal references to meeting documents. The methods are trained and evaluated thanks to a common data set and annotation format. The integration of the components into an automated shallow dialogue parser opens the way to multimodal meeting processing and retrieval applications.

Semantic memory involvement in the default mode network: a functional neuroimaging study using independent component analysis

The Default Mode Network (DMN) is a higher order functional neural network that displays activation during passive rest and deactivation during many types of cognitive tasks. Accordingly, the DMN is viewed to represent the neural correlate of internally-generated self-referential cognition. This hypothesis implies that the DMN requires the involvement of cognitive processes, like declarative memory. The present study thus examines the spatial and functional convergence of the DMN and the semantic memory system. Using an active block-design functional Magnetic Resonance Imaging (fMRI) paradigm and Independent Component Analysis (ICA), we trace the DMN and fMRI signal changes evoked by semantic, phonological and perceptual decision tasks upon visually-presented words. Our findings show less deactivation during semantic compared to the two non-semantic tasks for the entire DMN unit and within left-hemispheric DMN regions, i.e., the dorsal medial prefrontal cortex, the anterior cingulate cortex, the retrosplenial cortex, the angular gyrus, the middle temporal gyrus and the anterior temporal region, as well as the right cerebellum. These results demonstrate that well-known semantic regions are spatially and functionally involved in the DMN. The present study further supports the hypothesis of the DMN as an internal mentation system that involves declarative memory functions.

Integration of WSNs into enterprise systems based on semantic physical business entities

Internet of Things based systems are anticipated to gain widespread use in industrial applications. Standardization efforts, like 6L0WPAN and the Constrained Application Protocol (CoAP) have made the integration of wireless sensor nodes possible using Internet technology and web-like access to data (RESTful service access). While there are still some open issues, the interoperability problem in the lower layers can now be considered solved from an enterprise software vendors' point of view. One possible next step towards integration of real-world objects into enterprise systems and solving the corresponding interoperability problems at higher levels is to use semantic web technologies. We introduce an abstraction of real-world objects, called Semantic Physical Business Entities (SPBE), using Linked Data principles. We show that this abstraction nicely fits into enterprise systems, as SPBEs allow a business object centric view on real-world objects, instead of a pure device centric view. The interdependencies between how currently services in an enterprise system are used and how this can be done in a semantic real-world aware enterprise system are outlined, arguing for the need of semantic services and semantic knowledge repositories. We introduce a lightweight query language, which we use to perform a quantitative analysis of our approach to demonstrate its feasibility.

A fuzzy grassroots ontology for improving social semantic web search

The web is continuously evolving into a collection of many data, which results in the interest to collect and merge these data in a meaningful way. Based on that web data, this paper describes the building of an ontology resting on fuzzy clustering techniques. Through continual harvesting folksonomies by web agents, an entire automatic fuzzy grassroots ontology is built. This self-updating ontology can then be used for several practical applications in fields such as web structuring, web searching and web knowledge visualization.A potential application for online reputation analysis, added value and possible future studies are discussed in the conclusion.

Does Age Matter? Semantic Memory in Healthy Young and Elderly Adults

Background: Semantic memory processes have been well described in literature. However, the available findings are mostly based on relatively young subjects and concrete word material (e.g. tree). Comparatively little information exists about semantic memory for abstract words (e.g. mind) and possible age related changes in semantic retrieval. In this respect, we developed a paradigm that is useful to investigate the implicit (i.e. attentionindependent) access to concrete and abstract semantic memory. These processes were then compared between young and elderly healthy subjects. Methods: A well established tool for investigating semantic memory processes is the semantic priming paradigm, which consists both of semantically unrelated and related word pairs. In our behavioral task these noun-noun word pairs were further divided into concrete, abstract and matched pronounceable non-word conditions. With this premise, the young and elderly participants performed a lexical decision task: they were asked to press a choice of two buttons as an indication for whether the word pair contained a non-word or not. In order to minimize controlled (i.e. attention-dependent) retrieval strategies, a short stimulus onset asynchrony (SOA) of 150ms was set. Reaction time (RT) changes and accuracy to related and unrelated words (priming effect) in the abstract vs. concrete condition (concreteness effect) were the dependent variables of interest. Results and Discussion: Statistical analysis confirmed both a significant priming effect (i.e. shorter RTs in semantically related compared to unrelated words) and a concreteness effect (i.e. RT decrease for concrete compared to abstract words) in the young and elderly subjects. There was no age difference in accuracy. The only age effect was a commonly known general slowing in RT over all conditions. In conclusion, age is not a critical factor in the implicit access to abstract and concrete semantic memory.

Altered Electrophysiology of Semantic Word Processing in Alzheimer's Disease and Semantic Dementia

With the progressing course of Alzheimer's disease (AD), deficits in declarative memory increasingly restrict the patients' daily activities. Besides the more apparent episodic (biographical) memory impairments, the semantic (factual) memory is also affected by this neurodegenerative disorder. The episodic pathology is well explored; instead the underlying neurophysiological mechanisms of the semantic deficits remain unclear. For a profound understanding of semantic memory processes in general and in AD patients, the present study compares AD patients with healthy controls and Semantic Dementia (SD) patients, a dementia subgroup that shows isolated semantic memory impairments. We investigate the semantic memory retrieval during the recording of an electroencephalogram, while subjects perform a semantic priming task. Precisely, the task demands lexical (word/nonword) decisions on sequentially presented word pairs, consisting of semantically related or unrelated prime-target combinations. Our analysis focuses on group-dependent differences in the amplitude and topography of the event related potentials (ERP) evoked by related vs. unrelated target words. AD patients are expected to differ from healthy controls in semantic retrieval functions. The semantic storage system itself, however, is thought to remain preserved in AD, while SD patients presumably suffer from the actual loss of semantic representations.

Discovering EEG resting state alterations of Semantic Dementia

Objective Diagnosis of semantic dementia relies on cost-intensive MRI or PET, although resting EEG markers of other dementias have been reported. Yet the view still holds that resting EEG in patients with semantic dementia is normal. However, studies using increasingly sophisticated EEG analysis methods have demonstrated that slightest alterations of functional brain states can be detected. Methods We analyzed the common four resting EEG microstates (A, B, C, and D) of 8 patients with semantic dementia in comparison with 8 healthy controls and 8 patients with Alzheimer’s disease. Results Topographical differences between the groups were found in microstate classes B and C, while microstate classes A and D were comparable. The data showed that the semantic dementia group had a peculiar microstate E, but the commonly found microstate C was lacking. Furthermore, the presence of microstate E was significantly correlated with lower MMSE and language scores. Conclusion Alterations in resting EEG can be found in semantic dementia. Topographical shifts in microstate C might be related to semantic memory deficits. Significance This is the first study that discovered resting state EEG abnormality in semantic dementia. The notion that resting EEG in this dementia subtype is normal has to be revised.

A multifactorial analysis of explicitation in translation

The search for translation universals has been an important topic in translation studies over the past decades. In this paper, we focus on the notion of explicitation through a multifaceted study of causal connectives, integrating four different variables: the role of the source and the target languages, the influence of specific connectives and the role of the discourse relation they convey. Our results indicate that while source and target languages do not globally influence explicitation, specific connectives have a significant impact on this phenomenon. We also show that in English and French, the most frequently used connectives for explicitation share a similar semantic profile. Finally, we demonstrate that explicitation also varies across different discourse relations, even when they are conveyed by a single connective.