Dataset Information

Sentence understanding depends on contextual use of semantic and real world knowledge.

ABSTRACT: Human language allows us to express our thoughts and ideas by combining entities, concepts and actions into multi-event episodes. Yet, the functional neuroanatomy engaged in interpretation of such high-level linguistic input remains poorly understood. Here, we used easy to detect and more subtle "borderline" anomalies to investigate the brain regions and mechanistic principles involved in the use of real-world event knowledge in language comprehension. Overall, the results showed that the processing of sentences in context engages a complex set of bilateral brain regions in the frontal, temporal and inferior parietal lobes. Easy anomalies preferentially engaged lower-order cortical areas adjacent to the primary auditory cortex. In addition, the left supramarginal gyrus and anterior temporal sulcus as well as the right posterior middle temporal gyrus contributed to the processing of easy and borderline anomalies. The observed pattern of results is explained in terms of (i) hierarchical processing along a dorsal-ventral axis and (ii) the assumption of high-order association areas serving as cortical hubs in the convergence of information in a distributed network. Finally, the observed modulation of BOLD signal in prefrontal areas provides support for their role in the implementation of executive control processes.

SUBMITTER: Tune S

PROVIDER: S-EPMC5120675 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Sentence understanding depends on contextual use of semantic and real world knowledge.

Tune Sarah S Schlesewsky Matthias M Nagels Arne A Small Steven L SL Bornkessel-Schlesewsky Ina I

NeuroImage 20160510

Human language allows us to express our thoughts and ideas by combining entities, concepts and actions into multi-event episodes. Yet, the functional neuroanatomy engaged in interpretation of such high-level linguistic input remains poorly understood. Here, we used easy to detect and more subtle "borderline" anomalies to investigate the brain regions and mechanistic principles involved in the use of real-world event knowledge in language comprehension. Overall, the results showed that the proces ...[more]

PMID: 27177762

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Project description:Predictions and predictive knowledge have seen recent success in improving not only robot control but also other applications ranging from industrial process control to rehabilitation. A property that makes these predictive approaches well-suited for robotics is that they can be learned online and incrementally through interaction with the environment. However, a remaining challenge for many prediction-learning approaches is an appropriate choice of prediction-learning parameters, especially parameters that control the magnitude of a learning machine's updates to its predictions (the learning rates or step sizes). Typically, these parameters are chosen based on an extensive parameter search-an approach that neither scales well nor is well-suited for tasks that require changing step sizes due to non-stationarity. To begin to address this challenge, we examine the use of online step-size adaptation using the Modular Prosthetic Limb: a sensor-rich robotic arm intended for use by persons with amputations. Our method of choice, Temporal-Difference Incremental Delta-Bar-Delta (TIDBD), learns and adapts step sizes on a feature level; importantly, TIDBD allows step-size tuning and representation learning to occur at the same time. As a first contribution, we show that TIDBD is a practical alternative for classic Temporal-Difference (TD) learning via an extensive parameter search. Both approaches perform comparably in terms of predicting future aspects of a robotic data stream, but TD only achieves comparable performance with a carefully hand-tuned learning rate, while TIDBD uses a robust meta-parameter and tunes its own learning rates. Secondly, our results show that for this particular application TIDBD allows the system to automatically detect patterns characteristic of sensor failures common to a number of robotic applications. As a third contribution, we investigate the sensitivity of classic TD and TIDBD with respect to the initial step-size values on our robotic data set, reaffirming the robustness of TIDBD as shown in previous papers. Together, these results promise to improve the ability of robotic devices to learn from interactions with their environments in a robust way, providing key capabilities for autonomous agents and robots.

Project description:BackgroundComputing semantic relatedness between textual labels representing biological and medical concepts is a crucial task in many automated knowledge extraction and processing applications relevant to the biomedical domain, specifically due to the huge amount of new findings being published each year. Most methods benefit from making use of highly specific resources, thus reducing their usability in many real world scenarios that differ from the original assumptions. In this paper we present a simple resource-efficient method for calculating semantic relatedness in a knowledge-poor environment. The method obtains results comparable to state-of-the-art methods, while being more generic and flexible. The solution being presented here was designed to use only a relatively generic and small document corpus and its statistics, without referring to a previously defined knowledge base, thus it does not assume a 'closed' problem.ResultsWe propose a method in which computation for two input texts is based on the idea of comparing the vocabulary associated with the best-fit documents related to those texts. As keyterm extraction is a costly process, it is done in a preprocessing step on a 'per-document' basis in order to limit the on-line processing. The actual computations are executed in a compact vector space, limited by the most informative extraction results. The method has been evaluated on five direct benchmarks by calculating correlation coefficients w.r.t. average human answers. It also has been used on Gene - Disease and Disease- Disease data pairs to highlight its potential use as a data analysis tool. Apart from comparisons with reported results, some interesting features of the method have been studied, i.e. the relationship between result quality, efficiency and applicable trimming threshold for size reduction. Experimental evaluation shows that the presented method obtains results that are comparable with current state of the art methods, even surpassing them on a majority of the benchmarks. Additionally, a possible usage scenario for the method is showcased with a real-world data experiment.ConclusionsOur method improves flexibility of the existing methods without a notable loss of quality. It is a legitimate alternative to the costly construction of specialized knowledge-rich resources.

Dataset Information

Sentence understanding depends on contextual use of semantic and real world knowledge.

Publications

Sentence understanding depends on contextual use of semantic and real world knowledge.

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