Dataset Information

The Nature and Neural Correlates of Semantic Association versus Conceptual Similarity.

ABSTRACT: The ability to represent concepts and the relationships between them is critical to human cognition. How does the brain code relationships between items that share basic conceptual properties (e.g., dog and wolf) while simultaneously representing associative links between dissimilar items that co-occur in particular contexts (e.g., dog and bone)? To clarify the neural bases of these semantic components in neurologically intact participants, both types of semantic relationship were investigated in an fMRI study optimized for anterior temporal lobe (ATL) coverage. The clear principal finding was that the same core semantic network (ATL, superior temporal sulcus, ventral prefrontal cortex) was equivalently engaged when participants made semantic judgments on the basis of association or conceptual similarity. Direct comparisons revealed small, weaker differences for conceptual similarity > associative decisions (e.g., inferior prefrontal cortex) and associative > conceptual similarity (e.g., ventral parietal cortex) which appear to reflect graded differences in task difficulty. Indeed, once reaction time was entered as a covariate into the analysis, no associative versus category differences remained. The paper concludes with a discussion of how categorical/feature-based and associative relationships might be represented within a single, unified semantic system.

SUBMITTER: Jackson RL

PROVIDER: S-EPMC4816784 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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The Nature and Neural Correlates of Semantic Association versus Conceptual Similarity.

Jackson Rebecca L RL Hoffman Paul P Pobric Gorana G Lambon Ralph Matthew A MA

Cerebral cortex (New York, N.Y. : 1991) 20150130 11

The ability to represent concepts and the relationships between them is critical to human cognition. How does the brain code relationships between items that share basic conceptual properties (e.g., dog and wolf) while simultaneously representing associative links between dissimilar items that co-occur in particular contexts (e.g., dog and bone)? To clarify the neural bases of these semantic components in neurologically intact participants, both types of semantic relationship were investigated i ...[more]

PMID: 25636912

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Project description:BackgroundNeural network based embedding models are receiving significant attention in the field of natural language processing due to their capability to effectively capture semantic information representing words, sentences or even larger text elements in low-dimensional vector space. While current state-of-the-art models for assessing the semantic similarity of textual statements from biomedical publications depend on the availability of laboriously curated ontologies, unsupervised neural embedding models only require large text corpora as input and do not need manual curation. In this study, we investigated the efficacy of current state-of-the-art neural sentence embedding models for semantic similarity estimation of sentences from biomedical literature. We trained different neural embedding models on 1.7 million articles from the PubMed Open Access dataset, and evaluated them based on a biomedical benchmark set containing 100 sentence pairs annotated by human experts and a smaller contradiction subset derived from the original benchmark set.ResultsExperimental results showed that, with a Pearson correlation of 0.819, our best unsupervised model based on the Paragraph Vector Distributed Memory algorithm outperforms previous state-of-the-art results achieved on the BIOSSES biomedical benchmark set. Moreover, our proposed supervised model that combines different string-based similarity metrics with a neural embedding model surpasses previous ontology-dependent supervised state-of-the-art approaches in terms of Pearson's r (r = 0.871) on the biomedical benchmark set. In contrast to the promising results for the original benchmark, we found our best models' performance on the smaller contradiction subset to be poor.ConclusionsIn this study, we have highlighted the value of neural network-based models for semantic similarity estimation in the biomedical domain by showing that they can keep up with and even surpass previous state-of-the-art approaches for semantic similarity estimation that depend on the availability of laboriously curated ontologies, when evaluated on a biomedical benchmark set. Capturing contradictions and negations in biomedical sentences, however, emerged as an essential area for further work.

Dataset Information

The Nature and Neural Correlates of Semantic Association versus Conceptual Similarity.

Publications

The Nature and Neural Correlates of Semantic Association versus Conceptual Similarity.

Similar Datasets

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