Topic
Semantic similarity
About: Semantic similarity is a research topic. Over the lifetime, 14605 publications have been published within this topic receiving 364659 citations. The topic is also known as: semantic relatedness.
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08 Jan 1998
TL;DR: In this paper, a method and system for disambiguating multiples of syntactically related words automatically using the notion of semantic similarity between words is provided for word clustering.
Abstract: A method and system are provided for disambiguating multiples of syntactically related words automatically using the notion of semantic similarity between words. Based on syntactically related words derived from a sample text, a set is formed containing each associating word and the words associated in the syntactic relationship with it. The associating words are expanded to all word senses. Pair wise intersections of the resulting sets are formed so as to form pairs of semantically compatible word clusters which may be stored as pairs of cooccurrence restriction codes.
91 citations
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14 Jun 2020TL;DR: The authors proposed an adaptive margin principle to improve the generalization ability of metric-based meta-learning approaches for few-shot learning problems, where semantic similarity between each pair of classes is considered to separate samples in the feature embedding space from similar classes.
Abstract: Few-shot learning (FSL) has attracted increasing attention in recent years but remains challenging, due to the intrinsic difficulty in learning to generalize from a few examples. This paper proposes an adaptive margin principle to improve the generalization ability of metric-based meta-learning approaches for few-shot learning problems. Specifically, we first develop a class-relevant additive margin loss, where semantic similarity between each pair of classes is considered to separate samples in the feature embedding space from similar classes. Further, we incorporate the semantic context among all classes in a sampled training task and develop a task-relevant additive margin loss to better distinguish samples from different classes. Our adaptive margin method can be easily extended to a more realistic generalized FSL setting. Extensive experiments demonstrate that the proposed method can boost the performance of current metric-based meta-learning approaches, under both the standard FSL and generalized FSL settings.
91 citations
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TL;DR: These findings show that patterns of brain activity in ITC not only reflect the organization of visual information into objects but also represent objects in a format compatible with conceptual thought and language.
Abstract: In the ventral visual pathway, early visual areas encode light patterns on the retina in terms of image properties, for example, edges and color, whereas higher areas encode visual information in terms of objects and categories. At what point does semantic knowledge, as instantiated in human language, emerge? We examined this question by studying whether semantic similarity in language relates to the brain's organization of object representations in inferior temporal cortex ITC, an area of the brain at the crux of several proposals describing how the brain might represent conceptual knowledge. Semantic relationships among words can be viewed as a geometrical structure with some pairs of words close in their meaning e.g., man and boy and other pairs more distant e.g., man and tomato. ITC's representation of objects similarly can be viewed as a complex structure with some pairs of stimuli evoking similar patterns of activation e.g., man and boy and other pairs evoking very different patterns e.g., man and tomato. In this study, we examined whether the geometry of visual object representations in ITC bears a correspondence to the geometry of semantic relationships between word labels used to describe the objects. We compared ITC's representation to semantic structure, evaluated by explicit ratings of semantic similarity and by five computational measures of semantic similarity. We show that the representational geometry of ITC-but not of earlier visual areas V1-is reflected both in explicit behavioral ratings of semantic similarity and also in measures of semantic similarity derived from word usage patterns in natural language. Our findings show that patterns of brain activity in ITC not only reflect the organization of visual information into objects but also represent objects in a format compatible with conceptual thought and language.
91 citations
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01 Jan 2018
TL;DR: This article propose a simple yet effective approach for incorporating side information in the form of distributional constraints over the generated responses, which help generate more content rich responses that are based on a model of syntax and topics.
Abstract: Neural conversation models tend to generate safe, generic responses for most inputs This is due to the limitations of likelihood-based decoding objectives in generation tasks with diverse outputs, such as conversation To address this challenge, we propose a simple yet effective approach for incorporating side information in the form of distributional constraints over the generated responses We propose two constraints that help generate more content rich responses that are based on a model of syntax and topics (Griffiths et al, 2005) and semantic similarity (Arora et al, 2016) We evaluate our approach against a variety of competitive baselines, using both automatic metrics and human judgments, showing that our proposed approach generates responses that are much less generic without sacrificing plausibility A working demo of our code can be found at https://githubcom/abaheti95/DC-NeuralConversation
90 citations
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TL;DR: Semantic priming refers to a reduction in the reaction time to identify or make a judgment about a stimulus that has been immediately preceded by a semantically related word or picture and is thought to result from a partial overlap in the semantic associates of the two words.
90 citations