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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Papers
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TL;DR: The hypothesis that the right hemisphere semantic system is involved in processing of remote semantic information is supported by the results of a semantic priming paradigm study on healthy subjects.
142 citations
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21 Oct 2013TL;DR: Experimental results show that the proposed A2 SH can characterize the semantic affinities among images accurately and can shape user search intent precisely and quickly, leading to more accurate search results as compared to state-of-the-art CBIR solutions.
Abstract: This paper presents a novel Attribute-augmented Semantic Hierarchy (A2 SH) and demonstrates its effectiveness in bridging both the semantic and intention gaps in Content-based Image Retrieval (CBIR). A2 SH organizes the semantic concepts into multiple semantic levels and augments each concept with a set of related attributes, which describe the multiple facets of the concept and act as the intermediate bridge connecting the concept and low-level visual content. A hierarchical semantic similarity function is learnt to characterize the semantic similarities among images for retrieval. To better capture user search intent, a hybrid feedback mechanism is developed, which collects hybrid feedbacks on attributes and images. These feedbacks are then used to refine the search results based on A2 SH. We develop a content-based image retrieval system based on the proposed A2 SH. We conduct extensive experiments on a large-scale data set of over one million Web images. Experimental results show that the proposed A2 SH can characterize the semantic affinities among images accurately and can shape user search intent precisely and quickly, leading to more accurate search results as compared to state-of-the-art CBIR solutions.
142 citations
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01 Jun 2014TL;DR: A robust answer reranking model for non-factoid questions that integrates lexical semantics with discourse information is proposed, driven by two representations of discourse: a shallow representation centered around discourse markers, and a deep one based on Rhetorical Structure Theory.
Abstract: We propose a robust answer reranking model for non-factoid questions that integrates lexical semantics with discourse information, driven by two representations of discourse: a shallow representation centered around discourse markers, and a deep one based on Rhetorical Structure Theory. We evaluate the proposed model on two corpora from different genres and domains: one from Yahoo! Answers and one from the biology domain, and two types of non-factoid questions: manner and reason. We experimentally demonstrate that the discourse structure of nonfactoid answers provides information that is complementary to lexical semantic similarity between question and answer, improving performance up to 24% (relative) over a state-of-the-art model that exploits lexical semantic similarity alone. We further demonstrate excellent domain transfer of discourse information, suggesting these discourse features have general utility to non-factoid question answering.
142 citations
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24 Feb 2014TL;DR: This work proposes a graph-based semantic model for representing document content that combines DBpedia's structure with an information-theoretic measure of concept association, based on its explicit semantic relations, and achieves a performance close to that of highly specialized methods that have been tuned to these specific tasks.
Abstract: We propose a graph-based semantic model for representing document content. Our method relies on the use of a semantic network, namely the DBpedia knowledge base, for acquiring fine-grained information about entities and their semantic relations, thus resulting in a knowledge-rich document model. We demonstrate the benefits of these semantic representations in two tasks: entity ranking and computing document semantic similarity. To this end, we couple DBpedia's structure with an information-theoretic measure of concept association, based on its explicit semantic relations, and compute semantic similarity using a Graph Edit Distance based measure, which finds the optimal matching between the documents' entities using the Hungarian method. Experimental results show that our general model outperforms baselines built on top of traditional methods, and achieves a performance close to that of highly specialized methods that have been tuned to these specific tasks.
141 citations
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TL;DR: This paper describes an approach to visualization of text document collection based on methods from linear algebra, the system implementing it and results of using the system on several datasets.
Abstract: Visualization is commonly used in data analysis to help the user in getting an initial idea about the raw data as well as visual representation of the regularities obtained in the analysis. In similar way, when we talk about automated text processing and the data consists of text documents, visualization of text document corpus can be very useful. From the automated text processing point of view, natural language is very redundant in the sense that many different words share a common or similar meaning. For computer this can be hard to understand without some background knowledge. We describe an approach to visualization of text document collection based on methods from linear algebra. We apply Latent Semantic Indexing (LSI) as a technique that helps in extracting some of the background knowledge from corpus of text documents. This can be also viewed as extraction of hidden semantic concepts from text documents. In this way visualization can be very helpful in data analysis, for instance, for finding main topics that appear in larger sets of documents. Extraction of main concepts from documents using techniques such as LSI, can make the results of visualizations more useful. For example, given a set of descriptions of European Research projects (6FP) one can find main areas that these projects cover including semantic web, e-learning, security, etc. In this paper we describe a method for visualization of document corpus based on LSI, the system implementing it and give results of using the system on several datasets.
140 citations