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.
Papers published on a yearly basis
Papers
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01 Jun 2014TL;DR: A semantic parsing framework based on semantic similarity for open domain question answering (QA) that achieves higher precision across different recall points compared to the previous approach, and can improve F1 by 7 points.
Abstract: We develop a semantic parsing framework based on semantic similarity for open domain question answering (QA). We focus on single-relation questions and decompose each question into an entity mention and a relation pattern. Using convolutional neural network models, we measure the similarity of entity mentions with entities in the knowledge base (KB) and the similarity of relation patterns and relations in the KB. We score relational triples in the KB using these measures and select the top scoring relational triple to answer the question. When evaluated on an open-domain QA task, our method achieves higher precision across different recall points compared to the previous approach, and can improve F1 by 7 points.
424 citations
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28 Aug 1987TL;DR: The Absity semantic interpreter helps clarify the role of language in semantic interpretation and provides a basis for future semantic interpreters to address language-based problems.
Abstract: Preface 1. Introduction 2. Semantic interpretation 3. The Absity semantic interpreter 4. Lexical disambiguation 5. Polaroid words 6. Structural disambiguation 7. The semantic enquiry desk 8. Conclusion 9. Speculations, partially baked ideas, and exercises for the reader References Index of names Index of subjects.
421 citations
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TL;DR: This work proposes a novel method, called Explicit Semantic Analysis (ESA), for fine-grained semantic interpretation of unrestricted natural language texts, which represents meaning in a high-dimensional space of concepts derived from Wikipedia, the largest encyclopedia in existence.
Abstract: Adequate representation of natural language semantics requires access to vast amounts of common sense and domain-specific world knowledge. Prior work in the field was based on purely statistical techniques that did not make use of background knowledge, on limited lexicographic knowledge bases such as WordNet, or on huge manual efforts such as the CYC project. Here we propose a novel method, called Explicit Semantic Analysis (ESA), for fine-grained semantic interpretation of unrestricted natural language texts. Our method represents meaning in a high-dimensional space of concepts derived from Wikipedia, the largest encyclopedia in existence. We explicitly represent the meaning of any text in terms of Wikipedia-based concepts. We evaluate the effectiveness of our method on text categorization and on computing the degree of semantic relatedness between fragments of natural language text. Using ESA results in significant improvements over the previous state of the art in both tasks. Importantly, due to the use of natural concepts, the ESA model is easy to explain to human users.
420 citations
01 Jan 2006
TL;DR: A WordNetbased measure of semantic relatedness is introduced by combining the structure and content of WordNet with co–occurrence information derived from raw text that can make comparisons between any two concepts without regard to their part of speech.
Abstract: In this paper, we introduce a WordNetbased measure of semantic relatedness by combining the structure and content of WordNet with co–occurrence information derived from raw text. We use the co–occurrence information along with the WordNet definitions to build gloss vectors corresponding to each concept in WordNet. Numeric scores of relatedness are assigned to a pair of concepts by measuring the cosine of the angle between their respective gloss vectors. We show that this measure compares favorably to other measures with respect to human judgments of semantic relatedness, and that it performs well when used in a word sense disambiguation algorithm that relies on semantic relatedness. This measure is flexible in that it can make comparisons between any two concepts without regard to their part of speech. In addition, it can be adapted to different domains, since any plain text corpus can be used to derive the co–occurrence information.
416 citations
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TL;DR: Experimental results show the increased accuracy obtained by combining lexical, structural and extensional matchers with semantic verification, and demonstrate the advantage of using a domain-specific thesaurus for the alignment of specialized ontologies.
415 citations