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Computing Semantic Relatedness Using Wikipedia-based Explicit Semantic Analysis
Computing semantic relatedness of natural language texts requires access to vast amounts of common-sense and domain-specific world knowledge. We propose Explicit Semantic Analysis (ESA), a novelExpand
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Placing search in context: the concept revisited
Keyword-based search engines are in widespread use today as a popular means for Web-based information retrieval. Although such systems seem deceptively simple, a considerable amount of skill isExpand
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Knowledge vault: a web-scale approach to probabilistic knowledge fusion
Recent years have witnessed a proliferation of large-scale knowledge bases, including Wikipedia, Freebase, YAGO, Microsoft's Satori, and Google's Knowledge Graph. To increase the scale even further,Expand
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A Review of Relational Machine Learning for Knowledge Graphs
Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper, we provide a review of how such statistical models can be “trained”Expand
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Wikipedia-based Semantic Interpretation for Natural Language Processing
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 statisticalExpand
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A word at a time: computing word relatedness using temporal semantic analysis
Computing the degree of semantic relatedness of words is a key functionality of many language applications such as search, clustering, and disambiguation. Previous approaches to computing semanticExpand
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Overcoming the Brittleness Bottleneck using Wikipedia: Enhancing Text Categorization with Encyclopedic Knowledge
When humans approach the task of text categorization, they interpret the specific wording of the document in the much larger context of their background knowledge and experience. On the other hand,Expand
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Feature Generation for Text Categorization Using World Knowledge
We enhance machine learning algorithms for text categorization with generated features based on domain-specific and common-sense knowledge. This knowledge is represented using publicly availableExpand
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Large-scale learning of word relatedness with constraints
Prior work on computing semantic relatedness of words focused on representing their meaning in isolation, effectively disregarding inter-word affinities. We propose a large-scale data mining approachExpand
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Concept-Based Information Retrieval Using Explicit Semantic Analysis
Information retrieval systems traditionally rely on textual keywords to index and retrieve documents. Keyword-based retrieval may return inaccurate and incomplete results when different keywords areExpand
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