Overcoming the Brittleness Bottleneck using Wikipedia: Enhancing Text Categorization with Encyclopedic Knowledge

By Evgeniy Gabrilovich, Shaul Markovitch. AAAI Conference on Artificial Intelligence, 2006

506 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/fb8f6c5670755a7d282fb9322bc8439492ea052a

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Citations in the corpus

Collaborative filtering recommender systems (2013, background): “Community-based is also connected with the raising popularity of open social networks with a growing in community-based or social recommender systems (Gabrilovich and Markovitch, 2006).”
A Survey of Cross-lingual Word Embedding Models (2017, methodology): “Another embedding model based on the document-aligned Wikipedia structure (Søgaard, Agić, Alonso, Plank, Bohnet, & Johannsen, 2015) bears resemblance with the cross-lingual Explicit Semantic Analysis model (Gabrilovich & Markovitch, 2006; Hassan & Mihalcea, 2009; Sorg & Cimiano, 2012).”
Importance of Semantic Representation: Dataless Classification (2008, unknown): “Here, we provide a brief summary of this approach and refer the reader to (Gabrilovich & Markovitch 2007) and (Gabrilovich & Markovitch 2006) for more details.”
Content-based Recommender Systems: State of the Art and Trends (2011, background): “Explicit Semantic Analysis (ESA) [34, 35] is a technique able to provide a finegrained semantic representation of natural language texts in a high-dimensional space of natural (and also comprehensible) concepts derived from Wikipedia.”
Learning multilingual named entity recognition from Wikipedia (2013, background): “ne linking [12,17,45] and document classification [29,66] rely on Wikipedia as a large source of world knowledge, fewer applications exploit both its text and structured features.”
Text-to-Text Semantic Similarity for Automatic Short Answer Grading (2009, methodology): “5 For ESA, we use our own implementation of the ESA algorithm as described in (Gabrilovich and Markovitch, 2006).”
BabelNet: Building a Very Large Multilingual Semantic Network (2010, background): “These include, among others, text summarization (Nastase, 2008), Named Entity Recognition (Bunescu and Pas¸ca, 2006), Question Answering (Harabagiu et al., 2000) and text categorization (Gabrilovich and Markovitch, 2006).”
Concept-Based Information Retrieval Using Explicit Semantic Analysis (2011, methodology): “Note that while [Gabrilovich and Markovitch 2006] also split documents into sentence and paragraph contexts in applying ESA to text categorization, they eventually combined the concepts of these subcontexts into a single unified representation.”
Autonomously semantifying wikipedia (2007, methodology): “used Wikipedia to enhance text categorization [15], and later proposed a semantic-relatedness metric using Wikipedia-based explicit semantic analysis [16].”
An effective, low-cost measure of semantic relatedness obtained from Wikipedia links (2008, background): “This is the case for most applications in the literature, which deal primarily with topics: named entities (Bunescu and Pasca, 2006; Cucerzan, 2007); key phrases (Mihalcea and Csomai, 2007); categories (Gabrilovich and Markovitch, 2006); or entries in existing ontologies (Medelyan and Legg, 2008) an”
Recommender Systems (2016, unknown): “A potential direction of work, mentioned in [376], is to enhance contentbased recommender systems with encyclopedic knowledge [174, 210, 211], such as that gained from Wikipedia.”
The Eras and Trends of Automatic Short Answer Grading (2015, methodology): “The two corpus-based measures are Explicit Semantic Analysis (ESA) (Gabrilovich and Markovitch 2006) and LSA.”