2009 WikipediaBasedSemIntForNLP

Subject Headings: Wikipedia-based Information Extraction.

Notes

It is related to (Gabrilovich & Markovitch, 2007) ⇒ Evgeniy Gabrilovich, and Shaul Markovitch. (2007). “Computing Semantic Relatedness Using Wikipedia-based Explicit Semantic Analysis.” In: Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007).

http://www.jair.org/bestpaper.html
- QUOTES: This paper demonstrates how contextual word meaning can be represented in a high-dimensional space of concepts derived from encyclopedic knowledge bases such as Wikipedia. A key insight is that the set of target documents provided for a semantic analysis task is normally insufficient; knowledge from publicly available resources, such as Wikipedia, allow much finer-grained representations of contextual word meaning to be recovered, which can significantly improve the quality of text categorization and assessments of semantic relatedness. This work represents one of the earliest and most influential investigations of using large-scale encyclopedic resources for extracting meaning representations --- an idea that now lies at the heart of much work in natural language processing and information retrieval.

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.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 WikipediaBasedSemIntForNLP	Evgeniy Gabrilovich Shaul Markovitch			Wikipedia-based Semantic Interpretation for Natural Language Processing		JAIR Journal Series	http://www.jair.org/media/2669/live-2669-4346-jair.pdf	10.1613/jair.2669		2007