Difference between revisions of "Wikipedia-Based Semantic Interpretation for Natural Language Processing"
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| − | '''Wikipedia-Based Semantic Interpretation for Natural Language Processing''' - scientific work related to Wikipedia quality published in 2009, written by Evgeniy Gabrilovich and Shaul Markovitch. | + | '''Wikipedia-Based Semantic Interpretation for Natural Language Processing''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Evgeniy Gabrilovich]] and [[Shaul Markovitch]]. |
== Overview == | == Overview == | ||
| − | 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 authors propose a novel method, called Explicit Semantic Analysis (ESA), for fine-grained semantic interpretation of unrestricted natural language texts. Authors method represents meaning in a high-dimensional space of concepts derived from Wikipedia, the largest encyclopedia in existence. Authors explicitly represent the meaning of any text in terms of Wikipedia-based concepts. Authors evaluate the effectiveness of 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. | + | 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 authors propose a novel method, called Explicit Semantic Analysis (ESA), for fine-grained semantic interpretation of unrestricted natural language texts. Authors method represents meaning in a high-dimensional space of concepts derived from [[Wikipedia]], the largest encyclopedia in existence. Authors explicitly represent the meaning of any text in terms of Wikipedia-based concepts. Authors evaluate the effectiveness of 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. |
Revision as of 07:05, 16 May 2020
Wikipedia-Based Semantic Interpretation for Natural Language Processing - scientific work related to Wikipedia quality published in 2009, written by Evgeniy Gabrilovich and Shaul Markovitch.
Overview
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 authors propose a novel method, called Explicit Semantic Analysis (ESA), for fine-grained semantic interpretation of unrestricted natural language texts. Authors method represents meaning in a high-dimensional space of concepts derived from Wikipedia, the largest encyclopedia in existence. Authors explicitly represent the meaning of any text in terms of Wikipedia-based concepts. Authors evaluate the effectiveness of 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.
