Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
Dependency-Based Construction of Semantic Space Models
Computational Linguistics
Sensitive webpage classification for content advertising
Proceedings of the 1st international workshop on Data mining and audience intelligence for advertising
What's in Wikipedia?: mapping topics and conflict using socially annotated category structure
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
WikiRelate! computing semantic relatedness using wikipedia
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Semantic multi-grain mixture topic model for text analysis
Expert Systems with Applications: An International Journal
A semantic network approach to measuring relatedness
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
A word at a time: computing word relatedness using temporal semantic analysis
Proceedings of the 20th international conference on World wide web
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Automatically computing the semantic relatedness of two words is an essential step for many tasks in natural language processing, including information retrieval. Previous approaches to computing semantic relatedness used statistical techniques or lexical resources. We propose Searcher Result Analysis (SRA), a novel method that captures related text from search engine by issuing proper queries. Inferring the relatedness is then based on word occurrences in certain number of pages. Compared with the previous state of the art, using SRA to computing semantic relatedness based on Wikipedia can achieve competitive results with no need to maintain a local copy of remote resources. It is also shown that the correctness can be further improved by selecting proper knowledge resources or corpora for SRA.