User Behaviors in Related Word Retrieval and New Word Detection: A Collaborative Perspective
ACM Transactions on Asian Language Information Processing (TALIP)
DeepPurple: estimating sentence semantic similarity using n-gram regression models and web snippets
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
Inferring the semantic properties of sentences by mining syntactic parse trees
Data & Knowledge Engineering
EmotiWord: affective lexicon creation with application to interaction and multimedia data
MUSCLE'11 Proceedings of the 2011 international conference on Computational Intelligence for Multimedia Understanding
An Ontology Based Model for Document Clustering
International Journal of Intelligent Information Technologies
Machine learning of syntactic parse trees for search and classification of text
Engineering Applications of Artificial Intelligence
A semantic similarity measure in document databases: an earth mover's distance-based approach
Proceedings of the 2013 Research in Adaptive and Convergent Systems
Future Generation Computer Systems
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In this work, Web-based metrics that compute the semantic similarity between words or terms are presented and compared with the state of the art. Starting from the fundamental assumption that similarity of context implies similarity of meaning, relevant Web documents are downloaded via a Web search engine and the contextual information of words of interest is compared (context-based similarity metrics). The proposed algorithms work automatically, do not require any human-annotated knowledge resources, e.g., ontologies, and can be generalized and applied to different languages. Context-based metrics are evaluated both on the Charles-Miller data set and on a medical term data set. It is shown that context-based similarity metrics significantly outperform co-occurrence-based metrics, in terms of correlation with human judgment, for both tasks. In addition, the proposed unsupervised context-based similarity computation algorithms are shown to be competitive with the state-of-the-art supervised semantic similarity algorithms that employ language-specific knowledge resources. Specifically, context-based metrics achieve correlation scores of up to 0.88 and 0.74 for the Charles-Miller and medical data sets, respectively. The effect of stop word filtering is also investigated for word and term similarity computation. Finally, the performance of context-based term similarity metrics is evaluated as a function of the number of Web documents used and for various feature weighting schemes.