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 dataset and on a medical term dataset. 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 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 datasets, 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.