Why Use Semantic Similarity?
The quality of semantic similarity has recently dramatically improved and has led to many
interesting applications. Here are some examples:
Plagiarism Checking
Thanks to semantic similarity, you can automatically detect whether a piece of
text is a paraphrase of another piece of text.
Semantic Search
Modern search engines must be able to detect the intent behind a search request and then
match that intent against a high volume of text samples. This is a great application for
semantic similarity.
Opinions Analysis
Thanks to semantic similarity, it is possible analyze a huge volume of Tweets,
conversations, comments... and then detect some trends out of them.
Recommendation Systems
In the domain of content recommendation (e.g., news, articles, products, or movies),
semantic similarity can be used to recommend items that are semantically related to those a
user has previously liked, viewed, or purchased. By analyzing the semantic content of items,
systems can identify and suggest other items with similar themes or topics, enhancing
personalization and user engagement.