Developers can use this technology in building a document search engine to retrieve the most related documents.
They can also use it to write software that sorts through a large repository of text and categorize them automatically. If you have example text for each category, when given new text, just use the API to see which category example it most closely relates to.
If you can automatically know the distance and relationship between any two pieces of text, what will you build?
Head over to the demo page and then come back and tell us your ideas in the comments below.
In SEM and SEO, keyword research is the process of finding good keywords to buy for paid search advertisements or to target their SEO efforts on. Usually, a keyword researcher has to goes through extremely long lists of keywords. They are doing this in order to find keywords that are popular and most relevant to their marketing campaign. Then, they either buy ads for those keywords or focus their SEO efforts on those keywords.
With the technology and algorithm behind our Text Similarity API, we built the first semantic keyword research tool that can sort by relevance. So now, when faced with a long list of keywords, the keyword researcher can go through the list faster. They simply type in a description of their campaign goals into the “Target Relevance” box. Then, each keyword in the list will be automatically scored and sorted by how related it is to the what the researcher set as the “Target Relevance”.