Ever wondered what's the technology behind Siri!
Here’s how the ArchiText (later Excite) search engine worked. Every query was stripped to its significant words — subjects, objects, verbs and adjectives — then each query became a vector in a multidimensional space with each unique word being a dimension. “How do space rockets stay in orbit when they are flying through space?” would become a vector string one unit long for each of those words but two units long for the word “space.” This bit of semantic DNA was then mapped against an index of millions of web pages that had all been similarly converted to multidimensional vectors. It was quick, scalable, concentrated the processing load on the indexing where it didn’t bog down retrieval, and could reliably return pages like “Why satellites fall from the sky” that might answer the question even though none of the same words were used.
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Here’s how the ArchiText (later Excite) search engine worked. Every query was stripped to its significant words — subjects, objects, verbs and adjectives — then each query became a vector in a multidimensional space with each unique word being a dimension. “How do space rockets stay in orbit when they are flying through space?” would become a vector string one unit long for each of those words but two units long for the word “space.” This bit of semantic DNA was then mapped against an index of millions of web pages that had all been similarly converted to multidimensional vectors. It was quick, scalable, concentrated the processing load on the indexing where it didn’t bog down retrieval, and could reliably return pages like “Why satellites fall from the sky” that might answer the question even though none of the same words were used.
Read more here!
For more news coverage, you can follow us on TWITTER, become a fan on our FACEBOOK page and add our G+ JAILBREAK PAGE to your circles! And we will keep you updated with posts over the web.
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