When it comes to search, there's Google and there's everyone else -- the company is basically synonymous with searching the internet. But Omnity, a relatively new company from San Francisco, thinks own search that's based on "semantic mapping" offers something that Google can't do. Omnity's trick is that it looks for the connections between documents on the internet based on rare words -- the theory that research that has several of the same rare words will likely be about related topics, even if that research doesn't directly link to or cite each other. Thus far, Omnity has operated primarily by selling enterprise plans to companies and educational institutions. Omnity can search not only all of the public datasets it scans (like patents, scientific, engineering and medical documents, clinical trials, case law, SEC filings and so forth) but also a company's internal documents -- for some companies, Omnity indexes 150 petabytes of data.
Search engines that aren't Google rarely have much that's interesting to offer to the average consumer. But Omnity, a new search engine aimed at researchers -- or even just students doing their homework -- offers some glimmers of something new that make it worth taking notice. Search, as we know it, is ripe for some sort of change, after all. Google is certainly working to bake search more fully into our cars, phones and other devices. Specialized search engines -- for flights, places to stay, even .gifs
By 2020, the market for machine-learning applications will reach 40 billion, per IDC. The next time you see Democratic presidential nominee Hillary Clinton with an unflattering look on her face in a TV spot supporting GOP rival Donald Trump, it's all but certain you can attribute the ad creative to artificial intelligence. The Republican National Committee is using machine-learning software from Veritone, a 2-year-old player that just secured 50 million in funding. Designed to work with laser-fast precision, its audio-based system lets the RNC zip through all the publicly available times Clinton has spoken on TV, radio or online video to scoop up her angriest or oddest moments. The company is about to add a visual-sentiment feature, which will zero in on facial expressions and make cringe-worthy moments even easier to find.
With such judgements, we can construct a better term-weighted query for the TL search, essentially producing true translingual RF. Of course, this RF process can also be used to enhance the SL query and search other SL databases at no extra cost to or involvement from the analyst. The envisioned mechanism is shown in Figure 3, and encompasses the following steps: 1. The analyst types in a source language query Qs; 2. Parallel corpus (source half) is searched by an engine using Qs; 3. One of the following methods is used to search the TL document database: Prom retrieved SL/TL document pairs, the TL document contents are used as a new query QT to search the TL document database; or The retrieved SL/TL document pairs are first given back to the analyst, in order to scan the SL documents for relevance; then the Rocchio formula is used for both SL and TL document database search.