Our accustomed systems of retrieving particular bits of information no longer fill the needs of many people. Searching traditional indexes of print publications has been aided by computerized databases, but still usually requires time-consuming serial searching of one database after the other, and then moving on to other methods of searching for internet sources. And what if the information being sought is a sound byte? A video clip? Yesterday's e-mail exchange between respected scientists? Artificial intelligence may hold the key to information retrieval in an age where widely different formats contain the information being sought, and the universe of knowledge is simply too big and growing too rapidly for successful searching to proceed at a human's slow speed.
About this course: Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. Text data are unique in that they are usually generated directly by humans rather than a computer system or sensors, and are thus especially valuable for discovering knowledge about people's opinions and preferences, in addition to many other kinds of knowledge that we encode in text. First, while the raw data may be large for any particular problem, it is often a relatively small subset of the data that are relevant, and a search engine is an essential tool for quickly discovering a small subset of relevant text data in a large text collection. Second, search engines are needed to help analysts interpret any patterns discovered in the data by allowing them to examine the relevant original text data to make sense of any discovered pattern.
Many of blockchain technology's true believers already see the next reincarnation of the internet on the horizon: A decentralized web where direct peer-to-peer interactions, fueled by digital sovereignty, replace many of the business models we know from Uber to Twitter. The New York-based tech company Blockstack just launched a $25 million venture capital fund for startups and projects working to create a decentralized internet. Shea and Ali are just two prominent members of a wider movement for decentralization, according to Colin Pape, project lead at the decentralized search engine project Presearch. Google dominates internet searches, monopolizing global access to information in an almost unprecedented way.
Nowhere was this more apparent than at this year's Apple Worldwide Developers Conference (WWDC 2017). These include real-time image recognition, text prediction, sentiment analysis, face detection, handwriting detection, emotion detection, and entity recognition. As an example, see how Amazon deploys image recognition to simplify purchasing. Another illustration is the new Siri-powered watch face which uses machine learning to customize its content in real time throughout the day, including reminders, traffic information, upcoming meetings, news, smart home controls, etc.
Rick and Morty is chock-full of quotable moments, so it would only make sense that someone would eventually find a way search every single word, wouldn't it? The creators of the Simpsons and Futurama search tools (Paul Kehrer, Sean Schulte and Allie Young) have trotted out Master of All Science, a web engine that lets you find any Rick and Morty line and create a meme or animated GIF to match. In a sense, the Rick and Morty engine is the culmination of the developers' work so far: it's proof that their technology can search virtually any show where it was originally very Simpsons-specific. It can't search for objects, alas, but it's easy to imagine imagine TV networks using this engine to index their shows and let fans share their favorite moments.
This week, at the 2017 Annual Meeting of the Association for Computational Linguistics in Vancouver, Canada, Lease and collaborators from UT Austin and Northeastern University presented two papers describing their novel IR systems. They proposed a method for exploiting these existing linguistic resources via weight sharing to improve NLP models for automatic text classification. "This provides a general framework for codifying and exploiting domain knowledge in data-driven neural network models," say Byron Wallace, Lease's collaborator from Northeastern University. By improving core natural language processing technologies for automatic information extraction and the classification of texts, web search engines built on these technologies can continue to improve.
The entire process, in it's current state, is geared heavily towards people (litigation analysts) using their monkey-brains to instruct software how to find relevant documents, using filters like: text that matches the exact phrases they are looking for, in the time periods they wish to target, between the two custodians they believe to have been involved in the exchange, and so on. Typically applied to content generated on social media platforms and review systems, sentiment analysis is the application of NLP techniques and computational linguistics to derive emotional attributes from text content. Imagine, though, a legal analyst finding a relevant case document, noting the topics discussed in that document, and then being able to surface similar documents based on topical similarity identified by software. The firm that handed over, or "produced" those documents had similar needs, but their tools are geared towards finding the relevant information which they are legally obligated to produce, producing that and nothing more.
The address bar, also known as the omnibox, sits up at the top of the Google Chrome interface. In fact, it has all the superpowers of the Google search engine. You can even flip a coin using the omnibox by searching for "flip a coin." For more detailed searches, enter your keywords and then type "site:popsci.com" The address bar improves on the Google search website because it can interact with the text on the page you're currently browsing: If you don't want to type out a search term, for example, you can highlight a word or a phrase on a webpage then drag it up to the omnibox to search for it.
In this multi-part series, we will explore how to build a search engine. We will build this search engine with an AngularJS front-end and use elasticsearch as the computation back end. Single Page Applications (SPA) are gaining a lot of traction because of their simplicity and ability to act as a graceful front end to gigabytes of back end data. You can head over to Oracle's website to check install Java 8 for your operating system.
Russian antitrust watchdog Federal Antimonopoly Service (FAS) is looking into the violations of country's anti-monopoly law by some of the biggest search engines such as Google and Yandex, Russia's state-run news agency RIA Novosti reported Monday. An enquiry has been initiated into the matter after the European Commission fined Google €2.42 billion ($2.85 billion) in June over the abuse of its dominant position in the search engine market. EC said Google preferred results from its own shopping services. The search engine agreed to pay FAS penalties amounting to 438 million rubles ($7.7 million), along with an additional fine of one million rubles ($16,688), although the company stated its apps were not exclusive to Android devices in Russia and it did not limit the preset of other applications by device makers on the default home screen.
However, a higher proportion of female users issued navigational queries compared to male users and slightly more male users issued tail queries. We found that a higher proportion of older users issued navigational queries and more tail queries compared to younger users. We also expect that queries about different topics will lead to different metric values, as will queries issued by users with different demographics. Rather than estimating absolute levels of satisfaction for each demographic group and then comparing these estimates, this method estimates differences in satisfaction between demographic groups directly.