At Google, we spend a lot of time thinking about how computer systems can read and understand human language in order to process it in intelligent ways. Today, we are excited to share the fruits of our research with the broader community by releasing SyntaxNet, an open-source neural network framework implemented in TensorFlow that provides a foundation for Natural Language Understanding (NLU) systems. Our release includes all the code needed to train new SyntaxNet models on your own data, as well as Parsey McParseface, an English parser that we have trained for you and that you can use to analyze English text.
We solve this problem using a semi-supervised form of logistic regression. A large portion of the model consists of “bag of words” type features from user submitted reviews on the properties. Since it is a semi-supervised technique, not only do we use the reviews on locations that we have tag votes on during training, we also use a large chunk of unlabeled data. Also, when applying the model to get the end results, we need to read and process all our reviews. On top of that, we have hundreds of different tags.
Vast amounts of new information and data are generated everyday through economic, academic and social activities, much with significant potential economic and societal value. Techniques such as text and data mining and analytics are required to exploit this potential.
What makes a language important on a global scale? Is it the oldest? The one spoken by the most people? What about the one that has the greatest ability to reach other people by being translated? A multidisciplinary research team has examined the languages of the world and categorized them on how widely certain forms of media are translated into other languages. César Hidalgo of MIT led the research, and the paper was published in the Proceedings of the National Academy of Sciences.
Analyst Seth Grimes interviews François-Régis Chaumartin, CEO of Paris-based text-analytics provider Proxem, whose solutions are applied for reputation management, human resources, and voice of the customer applications.
Natural language isn't that great for searching. When you type a search query into Google, you miss out on a wide spectrum of human concepts and human emotions. Queried words have to be present in the web page, and then those pages are ranked according to the number of inbound and outbound links. That's great for filtering out the cruft on the internet -- and there's a lot of that out there. What it doesn't do is understand the relationships between words and understand the similarities or dissimilarities.
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