1. Articles from dzone.com

  2. 73-83 of 83 « 1 2 3 4
    1. Using OpenNLP for Named-Entity-Recognition in Scala Big Data

      Using OpenNLP for Named-Entity-Recognition in Scala Big Data

      A common challenge in Natural Language Processing (NLP) is Named Entity Recognition (NER) - this is the process of extracting specific pieces of data from a body of text, commonly people, places and organizations (for example trying to extract the name of all people mentioned in a wikipedia article). NER is a problem that has been tackled many times over the evolution of NLP, from dictionary-based, to rule-based, to statistical models and more recently using Neural Nets to solve the problem.

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      Mentions: Stanford NLP GPL
    2. PyDev of the Week: Brianna Laugher Web Dev

      This week we welcome Brianna Laugher as our PyDev of the Week! Brianna is the organizer behind her local PyLadies chapter in Australia. She has a very interesting website that displays her work. You might also find her GitHub profile illuminating. Let’s take some time getting to know our fellow Pythonista better! Can you tell us a little about yourself (hobbies, education, etc): I’m a software developer living in Melbourne, Australia.

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    3. Using NLP and Neo4j for a Social Media Recommendation Engine Database

      Using NLP and Neo4j for a Social Media Recommendation Engine Database

      Introduction In recent years, the rapid growth of social media communities has created a vast amount of digital documents on the web. Recommending relevant documents to users is a strategic goal for the effectiveness of customer engagement but at the same time is not a trivial problem. In a previous blog post , we introduced the GraphAware Natural Language Processing (NLP) plugin.

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      Mentions: ElasticSearch
    4. The Next User Interface: Why, How, and When? Big Data

      Will the future of UI be based on natural language or virtual and augmented reality? A full understanding of natural language is not possible right now (and barely will be in the coming years). Virtual reality forces a user to be fully isolated from reality, which is not always acceptable. AR is a variation of a GUI merged with reality, which is not usually considered a sort of UI. The user interface has origins in things of human nature like senses and thinking.

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      Mentions: New York Earth GUI
    5. Documenting APIs When Preferences Matter Integration

      Documenting APIs When Preferences Matter Integration

      I sat down to write a “Document APIs with Open API and JSON Schema” article. That’s still quite possible, of course, and there’re lots of great resources to help you do just that. However, my head’s been ironing out the wrinkles in the Web Annotation Protocol for the last few weeks, and I really wanted to see how the current blend of API documentation and description tooling fared against this rather minimal API.

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      Mentions: MIT API
    6. My Summer at Hortonworks: Part III Big Data

      We Are Only Seeing the Tip of the Iceberg Day 1: On Day 1 we heard about how data is revolutionizing the world and how people are changing the landscape of Big Data. At our Women in Big Data (WiBD) lunch, panelists offered advice and insight on how women are changing the cultural landscape of technology. The Women in Big Data group was strongly encouraging girls to start their interest in technology young, develop an interest in science and math and follow their passions.

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    7. SnapTravel Launches Emma, a Concierge Bot for Hotel Bookings Big Data

      SnapTravel Launches Emma, a Concierge Bot for Hotel Bookings Big Data

      As Opus Research delves into the bifurcated domain of Intelligent Assistance, it has become increasingly evident that today’s bot developers and customer care professionals live in parallel universes. In a post issued in 2011 , when coining the term “Conversational Commerce”, Dan Miller, lead analyst and founder with Opus Research, anticipated “the advent of true self-service.” Noting that the combination of smartphones, “The Cloud,” speech recognition with natural language understanding, and recognizing that our spoken words are assets, he saw “the foundation for smartphone-based services that are highly responsive to individual end-users.” In other words, the drudgery associated with searching ...

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    73-83 of 83 « 1 2 3 4
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      Discourse, Entailment, Machine Translation, NER, Parsing, Segmentation, Semantic, Sentiment, Summarization, WSD