natural language processing techniques

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Published By: IBM APAC     Published Date: Nov 22, 2017
AlchemyAPI’s approach to natural language processing incorporates both linguistic and statistical analysis techniques into a single unified system. This hybrid approach provides an industry-leading advantage since both techniques have benefits and drawbacks depending on the content and specific usecases. Linguistic analysis takes a basic grammatical approach to understand how words combine into phrases, and how those phrases combine into sentences. While this approach works well with editorialized text (e.g., news articles and press releases), it does not perform as well when it comes to usergenerated content, often filled with slang, misspellings and idioms. Statistical analysis, however, understands language from a mathematical standpoint and works well on “noisy” content (e.g., tweets, blog posts, and Facebook status updates). The combination of these two approaches allows for increased accuracy on a variety of content.
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industry, advantage, linguistic, grammatical, statistical analysis, content
    
IBM APAC
Published By: IBM     Published Date: Jun 25, 2018
Recognizing the shift to a subscription business model required real-time customer support, Autodesk turned to IBM technology to enhance its customer experience. Using Watson Assistant, Autodesk developed a virtual agent to interact with customers, applying natural language processing (NLP) and deep learning techniques to recognize and extract the intent, context and meaning behind inquiries. Quickly resolving easy customer concerns, Watson Assistant is supporting 100,000 conversations per month, with response times 99% faster than before and leading to a 10-point increase in customer satisfaction levels for Autodesk. Find out how Watson Assistant can accelerate your customer support experience. Click here to find out more about how embedding IBM technologies can accelerate your solutions’ time to market.
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IBM
Published By: SPSS Inc.     Published Date: Mar 31, 2009
This paper briefly defines text analytics, describes various approaches to text analytics, and then focuses on the natural language processing techniques used by text analytics solutions.
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spss, text analytics, data management, statistical analysis, natural language processing techniques, computational linguistics, web sites, blogs, wikis, e-mails, instant messaging, predictive analytics, bottom up approach, documents, customer relationship management, crm, voice of the customer, competitive landscape, security threats, understanding text
    
SPSS Inc.
Published By: SPSS     Published Date: Jun 30, 2009
This paper briefly defines text analytics, describes various approaches to text analytics, and then focuses on the natural language processing techniques used by text analytics solutions.
Tags : 
spss, text analytics, data management, statistical analysis, natural language processing techniques, computational linguistics, web sites, blogs, wikis, e-mails, instant messaging, predictive analytics, bottom up approach, documents, customer relationship management, crm, voice of the customer, competitive landscape, security threats, understanding text
    
SPSS
Published By: SPSS, Inc.     Published Date: Mar 31, 2009
This paper briefly defines text analytics, describes various approaches to text analytics, and then focuses on the natural language processing techniques used by text analytics solutions.
Tags : 
spss, text analytics, data management, statistical analysis, natural language processing techniques, computational linguistics, web sites, blogs, wikis, e-mails, instant messaging, predictive analytics, bottom up approach, documents, customer relationship management, crm, voice of the customer, competitive landscape, security threats, understanding text
    
SPSS, Inc.
Published By: SAS     Published Date: Oct 18, 2017
With all of the attention on machine learning, many are seeking a better understanding of this hot topic and the benefits that it could provide to their organizations. Machine learning – as well as deep learning, natural language processing and cognitive computing – are driving innovations in identifying images, personalizing marketing campaigns, genomics, and navigating the self-driving car. This e-book provides a primer on these innovative techniques as well as 10 best practices and a checklist for machine learning readiness.
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SAS
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