'Natural language processing algorithms' presentation slideshows

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From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series

From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series

From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series. Brendan O’Connor http://brenocon.com Machine Learning Department Carnegie Mellon University Presentation at AAPOR, May 2012. Joint work with: Ramnath Balasubramanyan, Bryan R. Routledge, Noah A. Smith; CMU

By wind
(287 views)

How To Using AI To Transform Consumer Businesses

How To Using AI To Transform Consumer Businesses

Artificial Intelligence is the current buzzword. AI is worth exploring as it is transforming all industries. These are the benefits enhance efficiency, adds jobs, leads to loss of control, enhances our lifestyles and automation etc. Let’s take a look at our ppt which briefs you about how to use artificial intelligence to transform consumer businesses.

By consagousmridul
(6 views)


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Natural Language Processing: Data, Algorithms, and Knowledge

Natural Language Processing: Data, Algorithms, and Knowledge

Natural Language Processing: Data, Algorithms, and Knowledge. BEARS 2011. Dan Klein Computer Science Division University of California, Berkeley. TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A A A A. Language Technologies. Goal: Deep Understanding.

By kyrie (103 views)

Natural Language Processing

Natural Language Processing

Natural Language Processing. Expectation Maximization. Word Based Model. How to translate a word → look up in dictionary Haus — house, building, home, household, shell Multiple translations some more frequent than others for instance: house , and building most common

By taima (69 views)

Natural Language Processing

Natural Language Processing

Natural Language Processing. Machine Translation (MT). Machine translation was one of the first applications envisioned for computers First demonstrated by IBM in 1954 with a basic word-for-word translation system. Rule-Based vs. Statistical MT. Rule-based MT:

By dorie (119 views)

Natural Language Processing

Natural Language Processing

Natural Language Processing. Rada Mihalcea. Fall 2008. Any Light at The End of The Tunnel ?. Yahoo, Google, Microsoft  Information Retrieval Monster.com, HotJobs.com (Job finders)  Information Extraction + Information Retrieval Systran powers Babelfish  Machine Translation

By lynda (105 views)

Natural Language Processing

Natural Language Processing

Natural Language Processing. Introduction. Any Light at The End of The Tunnel ?. Yahoo, Google, Microsoft  Information Retrieval Monster.com, HotJobs.com (Job finders)  Information Extraction + Information Retrieval Systran powers Babelfish  Machine Translation

By macon (92 views)

Natural Language Processing

Natural Language Processing

Natural Language Processing. For us humans, spoken language is the most efficient and convenient way of direct communication. Therefore, being able to simply speak to a computer instead of using a keyboard or mouse would be desirable.

By ledell (468 views)

Natural Language Processing

Natural Language Processing

Natural Language Processing. Frank Bergman Mike Gallagher Jason Firestone. Barcelona, October 19th , 2001. Stacked Layers of Language. Person A. Person B. „Thoughts“. „Thoughts“. Pragmatics. Pragmatics. Semantics. Semantics. Syntax. Syntax. Speech. Speech. Voice Recognition.

By ethelda (1 views)

Natural Language Processing

Natural Language Processing

Natural Language Processing. Why “natural language”?. Natural vs. artificial Language vs. English. Why “natural language”?. Natural vs. artificial Not precise, ambiguous, wide range of expression Language vs. English English, French, Japanese, Spanish. Why “natural language”?.

By tameron (254 views)

Natural Language Processing

Natural Language Processing

Università di Pisa. Natural Language Processing. Giuseppe Attardi Dipartimento di Informatica Università di Pisa. Goal of NLP. Computers would be a lot more useful if they could handle our email, do our library research, chat to us … But they are fazed by natural languages

By kris (212 views)