PPT-Utilizing vector models for automatic text lemmatization

Author : jiggyhuman | Published Date : 2020-11-06

Ladislav Gallay Supervisor Ing Marián Šimko PhD Slovak University of Technology Faculty of Informatics and Information Technologies Lemmatization basic form

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Utilizing vector models for automatic text lemmatization: Transcript

Ladislav Gallay Supervisor Ing Marián Šimko PhD Slovak University of Technology Faculty of Informatics and Information Technologies Lemmatization basic form of a word houses gt . of. . Camera . Captured Documents . Using. . Document . Image Retrieval. Sheraz. Ahmed, Koichi . Kise. , Masakazu . Iwamura. , Marcus . Liwicki. , . and Andreas . Dengel. Problem to be tackled. OCR for camera-captured documents. Corpora and Statistical Methods. Lecture 6. Semantic similarity. Part 1. Synonymy. Different phonological. /orthographic. words. highly related meanings. :. sofa / couch. boy / lad. Traditional definition:. medical dictations. Stefan Petrik . , . Christina . Drexel, . Leo Fessler . , . Jeremy Jancsary . , . Alexandra Klein . ,Gernot . Kubin . , . Johannes Matiasek . , . Franz Pernkopf . , . Harald . Trost. Padhraic Smyth. Department of Computer Science. University of California, Irvine . . Progress Report. New deadline. In class, Thursday February 18. th. (not Tuesday). Outline. 3 to 5 pages maximum. Vagelis Hristidis. Prepared with the help of . Nhat. Le. Many slides are from Richard . Socher. , . Stanford CS224d: Deep Learning for NLP. To better classify text. We need effective representation of :. Vectorized. Fast Fourier Transform Libraries . for the . Larrabee. and AVX . Instruction Set Extension. Daniel . McFarlin. Franz . Franchetti. Markus . Püschel. Carnegie Mellon University. HPEC, September 2009, Lexington, MA, USA. computing the similarity between words. “. fast. ” is similar to “. rapid. ”. “. tall. ” is similar to “. height. ”. Question answering:. Q. : “. How . tall. . is Mt. Everest?”. Candidate A: “The . Applications:. Web pages . Recommending pages. Yahoo-like classification hierarchies. Categorizing bookmarks. Newsgroup Messages /News Feeds / Micro-blog Posts. Recommending messages, posts, tweets, etc.. Predictively Modeling Social Text William W. Cohen Machine Learning Dept. and Language Technologies Institute School of Computer Science Carnegie Mellon University Joint work with : Amr Ahmed, Andrew Arnold, Ramnath Balasubramanyan, Frank Lin, Matt Hurst (MSFT), Ramesh Nallapati, Noah Smith, Eric Xing, Tae Yano Scott Wen-tau Yih . (Microsoft Research). Joint work with. . Vahed Qazvinian . (University of . Michigan). Measuring Semantic Word Relatedness. How related are words “movie” and “popcorn”?. Vagelis Hristidis. Prepared with the help of . Nhat. Le. Many slides are from Richard . Socher. , . Stanford CS224d: Deep Learning for NLP. To . compare pieces of text. We need effective representation of :. understand the molecular mechanisms. of . astrogliosis. Bhagyashri. Pandey, S.N. Bose Scholar. Asmita. Jaiswal, . pHD. Student. Dr. . Naren. . Ramanan. , Principal Investigator. Astrocytes. Regulate blood flow. Why vector models of meaning?. computing the similarity between words. “. fast. ” is similar to “. rapid. ”. “. tall. ” is similar to “. height. ”. Question answering:. Q. : “. How . Nisheeth. Coin toss example. Say you toss a coin N times. You want to figure out its bias. Bayesian approach. Find the generative model. Each toss ~ Bern(. θ. ). θ. ~ Beta(. α. ,. β. ). Draw the generative model in plate notation.

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