PPT-Supervised ranking hash for semantic similarity search

Kai Li GuoJun Qi Jun Ye Tuoerhongjiang Yusuph Kien A Hua Department of Computer Science University of Central Florida ISM 2016 Presented by Tuoerhongjiang Yusuph Introduction Massive amount of highdimensional data high computational costs . com ABSTRACT An important problem in search engine advertising is key word generation In the past advertisers have preferred to bid for keywords that tend to have high search volumes and hence are more expensive An alternate strategy in volves biddin . Multi-label Protein Subcellular Localization. Shibiao WAN and Man-Wai MAK. The Hong Kong Polytechnic University. Sun-Yuan KUNG. Princeton University. Outline. Introduction and Motivation. Retrieval of GO Terms. Ciro . Cattuto. , Dominik Benz, Andreas . Hotho. , . Gerd. . Stumme. Presented by. Smitashree. . Choudhury. Overview. Motivation. Measures of . semantic Relatedness. Semantic . Grounding of measures. １２月７日. 研究会. 祭都援炉. (. マットエンロ. ). Up until now: Getting to know NLP. “Speech and Language Processing” (. Jurafsky. & Martin). 論文：. On-Demand Information Extract . Instructors:. http://www.cohenwang.com/edith/bigdataclass2013. Edith Cohen. Amos Fiat. Haim. Kaplan. Tova. Milo. Overview: More on Min-Hash Sketches . Subset/Selection size queries from random samples. CIS 606. Spring 2010. Hash tables. Many applications require a dynamic set that supports only the . dictionary . operations . INSERT. , SEARCH, and DELETE. Example: a symbol table in a compiler.. A hash table is effective for implementing a dictionary.. Andrew Chi. Brian Cristante. COMP 790-133: January 27, 2015. Image Retrieval. AI / Vision Problem. Systems Design / Software Engineering Problem. Sensory Gap. : “What features should we use?”. Query-Dependent?. Tutorial. Introduction. Miriam Fernandez | KMI, Open University, UK. Thanh Tran | Institute AIFB, KIT, DE. Peter Mika| Yahoo Research, Spain. Search . Document Retrieval vs. Data Retrieval. Differences of search technologies. S. imilarity to Semantic Relations. Georgeta. . Bordea. , November 25. Based on a talk by Alessandro . Lenci. . titled “Will DS ever become Semantic?”, Jan 2014. Distributional Semantics . (DS. Lioma. Lecture . 18: Latent Semantic Indexing. 1. Overview. Latent semantic indexing . Dimensionality reduction. LSI in information retrieval. 2. Outline. Latent semantic indexing . Dimensionality reduction. Hashing for Large-Scale Visual Search. Shih-Fu . Chang. www.ee.columbia.edu/dvmm. Columbia University. December 2012. Joint work with . Junfeng. He (Facebook), . Sanjiv. Kumar (Google), Wei Liu (IBM Research), and Jun Wang (IBM . Opportunities. April 2015. Search – . Where we were!. https://pbs.twimg.com/media/B1sh79LIEAAR4Hg.jpg:large. https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQ5sTW2_hPnqpRVmpHW2L4FFJ9SPuDaNF8TCTvH7xqUNXeo8Cqj-A. PRESENTED BY . Peter Mika, Sr. Research Scientist, Yahoo Labs. . ⎪ . November 27, . 2014 . The Semantic Web (2001-). 11/27/14. 2. Part of Tim . Berners-Lee’s . original proposal . for the . Web. Petra Bud. íková, FI MU. CEMI meeting, Plze. ň. , 1. 6. . . 4. . 2014. Formalization. The annotation problem is . defined by a . query image . I. . and a . vocabulary . V. of candidate concepts.