PPT-Entity- & Topic-Based

Author : pamella-moone | Published Date : 2017-07-03

Information Ordering Ling 573 Systems and Applications May 5 2016 Roadmap Entitybased cohesion model Model entity based transitions Topicbased cohesion model Models

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Entity- & Topic-Based: Transcript

Information Ordering Ling 573 Systems and Applications May 5 2016 Roadmap Entitybased cohesion model Model entity based transitions Topicbased cohesion model Models sequence of topic transitions. Tracking in Real-Time using Sub-Topic Detection. on Twitter. . Sandeep . Panem. , . Romil. Bansal, Manish Gupta, . Vasudeva. Varma. International . Institute of information technology, . Hyderabad. era:. Topic-based. . Annotators. “. Diego Maradona won against Mexico. ”. Dictionary of terms. against. Diego. Maradona. Mexico. won. 2.2. 5.1. 9.1. 1.0. 0.1. Term Vector. Similarity(v,w) ≈ cos(. Martin . Ja. čala and . Jozef Tvarožek. Špindlerův. . Mlýn. , Czech Republic. January 23, 2012. Slovak University of Technology. Bratislava, Slovakia. Problem. Given an input text, detect and decide on correct meaning of named entites. Jie Tang. *. , Limin Yao. #. , and Dewei Chen. *. *. Dept. of Computer Science and Technology. Tsinghua University. #. Dept. of Computer Science, University of Massachusetts Amherst. April, 2009. ?. What are the major topics in the returned docs?. with MapReduce. Lars Kolb. , Hanna Köpcke, Andreas Thor, Erhard Rahm. Database . Group Leipzig. http://dbs.uni-leipzig.de. Glasgow, . CloudDB. 2011. Identification of semantically equivalent entities. Jiawei Han, Chi Wang and Ahmed El-. Kishky. Computer Science, University of Illinois at Urbana-Champaign. August 24, 2014. 1. Outline. Introduction to bringing structure to text. Mining phrase-based and entity-enriched topical hierarchies. Fanjin. Zhang, Xiao Liu, . Jie. Tang, . Yuxiao. Dong, . Peiran. Yao, . Jie. Zhang, . Xiaotao. Gu, Yan Wang, Bin Shao, Rui Li and . Kuansan. Wang.. Tsinghua University Microsoft Research. Selectional. Preferences. Alan Ritter. Mausam. Oren . Etzioni. 1. Selectional. Preferences. Encode admissible arguments for a relation. E.g. “eat X”. Plausible. Implausible. chicken. Windows XP. Selectional. Preferences. Alan Ritter. Mausam. Oren . Etzioni. 1. Selectional. Preferences. Encode admissible arguments for a relation. E.g. “eat X”. Plausible. Implausible. chicken. Windows XP. TOOLS. 1. Xiao Liu, Shuo Yu, and Hsinchun Chen. Spring 2019. Introduction. Text mining, also referred to as text data mining, refers to the process of deriving high quality information from text. . Text mining is an interdisciplinary field that draws on . DatasetNovels in the Dutch National Bibliography:164.394Bibliographic records104.235 Works113.227Expressions163.234Manifestations192.610ItemsNext stepsPublishing Workentities of novels as LODConversio Pankaj. . Vanwari. . Under guidance of . Dr. S. . Sudarshan. . Overview of Presentation. Introduction to Entity Queries. Keyword search on structured data. Querying over unstructured data. Entity queries using ontology based extraction. Queries . Using . an . Extended . Graph Model. Ankur. . Agrawal. . S. Sudarshan . Ajitav. . Sahoo. . Adil. . Sandalwala. . Prashant. . Jaiswal. . IIT . Bombay. History of Keyword Queries. MS Thesis Defense. Manishkumar. . Jha. Committee Members. Dr. . Subbarao. . Kambhampati. Dr. . Huan. Liu. Dr. . Hasan. . Davulcu. 2. Deep Web Integration Scenario. Web DB. Mediator. ←. query. Web DB.

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