PPT-5. Vector Space and Probabilistic Retrieval Models

Many slides in this section are adapted from Prof Joydeep Ghosh UT ECE who in turn adapted them from Prof Dik Lee Univ of Science and Tech Hong Kong 1 These notes are based in part on notes by Dr Raymond J Mooney at the University of Texas at Austin . Jian-Yun . Nie. Main IR processes. Last lecture: Indexing – determine the important content terms. Next process: Retrieval. How should a retrieval process be done?. Implementation issues: using index (e.g. merge of lists). CSC 575. Intelligent Information Retrieval. Intelligent Information Retrieval. 2. Retrieval Models. Model is an idealization or abstraction of an actual process. in this case, process is matching of documents with queries, i.e., retrieval. . Natarajan. Introduction to Probabilistic Logical Models. Slides based on tutorials by . Kristian. . Kersting. , James . Cussens. , . Lise. . Getoor. . & Pedro . Domingos. Take-Away Message . Chris Manning, Pandu Nayak and . Prabhakar. . Raghavan. Who are these people?. Stephen Robertson. Keith van . Rijsbergen. Karen . Sp. ä. rck. . Jones. Summary – vector space ranking. Represent the query as a weighted tf-idf vector. Corpora and Statistical Methods. Lecture 6. Semantic similarity. Part 1. Synonymy. Different phonological. /orthographic. words. highly related meanings. :. sofa / couch. boy / lad. Traditional definition:. Debapriyo Majumdar. Information Retrieval – Spring 2015. Indian Statistical Institute Kolkata. Using majority of the slides from . Chris . Manning, . Pandu. . Nayak. and . Prabhakar. . Raghavan. Machine Learning @ CU. Intro courses. CSCI 5622: Machine Learning. CSCI 5352: Network Analysis and Modeling. CSCI 7222: Probabilistic Models. Other courses. cs.colorado.edu/~mozer/Teaching/Machine_Learning_Courses. & Subspaces. Kristi Schmit. Definitions. A subset W of vector space V is called a . subspace . of V . iff. The. . zero vector of V is in W.. W. is closed under vector addition, for each . u. . Textbook by. Christopher D. Manning, . Prabhakar. . Raghavan. , and . Hinrich. . Schutze. Notes Revised by X. . Meng. for SEU. May 2014. Retrieval Models and Vector Space Model. Retrieval Models. A retrieval model specifies the details of:. (VSM). doc1. | . Documents as . Vectors. . Terms are axes of the space. Documents are points or vectors . . in this space. So we have a |V|-dimensional vector space. | . The Matrix. Doc 1 : makan makan. 4.1 Vectors in . R. n. 4.2 Vector Spaces. 4.3 Subspaces of Vector Spaces. 4.4 Spanning Sets and Linear Independence. 4.5 Basis and Dimension. 4.6 Rank of a Matrix and Systems of Linear Equations. BY. DR. ADNAN ABID. Lecture # . Introduction. Library Management System. Structured Data Storage / Tables. Semi-Structured and Unstructured . Employee Department Salary. Library Digitization. Information Retrieval Models. 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”?. . H. HABEEB RANI. Assistant professor of Mathematics. Department of mathematics. Hajee. . Karutha. . Rowther. . Howdia. College. VECTOR SPACES. Definition. Examples. THEOREM. Subspaces.