PPT-Term Weighting and Ranking Models

Debapriyo Majumdar Information Retrieval Spring 2015 Indian Statistical Institute Kolkata Parametric and zone indexes Thus far a doc has been a sequence of terms In fact documents have multiple parts some with special semantics. 41 Weighting Function for Microwave and Infrared Satellite Nadir Sounding As a simple example we will consider in this section the retri eval of atmo spheric temperature by a nadir looking microwave satellite sounder Ie the instrument measures brigh 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). 2. Weighting cases and weighting variables A data set is usually composed of cases for which several are predicated. Thus the data set is a rectangular array of cases by variables. Statistical analys Roman . Sergienko. , . Ph.D. . student. Tatiana . Gasanova. , . Ph.D. . student. Ulm University, . Germany. Shaknaz. . Akhmedova. , . Ph.D. . . student. Siberian. State Aerospace University, . Krasnoyarsk. . Schütze. and Christina . Lioma. Lecture 7: Scores in a Complete Search System. 1. Overview. Recap . . Why rank? . More on cosine. Implementation of ranking . The complete search system. 2. Kieskompas. datasets. Max . Boiten. Overview. Weighting. . methodologies. Sampling . from. data. Weighting. data. Sampling . from. . data. Sample matching. Take . probability. sample. Match cases . Ranked retrieval. Thus far, our queries have all been Boolean.. Documents either match or . don’t. .. Good for expert users with precise understanding of their needs and the collection.. Also good for applications: Applications can easily consume 1000s of results.. 楊立偉教授. wyang@ntu.edu.tw. 本投影片修改自. Introduction to Information Retrieval. 一書之投影片 . Ch. 1~3, 6. 1. Basics to Informational Retrieval. 2. 3. Definition . of. . information. and . Ranking Models. Debapriyo Majumdar. Information Retrieval – Spring 2015. Indian Statistical Institute Kolkata. Parametric and zone indexes. Thus far, a doc has been a sequence of terms. In fact documents have multiple parts, some with special semantics:. 楊立偉教授. 台灣科大資管系. wyang@ntu.edu.tw. 本投影片修改自. Introduction to Information Retrieval. 一書之投影片 . Ch. 6. 1. Ranked Retrieval. 2. 3. Ranked. . retrieval. Boolean. IR models: Vector Space . Model. Term Weighting Approaches. Instructor: Rada . Mihalcea. Graphic Representation. Example. :. D. 1. = 2T. 1. 3T. 2. 5T. 3. D. 2. = 3T. 1. 7T. 2. T. 3. Q = 0T. Two factors:. A term that appears just once in a document is probably not as significant as a term that appears a number of times in the document.. A term that is common to every document in a collection is not a very good choice for choosing one document over another to address an information need. . signals in silicon sensors. 34. th. RD50 workshop Lancaster 12-14 June 2017. W. . Riegler. , CERN. . The current induced on a grounded . electrode . by a moving point charge q is given by . Where the weighting field . Scale-Varying Triplet Ranking with Classification Loss. for Facial Age Estimation. Woobin Im, Sungeun Hong, Sung-Eui Yoon, Hyun S. Yang. Age Estimation. Estimating age group or age value from face images.