PPT-Hinrich

Schütze and Christina Lioma Lecture 5 Index Compression 1 Overview Recap Compression Term statistics Dictionary compression Postings compression 2 Outline Recap Compression. org Abstract This paper presents a graphtheoretic model of the acquisition of lexical syntactic representa tions The representations the model learns are noncategorical or graded We propose a new evaluation methodology of syntactic ac quisition in th V 2007 Abstract The colors of fruits and 64258owers are traditionally viewed as an adaptation to increase the detectability of plant organs to animal vectors The detectability of visual signals increases with increasing contrasts between target and b . Schütze. and Christina . Lioma. Lecture 1: Boolean Retrieval. 1. 2. Take-. away. Administrativa. Boolean Retrieval: Design and data structures of a simple . information. . retrieval. . system. . Schütze. and Christina . Lioma. Lecture . 14: Vector Space Classification. 1. Overview. Recap . . Feature selection. Intro vector space classification . . Rocchio. . kNN. Linear classifiers. . Schütze. and Christina . Lioma. Lecture 3: Dictionaries and tolerant retrieval. 1. Overview. Recap . . Dictionaries. . Wildcard queries. Edit distance. Spelling correction. Soundex. 2. Outline. . Schütze. and Christina . Lioma. Lecture . 20: Crawling. 1. Overview. . R. ecap . . A simple crawler. . A real crawler. 2. Outline. . R. ecap . . A simple crawler. . A real crawler. 3. 4. Search. . Schütze. and Christina . Lioma. Lecture . 11: Probabilistic Information Retrieval. 1. Overview. . Probabilistic Approach to Retrieval. . Basic Probability Theory. Probability Ranking Principle. . Schütze. and Christina . Lioma. Lecture . 15-1: Support Vector Machines. 1. Overview. . Support Vector Machines. . Issues in the classification of . text . documents. 2. Outline. . Support Vector Machines. . Schütze. and Christina . Lioma. Lecture . 15-2: Learning to Rank. 1. Overview. . Learning . Boolen. Weights. . Learning Real-Valued Weights. Rank Learning as Ordinal Regression. 2. Outline. . . Schütze. and Christina . Lioma. Lecture 2: The term vocabulary and postings lists. 1. Overview. Recap . . Documents. . Terms. General + Non-English. English. Skip pointers. Phrase queries. 2. . Schütze. and Christina . Lioma. Lecture . 19: Web Search. 1. Overview. Recap . . Big picture. Ads . Duplicate detection. 2. Outline. Recap . . Big picture. Ads . Duplicate detection. 3. 4. Lioma. Lecture 5: Index Compression. 1. Overview. Recap . . Compression. . Term statistics. Dictionary compression. Postings compression. 2. Outline. Recap . . Compression. Term statistics. Dictionary compression. Lioma. Lecture . 20: Crawling. 1. Overview. . R. ecap . . A simple crawler. . A real crawler. 2. Outline. . R. ecap . . A simple crawler. . A real crawler. 3. 4. Search. . engines. rank . content. Lioma. Lecture . 18: Latent Semantic Indexing. 1. Overview. Latent semantic indexing . Dimensionality reduction. LSI in information retrieval. 2. Outline. Latent semantic indexing . Dimensionality reduction.