1 Introduction to Information Retrieval : The nature of unstructured and semi-structured text. Inverted index and Boolean queries. (Chapter - 1) 2 Text Indexing, Storage and Compression : Text encoding: tokenization, stemming, stop words, phrases, index optimization. Index compression: lexicon compression and postings lists compression. Gap encoding, gamma codes, Zipf's Law. Index construction. Postings size estimation, merge sort, dynamic indexing, positional indexes, n-gram indexes, real-world issues. (Chapter - 2) 3 Retrieval Models : Boolean, vector space, TFIDF, Okapi, probabilistic, language modeling, latent semantic indexing. Vector space scoring. The cosine measure. Efficiency considerations. Document length normalization. Relevance feedback and query expansion. Rocchio. (Chapter - 3) 4 Performance Evaluation : Evaluating search engines. User happiness, precision, recall, F-measure. Creating test collections: kappa measure, interjudge agreement. (Chapter - 4) 5 Text Categorization and Filtering : Introduction to text classification. Naive Bayes models. Spam filtering. Vector space classification using hyperplanes; centroids; k Nearest Neighbors. Support vector machine classifiers. Kernel functions. Boosting. (Chapter - 5) 6 Text Clustering : Clustering versus classification. Partitioning methods. k-means clustering. Mixture of Gaussians model. Hierarchical agglomerative clustering. Clustering terms using documents. (Chapter - 6) 7 Advanced Topics : Summarization, Topic detection and tracking, Personalization, Question answering, Cross language information retrieval. (Chapter - 7) 8 Web Information Retrieval : Hypertext, web crawling, search engines, ranking, link analysis, PageRank, HITS. (Chapter - 8) 9 Retrieving Structured Documents : XML retrieval, semantic web. (Chapter - 9)
