Transcription of Document Similarity in Information Retrieval
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Document Similarity in Information Retrieval
Document Similarity in Information Retrieval Mausam (Based on slides of W. Arms, Thomas Hofmann, Ata Kaban, Melanie Martin) Standard Web Search Engine Architecture crawl the web create an inverted index store documents, check for duplicates, extract links inverted index DocIds Slide adapted from Marti Hearst / UC Berkeley] Search engine servers user query show results To user Indexing Subsystem Documents break into tokens stop list* stemming* term weighting* Index database text non-stoplist tokens tokens stemmed terms terms with weights *Indicates optional operation. assign Document IDs documents Document numbers and *field numbers Search Subsystem Index database query parse query stemming* stemmed terms stop list* non-stoplist tokens query tokens Boolean operations* ranking* relevant Document set ranked Document set retrieved Document set *Indicates optional operation.
Noble Brutus hath told you Caesar was ambitious Doc 2 Term Doc # I 1 did 1 enact 1 julius 1 caesar 1 I 1 was 1 killed 1 i' 1 the 1 capitol 1 brutus 1 killed 1 me 1 so 2 let 2 it 2 be 2 with 2 caesar 2 the 2 noble 2 brutus 2 hath 2 told 2 you 2 caesar 2 was 2 Inverted index ambitious2. Later, sort inverted file by terms Term Doc # ambitious 2 be 2
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