'Tf term frequency' diaporamas de présentation

Knowledge Management with Documents

Knowledge Management with Documents. Qiang Yang HKUST Thanks: Professor Dik Lee, HKUST. Keyword Extraction. Goal: given N documents, each consisting of words, extract the most significant subset of words ? keywords Example [All the students are taking exams] -- >[student, take, exam]

768 views • 70 slides

Knowledge Management with Documents

Knowledge Management with Documents. Qiang Yang HKUST Thanks: Professor Dik Lee, HKUST. Keyword Extraction. Goal: given N documents, each consisting of words, extract the most significant subset of words ? keywords Example [All the students are taking exams] -- >[student, take, exam]

747 views • 63 slides

Vector Space Model

1.68k views • 62 slides

Special Topics in Computer Science Advanced Topics in Information Retrieval Chapter 2: Modeling

Special Topics in Computer Science Advanced Topics in Information Retrieval Chapter 2: Modeling. Alexander Gelbukh www.Gelbukh.com. Previous chapter. User Information Need Vague Semantic, not formal Document Relevance Order, not retrieve Huge amount of information Efficiency concerns

500 views • 29 slides

IR Homework #2

IR Homework #2. By J. H. Wang Mar. 25, 2008. Programming Exercise #2: Term Weighting. Goal: to assign TF-IDF weights for each index term in inverted files Input : inverted index files (the output of HW#1) Output : term weighting files (exact format to be described later).

220 views • 10 slides