PPT-Inferring User Interest Familiarity and Topic Similarity wi

Department of Computer Science KAIST Dabi Ahn Taehun Kim Soon J Hyun Dongman Lee IEEE 2012 Web Intelligence and Intelligent Agent Technology System Workflow Inferring User Interest Using Topic Structure. Sometimes we use latches but not often Latches are smaller in size but create special often dif64257cult situations for testing and static timing analysis Latches are inferred in VHDL by using the IF statement without its matching ELSE This causes t Bamshad Mobasher. DePaul University. Distance or Similarity Measures. Many data mining and analytics tasks involve the comparison of objects and determining . their . similarities (or dissimilarities). Corpora and Statistical Methods. Lecture 6. Semantic similarity. Part 1. Synonymy. Different phonological. /orthographic. words. highly related meanings. :. sofa / couch. boy / lad. Traditional definition:. Instructor: . Dongchul. . kim. Anusha boothpur. 20303325.  . INTRODUCTION. A. ctive . users converse with their . social neighbors . via social activities such as posting comments . one after . another. Quickwrite. : What is happening in this image?. How do you know?. The man is banging his head against the blackboard–. Banging one’s head against the wall can be seen as a sign of frustration or ‘giving up’. Onur İZMİR. Introduction. Consumers. . face. . lots. of . brands. in . the. market.. Decision. . making. . process. is . getting. . harder. . and. . harder. .. C. onsumers. . rely on some certain set of tools in the evaluation of the products to decide whether to buy or . Word . Similarity: . Distributional Similarity (I). Problems with thesaurus-based . meaning. We don’t have a thesaurus for every language. Even if we do, . they have problems with . recall. M. any . W. Jay Dowling. Music Perception and Cognition Laboratory (. MPaC. ). The University of Texas at Dallas. Tonal Hierarchy. Provides a framework for encoding the pitches of a melody. Selects 5-7 pitches out of the 12 semitones to form a “scale”. STANFORD HCI. Niloufar Salehi, Andrew McCabe, . Melissa Valentine, . Michael S. Bernstein. 1. huddler: . convening . stable . and . familiar . crowd teams despite unpredictable . availability. 2. [Little et al. . Text Similarity. Motivation. People can express the same concept (or related concepts) in many different ways. For example, “the plane leaves at 12pm” vs “the flight departs at noon”. Text similarity is a key component of Natural Language Processing. Quiz. Which pair of words exhibits the greatest similarity?. 1. Deer-elk. 2. Deer-horse. 3. Deer-mouse. 4. Deer-roof. Quiz Answer. Which pair of words exhibits the greatest similarity?. 1. Deer-elk. 2. Deer-horse. Sampath Jayarathna. Cal Poly Pomona. Hierarchical Clustering. Build a tree-based hierarchical taxonomy (. dendrogram. ) from a set of documents.. One approach: recursive application of a . partitional. Hydropower Generation. Water Availability. Infrastructure . Component. Climate Conditions. Land Management. Extreme Events. Environment . Component. Policy Makers. Ministries & Managers. Communication. Sketching, Locality Sensitive Hashing. SIMILARITY AND DISTANCE. Thanks to:. Tan, Steinbach, . and Kumar, “Introduction to Data Mining”. Rajaraman. . and . Ullman, “Mining Massive Datasets”. Similarity and Distance.