Author: bact

  • kkk

    is for “Khob Khun Krab” or “Thank you” in Thai.

  • Who is this guy dude?

    Who is the first author krub? 😉 Ratanachai Sombatsrisomboon, Yutaka Matsuo, and Mitsuru Ishizuka (2003). Aquisition of Hypernyms and Hyponyms from the WWW, in Proceedings of 2nd Int’l Workshop on Active Mining (AM2003), pp.7-13, Maebashi, Japan (in conjunction with Int’l Sympo. on Methodologies for Intelligent Systems), October, 2003. What does it about?

  • Rush Hour Intro to IR

    by Mirella Lapata (slides for COM3110 Text Processing class, Department of Computer Science, University of Sheffield) breifly explains Google search, IR, issues in IR, indexing, inverted file, boolean model, vector space model, TF/IDF, term weighting, evaluation, precision, recall, and F-measure. introduction | term manipulation & evaluation

  • Managing Gigabytes (Book)

    Sometimes it’s more than just ‘search’. We may want it ‘faster’, and many times we want it ‘smaller’. (And for the case of database/index size, smaller one is probably the faster one — less things to looking for.) Managing Gigabytes: Compressing and Indexing Documents and Images by Ian H. Witten, Alistair Moffat, and Timothy C.…

  • Google File System

    How to search things from a collection is one problem. How to keep things (in a collection) for a searching is another problem. And the latter one could be a really big problem, if you have to keep “3,307,998,701 web pages” like Google does. Google File System: Technical paper, by Sanjay Ghemawat, Howard Gobioff, and…

  • Finding Out About

    This book [R. Belew. Finding Out About: A Cognitive Perspective on Search Engines and the WWW. Cambridge University Press, Cambridge, 2000.] investigates and try to describes IR from the cognitive perspective (what human/user think, percept, behave, ..).

  • Google Weblog

    “Everything you want and don’t want to know about Google” 🙂

  • Multi-Documents Summarization

    Text Summarization, a home of MEAD (a public domain portable multi-document summarization system). a summary of a collection of documents (which may comes from an automatic clustering) will help user decide if he/she wants to investigate that collection further or not — a time saving feature 🙂

  • Thumbshots for search engine — “Stop Guessing. Take Control!” Featuring small picture of each webpage, so users have more clue if it a site they looking for or not. [click here for example]

  • Topic Clustering

    There are more than Vivisimo out there, read about Topic Clustering at Fagan Finder.