<--- Back to Details
First PageDocument Content
Learning to rank / Text Retrieval Conference / XML-Retrieval / Search engine indexing / Document retrieval / Tf*idf / Character encodings in HTML / Precision and recall / Google Search / Information science / Information retrieval / Science
Date: 2013-10-16 04:53:55
Learning to rank
Text Retrieval Conference
XML-Retrieval
Search engine indexing
Document retrieval
Tf*idf
Character encodings in HTML
Precision and recall
Google Search
Information science
Information retrieval
Science

Microsoft Word - _paged_overlay_100.doc

Add to Reading List

Source URL: www.aviarampatzis.com

Download Document from Source Website

File Size: 700,32 KB

Share Document on Facebook

Similar Documents

Quick and Reliable Document Alignment via TF/IDF-weighted Cosine Distance Christian Buck Philipp Koehn University of Edinburgh Center for Language and Speech Processing

Quick and Reliable Document Alignment via TF/IDF-weighted Cosine Distance Christian Buck Philipp Koehn University of Edinburgh Center for Language and Speech Processing

DocID: 1tmqa - View Document

Back to Our Roots for Retrieving Very Short Passages Nada Naji University of Neuchatel, Computer Science Dept. Rue Emile-Argand 11, 2000 Neuchatel Switzerland

Back to Our Roots for Retrieving Very Short Passages Nada Naji University of Neuchatel, Computer Science Dept. Rue Emile-Argand 11, 2000 Neuchatel Switzerland

DocID: 1gC3e - View Document

Microsoft Word - _paged_overlay_100.doc

Microsoft Word - _paged_overlay_100.doc

DocID: 1gzhS - View Document

A Survey of Feature Location Techniques Julia Rubin and Marsha Chechik Abstract Feature location techniques aim at locating software artifacts that implement a specific program functionality, a.k.a. a feature. These tech

A Survey of Feature Location Techniques Julia Rubin and Marsha Chechik Abstract Feature location techniques aim at locating software artifacts that implement a specific program functionality, a.k.a. a feature. These tech

DocID: 1gwU0 - View Document

Splitting Models Using Information Retrieval and Model Crawling Techniques Daniel Str¨uber1 , Julia Rubin2,3 , Gabriele Taentzer1 , and Marsha Chechik3 1  Philipps-Universit¨at Marburg, Germany

Splitting Models Using Information Retrieval and Model Crawling Techniques Daniel Str¨uber1 , Julia Rubin2,3 , Gabriele Taentzer1 , and Marsha Chechik3 1 Philipps-Universit¨at Marburg, Germany

DocID: 1gwdL - View Document