<--- Back to Details
First PageDocument Content
Statistical natural language processing / Human resource management / Topic model / Dynamic topic model / Recruitment / Latent Dirichlet allocation / Competence / Job description / Topic and comment
Date: 2015-02-02 08:39:31
Statistical natural language processing
Human resource management
Topic model
Dynamic topic model
Recruitment
Latent Dirichlet allocation
Competence
Job description
Topic and comment

ABOUT MANUSCRIPTS FOR IJ ITA

Add to Reading List

Source URL: www.foibg.com

Download Document from Source Website

File Size: 785,02 KB

Share Document on Facebook

Similar Documents

Linguistics / Computational linguistics / Statistical natural language processing / Natural language processing / Corpus linguistics / Applied linguistics / Speech recognition / Topic model / Latent Dirichlet allocation / N-gram / Stemming / Text corpus

Understanding Text Pre-Processing for Latent Dirichlet Allocation Alexandra Schofield1 M˚ans Magnusson2 Laure Thompson1 David Mimno3 1 Department of Computer Science, Cornell University, Ithaca, NY {xanda, laurejt}@cs.c

DocID: 1xUNS - View Document

HarpLDA+: Optimizing Latent Dirichlet Allocation for Parallel Efficiency Bo Peng1 Shaojuan Zhu3 Bingjing Zhang1 Langshi Chen1 Mihai Avram1 Robert Henschel2 Craig Stewart2 Emily Mccallum3

DocID: 1uZcF - View Document

WarpLDA: a Cache Efficient O(1) Algorithm for Latent Dirichlet Allocation Jianfei Chen†‡ , Kaiwei Li†§ , Jun Zhu†‡ , Wenguang Chen†§ † Dept. of Comp. Sci. & Tech.; TNList Lab; CBICR Center; Tsinghua Univ

DocID: 1uZ6x - View Document

Journal of Machine Learning Research1022 Submitted 2/02; Published 1/03 Latent Dirichlet Allocation David M. Blei

DocID: 1thkm - View Document

Statistical natural language processing / Education / Natural language processing / Latent Dirichlet allocation / Topic model / Learning / Textbook / Information retrieval / Digital textbook / Ranking / Publishing / Bag-of-words model in computer vision

When One Textbook is not Enough: Linking Multiple Textbooks Using Probabilistic Topic Models Julio Guerra12 , Sergey Sosnovsky3 , and Peter Brusilovsky1 1

DocID: 1r8Au - View Document