INTRODUCTION
This is an implementation of GaussianSV - a semantic visualization method for short texts from Le & Lauw (IJCAI 2017).
Usage:
perl gaussiansv.pl --num_topics $num_topics
--dim $dim
--beta $beta
--gamma $gamma
--EM_iter $EM_iter
--Quasi_iter $Quasi_iter
--data $data
--word_vectors $word_vectors_file
--word_vectors_dim $D
--rho $rho
--rho_0 $rho_0
--output_file $output_file
Arguments:
$num_topics: number of topics
$dim: number of dimensions (default 2)
$beta: covariance for Gaussian prior of topic coordinates (default 0.1*$num_docs)
$gamma: covariance for Gaussian prior of document coordinates (default 0.1*$num_topics)
$EM_iter: number of iterations for EM (default 100)
$Quasi_iter: maximum iterations of Quasi-Newton (default 10)
$word_vectors_file: file contains word vectors
$D: dimension of word vector
$rho: hyper-parameter \rho
$rho_0: hyper-parameter \rho_0
$data: input data
$output_file: output file
Details:
-
This implementation needs Algorithm::LBFGS library for quasi-Newton method L-BFGS. The library can be downloaded at http://search.cpan.org/~laye/Algorithm-LBFGS-0.16/lib/Algorithm/LBFGS.pm. To install,
cpan Algorithm::LBFGS -
Example of input data with 3 documents (numbers are ids of words):
0 1 1 2 2 3 4 4 5 6 7 7 8 8 8 8 9 10 11 12 13 13 14 14 15 15 15 16
17 18 19 20 20 21 22 23 24 25 25 25 25 25 25 26 27 27 28 29
30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 50 51 52 53 54 54 55 56 57 58 -
Each line of $word_vectors_file is a word vector whose fields are separated by '\t'. Line numbers correspond to the ids of words.
HOW TO CITE
If you use GaussianSV for your research, please cite:
@inproceedings{gaussiansv,
title={Semantic Visualization for Short Texts with Word Embeddings},
author={Le, Tuan MV and Lauw, Hady W},
booktitle={International Joint Conference on Artificial Intelligence},
year={2017}
}
The paper can be downloaded from: http://www.hadylauw.com/publications/ijcai17a.pdf