This is a toolkit developed for learning the word representations. PDC and HDC are two unsupervised learning algorithms for word representations using both syntagmatic and paradigmatic relations via a joint training objective.

Contact: Fei Sun, Institute Of Computing Technology, [email protected], 
Project page: http://ofey.me/projects/wordrep

Requirements

To complile the souce codes, some external packages are required

• C++11
• Eigen
• OpenMP (for multithread)

Input

Each line of the input file represents a document in corpus.

... The cat sat on the mat. ...
... The quick brown fox jumps over the lazy dog. ...

Run

./w2v -train data.txt -word_output vec.txt -size 200 -window 5 -subsample 1e-4 -negative 5 -model pdc -binary 0 -iter 5

• -train, the input file of the corpus, each line a document;
• -word_output, the output file of the word embeddings;
• -binary, whether saving the output file in binary mode; the default is 0 (off);
• -word_size, the dimension of word embeddings; the default is 100;
• -doc_size, the dimension of word embeddings; the default is 100;
• -window, max skip length between words; default is 5;
• -negative, the number of negative samples used in negative sampling; the deault is 5;
• -subsample, parameter for subsampling; default is 1e-4;
• -threads, the total number of threads used; the default is 1.
• -alpha, the starting learning rate; default is 0.025 for HDC and 0.05 for PDC;
• -model, model used to learn the word embeddings; default is Parallel Document Context model(pdc) (use hdc for Hierarchical Document Context model)
• -min-count, the threshold for occurrence of words; default is 5;
• -iter, the number of iterations; default is 5;

@InProceedings{P15-1014,
author="Sun, Fei and Guo, Jiafeng and Lan, Yanyan and Xu, Jun and Cheng, Xueqi",
title="Learning Word Representations by Jointly Modeling Syntagmatic and Paradigmatic Relations",
booktitle="Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
year="2015",
publisher="Association for Computational Linguistics",
pages="136--145",
location="Beijing, China",
url="http://aclweb.org/anthology/P15-1014"
}