The toolkit implements the ngram2vec model proposed in EMNLP2017
Ngram2vec: Learning Improved Word Representation from Ngram Co-occurrence Statistics
ngram2vec toolkit is a natural extension to word2vec, where ngrams are introduced into recent word representation methods inspired by traditional language modeling problem. The toolkit can generate state-of-the-art word embeddings and high-quality ngram embeddings. For example, PPMI achieves 85+ accuracy on Google analogy questions (semantic group).
This toolkit may be also a good startpoint for those who want to learn about word representation models. It includes SGNS, GloVe, PPMI, and SVD and organize them in a pipeline. Arbitrary context features are supported. In terms of efficiency, it enables users to build vocabulary and co-occurrence matrix at a certain memory size. Also, we do optimization on many stages to speed up the process and reduce disk space required.
Firstly, run the following codes to make some files executable.
chmod +x *.sh
chmod +x scripts/clean_corpus.sh
chmod +x word2vecf/word2vecf
chmod +x glovef/build/glove
Also, a corpus should be prepared. We recommend to fetch it at
http://nlp.stanford.edu/data/WestburyLab.wikicorp.201004.txt.bz2 , a wiki corpus without XML tags. scripts/clean_corpus.sh is used for cleaning corpus in this work.
clean_corpus.sh ${corpus} > ${corpus}.clean
run ./uni_uni.sh to see baselines
run ./uni_bi.sh and PPMI of uni_bi type will bring you state-of-the-art results on Google semantic questions (85+)
run ./bi_bi.sh to see significant improvments achieved when ngrams are introduced into SGNS
Note that in this toolkit, we remove low-frequency words with a threshold of 100 to speed up training and evaluation process. One can set thr=10 to reproduce the results reported in the paper.
corpus2vocab builds ngram vocabulary from corpus
corpus2pairs extracts ngram pairs from corpus (multi-threading implementation), used by SGNS model
pairs2vocab generates center word vocabulary and context vocabulary, which are used by all models. (note that the two vocabularies are different. In uni_bi case, center word vocabulary only contains words while context vocabulary contains both words and bigrams)
pairs2counts builds co-occurrence matrix from pairs. We accelerate this stage by using mixed and stripes strategies. By now we only upload a coarse version and we will continue improving this code
counts2ppmi learns PPMI matrix from counts
counts2shuf shuffles the counts
counts2bin transfer counts into binary format, which is supported by glove
glovef To support arbitrary context features, we use a modification of word2vec, word2vecf (implemented by Omer Levy), to train SGNS model. In spirit of word2vecf, we implement glovef upon glove.
This toolkit is inspired by Omer Levy's work http://bitbucket.org/omerlevy/hyperwords
We reuse part of his code in this toolkit.
We build glovef upon glove https://github.com/stanfordnlp/GloVe