Skip to content
Switch branches/tags
This branch is 3 commits ahead of abasirat/principal_word_vectors:master.

Latest commit


Git stats


Failed to load latest commit information.
Latest commit message
Commit time

Principal Word Vectors

Principal Word Vectors refer to a set of word vectors (word embeddings) that are built through performing a principal component analysis on a transformed contextual matrix (also known as co-occurrence matrix). The codes in this repository train a set of principal word vectors from both raw and annotated corpora with different types of context including bag-of-words, position-of-words, and indexed context which enables the tool to process an arbitrary context. The codes are tested on Linux and Mac. The requirements are

  • A C compiler (e.g., gcc) to compile the codes in the directory cwvec. There is a Makefile in this directory for this aim.
  • Either octave or python3 (with numpy and scipy) to run the codes in the directory pwvec.

Before running the program, change your directory to cwvec and type make

cd cwvec

The bash script provides you with some parameters that can be set to train a set of word embeddings. Modify the parameters in this file in your desired way and type


We explain the main steps of in such a way that you can train the embeddings step-by-step. The directory cwvec (contextual word vectors) contains codes to construct a contextual matrix.

You should have two executable files in the directory cwvec/build

  • cwvec: to build a contextual matrix
  • print_overflow: to display overflow files created during the construction of a contextual matrix

The ELF file cwvec can process two types of corpus:

  • a raw corpus in which each line is a tokenised sentence
  • an annotated corpus in which each line is a word associated with its features. The words of a sentence are placed in adjacent lines, and an empty line is between sentences.

An example of a raw corpus is:

thats a pretty picture .

Each line of an annotated corpus should be in the following format:



  • 'id' is an integer stating the position of a word in a sentence. It starts at
  • 'word_form' is a word form (usually normalised). We refer to this as the current word
  • 'contexts_ids' is a comma-separated list of integers referring to the ids of the contexts of the current word. Use 0 for the root of a dependency context
  • 'contextual_features' is a comma-separated list of categorical features associated with the current word. Note that the symbol ',' is a reserved character used as a split character

An example of an annotated corpus is:

1	thats	4	that,NOUN,NNS,nsubj
2	a	4	a,DET,DT,det
3	pretty	4	pretty,ADJ,JJ,amod
4	picture	0	picture,NOUN,NN,root
5	.	4	.,PUNCT,.,punct

Note that the delimiter between the columns should be TAB. The python code in can be used to convert a CoNLL-U format file to an annotated corpus as described above.

cwvec has the following options:

$ ./build/cwvec --help
options --input <file.txt>
use the following options
    --corpus-type 	'raw' corpus or 'annotated' corpus (default raw)
    --input	-i	path to input file
    --output	-o	path to output file (default input.bin or input.txt)
    --vocab		path to vocabulary file (default input.vcb)
    --feature		path to feature file (default input.feat)
    --normalize	-n	word normalization (all letters are converted to lowecase format and all sequences of digits are replaced with <num>
    --context-type	-c	the context type, bow (bag-of-word), pow (position-of-word), neighbourhood, or indexed (default bow)
    --load-vocab	load words from vocab file
    --load-features	load features from feature file
    --output-format	-f	'bin' or 'txt' output (default bin)
    --max-memory	-m	the amount of memory (in gigabyte) used for fast matrix access. (default 1.0)
    --overflow-file	overflow file prefix (default overflow)
    --min-vcount	minimum word frequency. Words with frequency smaller than min-vcount are assumed as unknown word (default 1)
    --max-vocab		maximum number of voabulary plus one used for unknown words. Set 0 for infinity. (default 0)
    --min-fcount	minimum feature frequency. Feature with frequency smaller than min-fcount are assumed as unknown feature (default 1).
    --max-feature	maximum number of features plus one used for unknown feature. Set 0 for infinity. (default 0)
    --window	-w	window size (default -1)
    --symmetric		symmetric window of size window_size)
    --print		print cooccurrence matrix on standard output
    --verbose	-v	enable verbose
    --help	-h	print this message

Example of usage:

./build/cwvec --input test/dep_index.txt --corpus-type annotated -c indexed -o test/dep_index.bin -v 


  • dep_index.txt is an annotated corpus
  • dep_index.bin is the corresponding output contextual matrix

In addition to the contextual matrix, cwvec generates a list of vocabulary and features in two files specified by options --vocab and --feature.

Once a contextual matrix (the output of cwvec) is built, the principal word vectors are generated by performing a PCA on that. This can be done by the codes available at the pwvec directory. There are two implmentations, one is a python3 code and the other an octave code. If you are a python user import princ_wvec and contsruct an object based on the class PrincipalWordVectors. Set the cooc_file parameter to the path of the contextual matrix built by cwvec.

import princ_wvec as pwvec
princ_wvec = pwvec.PrincipalWordVector(cooc_file='../../cwvec/test/dep_index.bin', embeddings_file='../../cwvec/test/dep_index.wvec')

These are basically done in the file pwvec/python/

At this stage, the embeddings are in the file dep_index.wvec. It should have the same number of lines as the vocabulary file dep_index.txt.vcb. The lines of these two files are aligned to each other. If we want a single file with both words and vectors, we should merge (paste) these two files.

paste ../../cwvec/test/dep_index.txt.vcb ../../cwvec/test/dep_index.wvec |\
  awk '{printf($1) ; for (i=3;i<=NF;i++) printf(" %s", $i) ; printf("\n")}' > ../../cwvec/test/dep_index.wembed

The file dep_index.wembed should contain a list of words and vectors (word embeddings).


  • Basirat, A., (2018), Principal Word Vectors, Doctoral Thesis, Uppsala University. Series: Studia Linguistica Upsaliensia, ISSN 1652-1366 ; 22
  • Basirat, A., (2018), A Generalized Principal Component Analysis for Word Embedding, Basirat, A., The Seventh Swedish Language Technology Conference (SLTC), Stockholm, Sweden.


We use principal component analysis for word embedding. The method is able to process both annotated and raw corpora.






No releases published


No packages published