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non-distributional

This repository contains data released with the paper on non-distributional word vector representation (Faruqui & Dyer, 2015). We provide here word vectors that have been constructed using non-distributional information. This lexical information has been collected from different linguistic lexicons constrcuted over time in NLP research. For more details please refer to the paper.

Data and Tools

####binary-vectors.txt.gz

This is a word vector file which is very high dimensional and is 99.9% sparse. It contains binary vectors i.e, every word vector has only 1 or 0 as elements. Its best to use this file in a compressed mode as it expands to around 41 GB of text file.

Example vector:-

the 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 ...

####word-feat.txt

Every line of this file contains a word followed by all the features that the word possesses as collected from the group of lexicons in lexicons/ folder. This is an un-expanded form of the word vectors in binary-vectors.txt.gz

Example vector:-

untrustworthiness wn_noun.attribute noun,negative

####create-vector.py

This script takes a lexicon and converts it into a binary vector. We have created binary-vectors.txt.gz using this script from all the files in lexicon/ folder. If you want to create vectors from FrameNet use the following command:-

python create-vector.py < lexicons/framenet.txt > binary-fn-vectors.txt

We created binary-vectors.txt using the following command:-

python create-vector.py < <(cat lexicons/*) > binary-vectors.txt

####lexicons/

Every file in this directory is a lexicon containing the word and the features that it possesses.

###Reference

@InProceedings{faruqui:2015:non-dist,
  author    = {Faruqui, Manaal and Dyer, Chris},
  title     = {Non-distributional Word Vector Representations},
  booktitle = {Proceedings of ACL},
  year      = {2015},
}