This is a paper implementation of https://arxiv.org/pdf/1711.08609.pdf. This paper increases the accuracy of word embeddings by enriching the word embedding with its associated POS (Part of Speech) tag. (See Below):
However, this excludes the Lexicon2Vec conversion and follows the following steps:
Inputs:
S = {W1, W2,……., Wn} , Input sentence S contains n words
PT = {T1, T2,……, Tm}, All POS tags
Corpus = Imdb/Your corpus
Output:
IMV: Improved word vectors of corpus
The steps follow the following algorithm:
for j=1 to m do
VTj GenerateVector ( Tj )
Tj < Tj , VTj >
end for
for each Wi in S do
If Wi exist in "your choice of word vector" then extract VecWi
MVi VecWi
endif
# POS ExtractPOS ( Wi )
for k=1 to m do
If POS=Tk then ADD VTk into MVi
end if
end for
This gives you the option of testing it on the Imdb data and also to run it on your corpus. To run it on the imdb data:
In config.json assign use_imdb : 1
To run it on your corpus:
In config.json assign use_imdb : 0
The model used is a very generic one. Feel free to make changes to the model as per your requirements. To change model:
In pos_function.py change model_build
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent