https://github.com/prashantranjan09/improved-word-embeddings

Improving Word Embeddings by combining word embeddings with their POS (Part Of Speech) tag.
https://github.com/prashantranjan09/improved-word-embeddings

part-of-speech-tagger pos wordembeddings

Last synced: about 2 months ago
JSON representation

Improving Word Embeddings by combining word embeddings with their POS (Part Of Speech) tag.

• Host: GitHub
• URL: https://github.com/prashantranjan09/improved-word-embeddings
• Owner: PrashantRanjan09
• Created: 2018-06-17T08:55:58.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2020-08-18T17:38:51.000Z (about 5 years ago)
• Last Synced: 2024-04-27T23:38:39.208Z (over 1 year ago)
• Topics: part-of-speech-tagger, pos, wordembeddings
• Language: Python
• Homepage:
• Size: 314 KB
• Stars: 19
• Watchers: 2
• Forks: 7
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Improved-Word-Embeddings

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):

![Optional Text](../master/ImprovedWordEmbeddings.png)

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

    

#### Usage

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