resalaa / aravec Goto Github PK

Advancements in neural networks have led to developments in fields like computer vision, speech recognition and natural language processing (NLP). One of the most influential recent developments in NLP is the use of word embeddings, where words are represented as vectors in a continuous space, capturing many syntactic and semantic relations among them.

AraVec is a pre-trained distributed word representation (word embedding) open source project which aims to provide the Arabic NLP research community with free to use and powerful word embedding models. The first version of AraVec provides six different word embedding models built on top of three different Arabic content domains; Tweets, World Wide Web pages and Wikipedia Arabic articles. The total number of tokens used to build the models amounts to more than 3,300,000,000. This paper describes the resources used for building the models, the employed data cleaning techniques, the carried out preprocessing step, as well as the details of the employed word embedding creation techniques.

The second version of AraVec provides twelve different word embedding models built on top of three different Arabic content domains; Tweets, World Wide Web pages and Wikipedia Arabic articles. The difference between this version and the first, is that the hyper-parameter for minimum count was reduced to 50 instead of 500 for Tweets dataset, 200 for World Wide Web pages dataset and 5 for Wikipedia articels dataset. This resulted in models that have more coverage in terms of vocabulary. The other change, is the we produced a set of six embedding models that have a dimension of 100.

1. Twitter tweets
2. World Wide Web pages
3. Wikipedia Arabic articles

By total tokens of more than 3,300,000,000 tokens.

Abu Bakr Soliman, Kareem Eisa, and Samhaa R. El-Beltagy, “AraVec: A set of Arabic Word Embedding Models for use in Arabic NLP”, in proceedings of the 3rd International Conference on Arabic Computational Linguistics (ACLing 2017), Dubai, UAE, 2017.

These models were built using gensim Python library. Here's a simple code for loading and using one of the models by following these steps:

1. Install gensim using either pip or conda

pip install gensim

conda install gensim

2. extract the compressed model files to a directory [ e.g. Twittert-CBOW ]
3. keep the .npy files. You are gonna to load the file with no extension, like what you'll see in the following code.
4. run this python code to load and use the model

# -*- coding: utf8 -*-
import gensim
import re

# load the model
model = gensim.models.Word2Vec.load('Twittert-CBOW/tweets_cbow_300')

# Clean/Normalize Arabic Text
def clean_str(text):
    search = ["أ","إ","آ","ة","_","-","/",".","،"," و "," يا ",'"',"ـ","'","ى","\\",'\n', '\t','"','?','؟','!']
    replace = ["ا","ا","ا","ه"," "," ","","",""," و"," يا","","","","ي","",' ', ' ',' ',' ? ',' ؟ ',' ! ']
    
    #remove tashkeel
    p_tashkeel = re.compile(r'[\u0617-\u061A\u064B-\u0652]')
    text = re.sub(p_tashkeel,"", text)
    
    #remove longation
    p_longation = re.compile(r'(.)\1+')
    subst = r"\1\1"
    text = re.sub(p_longation, subst, text)
    
    text = text.replace('وو', 'و')
    text = text.replace('يي', 'ي')
    text = text.replace('اا', 'ا')
    
    for i in range(0, len(search)):
        text = text.replace(search[i], replace[i])
    
    #trim    
    text = text.strip()

    return text

# python 3.X
word = clean_str(u'القاهرة')
# python 2.7
# word = clean_str('القاهرة'.decode('utf8', errors='ignore'))

# find and print the most similar terms to a word
most_similar = model.wv.most_similar( word )
for term, score in most_similar:
	print(term, score)
	
# get a word vector
word_vector = model.wv[ word ]

Abu Bakr Soliman, Kareem Eisa, and Samhaa R. El-Beltagy, “AraVec: A set of Arabic Word Embedding Models for use in Arabic NLP”, in proceedings of the 3rd International Conference on Arabic Computational Linguistics (ACLing 2017), Dubai, UAE, 2017.