I'm working on multilingual word embedding code where I need to train my data on English and test it on Spanish. I'll be using the MUSE library by Facebook for the word-embeddings. I'm looking for a way to pre-process both my data the same way. I've looked into diacritics restoration to deal with the accents.

I'm having trouble coming up with a way in which I can carefully remove stopwords, punctuations and weather or not I should lemmatize.

How can I uniformly pre-process both the languages to create a vocabulary list which I can later use with the MUSE library.

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Hi Chandana I hope you're doing well. I would look into using the library spaCy https://spacy.io/api/doc the man that created it has a youtube video in which he discusses the implementation of of NLP in other languages. Below you will find code that will lemmatize and remove stopwords. as far as punctuation you can always set specific characters such as accent marks to ignore. Personally I use KNIME which is free and open source to do preprocessing. You will have to install nlp extentions but what is nice is that they have different extensions for different languages you can install here: https://www.knime.com/knime-text-processing the Stop word filter (since 2.9) and the Snowball stemmer node can be applied for Spanish language. Make sure to select the right language in the dialog of the node. Unfortunately there is no part of speech tagger node for Spanish so far.

# Create functions to lemmatize stem, and preprocess

# turn beautiful, beautifuly, beautified into stem beauti 
def lemmatize_stemming(text):
    stemmer = PorterStemmer()
    return stemmer.stem(WordNetLemmatizer().lemmatize(text, pos='v'))

# parse docs into individual words ignoring words that are less than 3 letters long
# and stopwords: him, her, them, for, there, ect since "their" is not a topic.
# then append the tolkens into a list
def preprocess(text):
    result = []
    for token in gensim.utils.simple_preprocess(text):
        newStopWords = ['your_stopword1', 'your_stop_word2']
        if token not in gensim.parsing.preprocessing.STOPWORDS and token not in newStopWords and len(token) > 3:
            nltk.bigrams(token)
            result.append(lemmatize_stemming(token))
    return result

I hope this helps let me know if you have any questions :)