Combining TF-IDF with pre-trained Word embeddings

Question

I have a list of website meta-description (128k descriptions; each with avg. 20-30 words), and am trying to build a similarity ranker (as in: show me the 5 most similar sites to this site meta description)

It worked AMAZINGLY well with TF-IDF uni- and bigram, and I thought that I could additionally improve it by adding pre-trained word embeddings (spacy "en_core_web_lg" to be exact). Plot twist: it does not work at all. Literally did not get one good guess, and its suddenly spits out completely random suggestions.

Below is my code. Any thoughts on where I might have gone wrong? Am I overseeing something highly intuitive?

import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
import sys
import pickle
import spacy
import scipy.sparse
from scipy.sparse import csr_matrix
import math
from sklearn.metrics.pairwise import linear_kernel
nlp=spacy.load('en_core_web_lg')


""" Tokenizing"""
def _keep_token(t):
    return (t.is_alpha and 
            not (t.is_space or t.is_punct or 
                 t.is_stop or t.like_num))
def _lemmatize_doc(doc):
    return [ t.lemma_ for t in doc if _keep_token(t)]

def _preprocess(doc_list):     
    return [_lemmatize_doc(nlp(doc)) for doc in doc_list]
def dummy_fun(doc):
    return doc

# Importing List of 128.000 Metadescriptions:
Web_data=open("./data/meta_descriptions","r", encoding="utf-8")
All_lines=Web_data.readlines()
# outputs a list of meta-descriptions consisting of lists of preprocessed tokens:
data=_preprocess(All_lines) 

# TF-IDF Vectorizer:    
vectorizer = TfidfVectorizer(min_df=10,tokenizer=dummy_fun,preprocessor=dummy_fun,)
    tfidf = vectorizer.fit_transform(data)    
dictionary = vectorizer.get_feature_names()

# Retrieving Word embedding vectors:
temp_array=[nlp(dictionary[i]).vector for i in range(len(dictionary))]

# I had to build the sparse array in several steps due to RAM constraints
# (with bigrams the vocabulary gets as large as >1m 
dict_emb_sparse=scipy.sparse.csr_matrix(temp_array[0])
for arr in range(1,len(temp_array),100000):
    print(str(arr))        
    dict_emb_sparse=scipy.sparse.vstack([dict_emb_sparse, scipy.sparse.csr_matrix(temp_array[arr:min(arr+100000,len(temp_array))])])

# Multiplying the TF-IDF matrix with the Word embeddings: 
tfidf_emb_sparse=tfidf.dot(dict_emb_sparse)

# Translating the Query into the TF-IDF matrix and multiplying with the same Word Embeddings:
query_doc= vectorizer.transform(_preprocess(["World of Books is one of the largest online sellers of second-hand books in the world Our massive collection of over million cheap used books also comes with free delivery in the UK Whether it s the latest book release fiction or non-fiction we have what you are looking for"]))
query_emb_sparse=query_doc.dot(dict_emb_sparse)

# Calculating Cosine Similarities:
cosine_similarities = linear_kernel(query_emb_sparse, tfidf_emb_sparse).flatten()

related_docs_indices = cosine_similarities.argsort()[:-10:-1]

# Printing the Site descriptions with the highest match:    
for ID in related_docs_indices:
    print(All_lines[ID])

I stole parts of the code/logic from this Github Rep Does anybody see any straightforward errors here? Many thanks!!

you use the word embeding from spacy, rather than training embeding on your meta description？ — Happy Boy, Feb 26 '19 at 02:16
Yes exactly. I hoped that this would achieve a higher accuracy. Should I have used my own trained embeddings? — benjo121212, Mar 06 '19 at 11:24
@benjo121212 Were you able to find a solution to your question? — StackPancakes, Aug 28 '20 at 19:04

Happy Boy · Accepted Answer · 2019-03-06T11:40:41.630

You should try to train embeding on your own corpus. There are many package: gensim, glove. You can use embeding from BERT without retraining on your own corpus.

You should know that the probability distribution on different corpus is always different. For example, the count of 'basketball' in posts about food is very different from the count of the term in news about sport, so the gap of word embeding of 'basketball' in those corpus is huge.

Combining TF-IDF with pre-trained Word embeddings

1 Answers1