将TF-IDF与预训练的单词嵌入结合在一起

Question

我有一个网站元描述的列表（128k描述；每个都有20-30个单词），并试图构建一个相似的等级器（如：向我展示5与此站点元描述的大多数相似站点）

它与TF-IDF Uni-and BigRam >非常出色，我认为我可以通过添加预训练的单词嵌入式（spacy＆quot" spacy＆quot"确切地"）。情节扭曲：它根本不起作用。从字面上看，没有一个很好的猜测，它突然吐出了完全随机的建议。

以下是我的代码。对我可能出错的地方有什么想法吗？我是在监督高度直观的东西吗？

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从此github rep偷走了代码/逻辑的一部分有人在这里看到任何直接错误吗？非常感谢！

Answer 1

您应该尝试训练嵌入自己的语料库。有很多包装：Gensim，手套。您可以在不在自己的语料库上进行重新培训的情况下使用嵌入。

您应该知道，不同语料库上的概率分布总是不同的。例如，关于食物的帖子中的"篮球"的数量与有关运动的新闻术语的数量大不相同，因此在这些语料库中嵌入"篮球"的单词差距很大。