2

不久前，我有一个 Google Colab 笔记本，它使用 spacy 2.2.4 并成功获取了单词列表中最相似的单词：

import spacy
import spacy.cli
spacy.cli.download("en_core_web_lg")
import en_core_web_lg
nlp = en_core_web_lg.load()
import numpy as np
import pandas as pd

print(spacy.__version__)

all_search_terms = ["technology", "internet", "smartphone"]

# define a function to get the x most similar words to a word
def most_similar(word, topn=2):
    print(word)
    word = nlp.vocab[str(word)]
    print(word.prob)
    queries = [
        w for w in word.vocab 
        if w.is_lower == word.is_lower and w.prob >= -15 and np.count_nonzero(w.vector)
    ]

    by_similarity = sorted(queries, key=lambda w: word.similarity(w), reverse=True)
    return [(w.lower_,w.similarity(word)) for w in by_similarity[:topn+1] if w.lower_ != word.lower_]


# create function to receive a list of words and return the 
# top 2 similar words for each word in the list

def get_similar_words(list_of_words):
    
    all_similar_words = []
    
    for word in list_of_words:
        spacy_word = nlp.vocab[str(word)]
        if spacy_word.has_vector:
        
            # find similar words to the word, and store them in a dataframe along with their scores
            similar_words = pd.DataFrame(most_similar(word, topn=2), columns=["word", "similarity_score"])

            # save the list of similar words
            similar_words_list = list(similar_words["word"])

            # append the list of similar words to the list to be returned
            all_similar_words.append(similar_words_list)
        
    # flatten the list of lists to one list
    all_similar_words = [item for sublist in all_similar_words for item in sublist]
    
    # remove duplicates from the list
    all_similar_words = list(dict.fromkeys(all_similar_words))
    
    # sort list in alphabetical order
    all_similar_words.sort()

    return all_similar_words


# run the function on the search terms entered by the user
new_search_terms = get_similar_words(all_search_terms)
new_search_terms

输出是：

technology
-10.063644409179688
internet
-8.897857666015625
smartphone
-12.11159896850586
['handset', 'online', 'smartphones', 'technological', 'technologies', 'web']

问题：我刚刚尝试在 RStudio 的不同环境中运行相同的代码（即不使用 Google Colab），其中 spacy 的版本是 3.0.6 并且相似词列表（new_search_terms）是空的。我还注意到单词概率都是相同的 (-20)。

spacy 3.0.6 的输出：

technology
-20.0
internet
-20.0
smartphone
-20.0
[]

在这个新版本的 spacy 中，我需要做些什么不同的事情才能获得与以前相同的输出？

4

1 回答 1

2

在 v3 中默认情况下不加载令牌概率，因此您必须做一些事情来加载它们。

import spacy
from spacy.lookups import load_lookups
nlp = spacy.load("en_core_web_sm")
lookups = load_lookups("en", ["lexeme_prob"])
nlp.vocab.lookups.add_table("lexeme_prob", lookups.get_table("lexeme_prob"))

在此之后，您的代码应该可以工作，尽管我不确定您为什么在.prob这里使用。

于 2021-07-03T07:35:17.273 回答