Question

我在python中正在做一个Question Answering项目。我已经有问题和答案文件的矢量以及tfidf的值。但后来我不知道如何在python中计算相似性匹配。

Answer 1

您可以在两个向量之间使用Euclidean distance，或者使用另一个距离度量（例如Hamming distance）或向量的cross-correlation。

Answer 2

余弦相似

length_question = .0
length_answer = .0

for word_tfidf in question:
    length_question += word_tfidf**2

for word_tfdif in answer:
     length_answer += word_tfidf**2

similarity = .0
for word in question:
    question_word_tfidf = question[word]
    answer_word_tfidf = answer.get(word, 0)
    similarity += question_word_tfidf * answer_word_tfidf
similarity /= math.sqrt(length_question * length_answer)

Answer 3

您可以使用Levenshtein距离，在此处查看：http://en.wikibooks.org/wiki/Algorithm_Implementation/Strings/Levenshtein_distance#Python代码，此处：http://en.wikipedia.org/wiki/Levenshtein_distance用于讨论算法。

以下是从上述链接复制的代码段：

def levenshtein(s1, s2):
    if len(s1) < len(s2):
        return levenshtein(s2, s1)
    if not s1:
        return len(s2)

    previous_row = xrange(len(s2) + 1)
    for i, c1 in enumerate(s1):
        current_row = [i + 1]
        for j, c2 in enumerate(s2):
            insertions = previous_row[j + 1] + 1 # j+1 instead of j since previous_row and current_row are one character longer
            deletions = current_row[j] + 1       # than s2
            substitutions = previous_row[j] + (c1 != c2)
            current_row.append(min(insertions, deletions, substitutions))
        previous_row = current_row

    return previous_row[-1]

python中的相似性匹配计算

3 个答案: