TF-IDF和BM25算法原理及python實現

1 TF-IDF

TF-IDF是英文Term Frequency–Inverse Document Frequency的縮寫，中文叫做詞頻-逆文檔頻率。

一個用戶問題與一個標準問題的TF-IDF相似度，是將用戶問題中每一詞與標準問題計算得到的TF-IDF值求和。計算公式如下：

TF-IDF算法，計算較快，但是存在着缺點，由於它只考慮詞頻的因素，沒有體現出詞彙在文中上下文的地位，因此不能夠很好的突出語義信息。

import numpy as np
class TF_IDF_Model(object):
    def __init__(self, documents_list):
        self.documents_list = documents_list
        self.documents_number = len(documents_list)
        self.tf = []
        self.idf = {}
        self.init()

    def init(self):
        df = {}
        for document in self.documents_list:
            temp = {}
            for word in document:
                temp[word] = temp.get(word, 0) + 1/len(document)
            self.tf.append(temp)
            for key in temp.keys():
                df[key] = df.get(key, 0) + 1
        for key, value in df.items():
            self.idf[key] = np.log(self.documents_number / (value + 1))

    def get_score(self, index, query):
        score = 0.0
        for q in query:
            if q not in self.tf[index]:
                continue
            score += self.tf[index][q] * self.idf[q]
        return score

    def get_documents_score(self, query):
        score_list = []
        for i in range(self.documents_number):
            score_list.append(self.get_score(i, query))
        return score_list

 

2 BM25算法

import numpy as np
from collections import Counter


class BM25_Model(object):
    def __init__(self, documents_list, k1=2, k2=1, b=0.5):
        self.documents_list = documents_list
        self.documents_number = len(documents_list)
        self.avg_documents_len = sum([len(document) for document in documents_list]) / self.documents_number
        self.f = []
        self.idf = {}
        self.k1 = k1
        self.k2 = k2
        self.b = b
        self.init()

    def init(self):
        df = {}
        for document in self.documents_list:
            temp = {}
            for word in document:
                temp[word] = temp.get(word, 0) + 1
            self.f.append(temp)
            for key in temp.keys():
                df[key] = df.get(key, 0) + 1
        for key, value in df.items():
            self.idf[key] = np.log((self.documents_number - value + 0.5) / (value + 0.5))

    def get_score(self, index, query):
        score = 0.0
        document_len = len(self.f[index])
        qf = Counter(query)
        for q in query:
            if q not in self.f[index]:
                continue
            score += self.idf[q] * (self.f[index][q] * (self.k1 + 1) / (
                        self.f[index][q] + self.k1 * (1 - self.b + self.b * document_len / self.avg_documents_len))) * (
                                 qf[q] * (self.k2 + 1) / (qf[q] + self.k2))

        return score

    def get_documents_score(self, query):
        score_list = []
        for i in range(self.documents_number):
            score_list.append(self.get_score(i, query))
        return score_list