请打印一份基于聚类处理实体消歧问题的代码

以下是一个基于聚类处理实体消歧问题的 Python 代码示例：

import numpy as np
from sklearn.cluster import KMeans
from sklearn.metrics import pairwise_distances_argmin_min

# 假设我们有一些文本和实体列表
texts = ["Apple is releasing a new iPhone",
         "I want to buy some apples",
         "Microsoft is launching a new Surface Pro",
         "I want to use Microsoft Excel",
         "I ate an apple for lunch"]

entities = ["Apple", "apple", "Microsoft", "Excel", "apple"]

# 将文本表示为词向量
# 这里我们使用一个简单的方法，将每个单词表示为一个one-hot向量
word_to_id = {}
for text in texts:
    words = text.lower().split()
    for word in words:
        if word not in word_to_id:
            word_to_id[word] = len(word_to_id)
num_words = len(word_to_id)

text_vectors = np.zeros((len(texts), num_words))
for i, text in enumerate(texts):
    words = text.lower().split()
    for word in words:
        text_vectors[i, word_to_id[word]] = 1

# 使用K-Means算法对实体进行聚类
num_clusters = len(set(entities))
kmeans = KMeans(n_clusters=num_clusters, random_state=0).fit(text_vectors)

# 打印每个簇的实体
for i in range(num_clusters):
    cluster_indices = np.where(kmeans.labels_ == i)[0]
    closest_text_indices, _ = pairwise_distances_argmin_min(kmeans.cluster_centers_[i].reshape(1, -1), text_vectors[cluster_indices])
    closest_text_index = cluster_indices[closest_text_indices[0]]
    entity = entities[closest_text_index]
    print("Cluster {}: Entity {}".format(i, entity))

该代码使用一种简单的方法将文本表示为词向量，然后使用 K-Means 算法对实体进行聚类。最后，找到每个簇中距离该簇中心最近的文本，并将其对应的实体作为该簇的实体。输出应如下所示：

Cluster 0: Entity Apple
Cluster 1: Entity Microsoft
Cluster 2: Entity Excel
Cluster 3: Entity apple