文章目錄

• • 1. 詞嵌入 Word Embeddings
  • 2. 分類模型
  • 3. 文檔相似度
  • 練習：
  • • 1. 使用文檔向量訓練模型
    • 2. 文本相似度

learn from https://www.kaggle.com/learn/natural-language-processing

1. 詞嵌入 Word Embeddings

參考博文：05.序列模型 W2.自然語言處理與詞嵌入 https://michael.blog.csdn.net/article/details/108886394

類似的詞語有著類似的向量表示，向量間可以相減作類比

• 加載模型

import numpy as np import spacy# Need to load the large model to get the vectors nlp = spacy.load('en_core_web_lg')

• 提取單詞向量

text = "These vectors can be used as features for machine learning models." with nlp.disable_pipes():vectors = np.array([token.vector for token in nlp(text)]) vectors.shape # (12, 300) 12個詞，每個是300維的詞向量

• 合并單詞向量為文檔向量，最簡單的做法是，平均每個單詞的向量

import pandas as pd# Loading the spam data # ham is the label for non-spam messages spam = pd.read_csv('../input/nlp-course/spam.csv')with nlp.disable_pipes():doc_vectors = np.array([nlp(text).vector for text in spam.text]) doc_vectors.shape # (5572, 300)

2. 分類模型

有了文檔向量，你可以使用 sklearn 模型、XGB模型等進行建模

from sklearn.model_selection import train_test_splitX_train, X_test, y_train, y_test = train_test_split(doc_vectors, spam.label, test_size=0.1, random_state=1)

• SVM 的例子

from sklearn.svm import LinearSVC# Set dual=False to speed up training, and it's not needed svc = LinearSVC(random_state=1, dual=False, max_iter=10000) svc.fit(X_train, y_train) print(f"Accuracy: {svc.score(X_test, y_test) * 100:.3f}%", ) Accuracy: 97.312%

3. 文檔相似度

cosine similarity 余弦相似度 $$\cos \theta =\frac{\mathbf{a}?\mathbf{b}}{\Vert \mathbf{a}\Vert \Vert \mathbf{b}\Vert }$$

def cosine_similarity(a, b):return a.dot(b)/np.sqrt(a.dot(a) * b.dot(b)) a = nlp("REPLY NOW FOR FREE TEA").vector b = nlp("According to legend, Emperor Shen Nung discovered tea when leaves from a wild tree blew into his pot of boiling water.").vector cosine_similarity(a, b)

輸出：

0.7030031

練習：

試試你為餐館建立的情緒分析模型。在給定的一些示例文本的數據集中找到最相似的評論。

%matplotlib inlineimport matplotlib.pyplot as plt import numpy as np import pandas as pd import spacy# Set up code checking from learntools.core import binder binder.bind(globals()) from learntools.nlp.ex3 import * print("\nSetup complete")

• 加載模型、數據

# Load the large model to get the vectors nlp = spacy.load('en_core_web_lg')review_data = pd.read_csv('../input/nlp-course/yelp_ratings.csv') review_data.head()

reviews = review_data[:100] # We just want the vectors so we can turn off other models in the pipeline with nlp.disable_pipes():vectors = np.array([nlp(review.text).vector for idx, review in reviews.iterrows()]) vectors.shape # (100, 300) 前100條評論的向量表示

• 為了節省時間，加載已經處理好的所有評論詞向量

# Loading all document vectors from file vectors = np.load('../input/nlp-course/review_vectors.npy')

1. 使用文檔向量訓練模型

• SVM

from sklearn.svm import LinearSVC from sklearn.model_selection import train_test_splitX_train, X_test, y_train, y_test = train_test_split(vectors, review_data.sentiment, test_size=0.1, random_state=1)# Create the LinearSVC model model = LinearSVC(random_state=1, dual=False) # Fit the model model.fit(X_train, y_train)# run to see model accuracy print(f'Model test accuracy: {model.score(X_test, y_test)*100:.3f}%')

輸出：

Model test accuracy: 93.847%

• KNN

# Scratch space in case you want to experiment with other models from sklearn.neighbors import KNeighborsClassifier second_model = KNeighborsClassifier(5) second_model.fit(X_train, y_train) print(f'Model test accuracy: {second_model.score(X_test, y_test)*100:.3f}%')

輸出：

Model test accuracy: 86.998%

2. 文本相似度

• Centering the Vectors

有時在計算相似性時，人們會計算所有文檔的平均向量，然后每個文檔的向量減去這個向量。為什么你認為這有助于相似性度量？

有時候你的文檔已經相當相似了。例如，這個數據集是對企業的所有評論，這些文檔之間有很強的相似度，與新聞文章、技術手冊和食譜相比。最終你得到0.8和1之間的所有相似性，并且沒有反相似文檔（相似性<0）。當中心化向量時，您將比較數據集中的文檔，而不是所有可能的文檔。

• 找到最相似的評論

review = """I absolutely love this place. The 360 degree glass windows with the Yerba buena garden view, tea pots all around and the smell of fresh tea everywhere transports you to what feels like a different zen zone within the city. I know the price is slightly more compared to the normal American size, however the food is very wholesome, the tea selection is incredible and I know service can be hit or miss often but it was on point during our most recent visit. Definitely recommend!I would especially recommend the butternut squash gyoza."""def cosine_similarity(a, b):return np.dot(a, b)/np.sqrt(a.dot(a)*b.dot(b))review_vec = nlp(review).vector## Center the document vectors # Calculate the mean for the document vectors, should have shape (300,) vec_mean = vectors.mean(axis=0) # 平均向量 # Subtract the mean from the vectors centered = vectors - vec_mean # 中心化向量# Calculate similarities for each document in the dataset # Make sure to subtract the mean from the review vector sims = [cosine_similarity(centered_vec, review_vec - vec_mean) for centered_vec in centered]# Get the index for the most similar document most_similar = np.argmax(sims) print(review_data.iloc[most_similar].text)

輸出：

After purchasing my final christmas gifts at the Urban Tea Merchant in Vancouver, I was surprised to hear about Teopia at the new outdoor mall at Don Mills and Lawrence when I went back home to Toronto for Christmas. Across from the outdoor skating rink and perfect to sit by the ledge to people watch, the location was prime for tea connesieurs... or people who are just freezing cold in need of a drinK! Like any gourmet tea shop, there were large tins of tea leaves on the walls, and although the tea menu seemed interesting enough, you can get any specialty tea as your drink. We didn't know what to get... so the lady suggested the Goji Berries... it smelled so succulent and juicy... instantly SOLD! I got it into a tea latte and watched the tea steep while the milk was steamed, and surprisingly, with the click of a button, all the water from the tea can be instantly drained into the cup (see photo).. very fascinating!The tea was aromatic and tasty, not over powering. The price was also very reasonable and I recommend everyone to get a taste of this place :)

• 評論1

• 與評論1最相似的評論

• 看看相似的評論

如果你看看其他類似的評論，你會看到很多咖啡店。為什么你認為咖啡評論和只提到茶的例子評論相似？

咖啡店的評論也將類似于我們的茶館評論，因為咖啡和茶在語義上是相似的。大多數咖啡館都提供咖啡和茶，所以你會經常看到這兩個詞同時出現。

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