数据挖掘技术与应用之NLTK使用

本文聚焦于数据挖掘技术在自然语言处理（NLP）领域的应用，以Python自然语言工具包（NLTK）为核心，探讨其在文本挖掘中的关键功能与实践方法。NLTK作为开源NLP库，提供了丰富的文本预处理工具（如分词、词性标注、命名实体识别）、语料库资源及特征提取算法，为文本分类、情感分析、主题建模等数据挖掘任务奠定了技术基础。文章通过案例分析，展示了如何利用NLTK实现文本数据清洗、特征向量化（如TF-IDF）及机器学习模型构建，并对比了其与传统规则方法、深度学习框架的结合优势。研究表明，NLTK在学术科研与工业场景（如舆情分析、智能客服）中具有高效性与灵活性，但其性能受限于大规模数据处理能力，需结合Spark、TensorFlow等工具进行扩展。最后，文章展望了NLTK在多模态数据挖掘及领域自适应中的潜力，为NLP技术落地提供了实践参考。

实践一：语句相似度的计算

要求计算英文语句"This two-wheeler is really good on slippery roads"和英文语句"This is really good"的相似度，请将两个语句按照相同字典使用Tf-idf方法进行矢量化，然后计算其cosine相似度。

data = ["This two-wheeler is really good on slippery roads"]
sentce=["This is really good"]
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.metrics.pairwise import cosine_similarity

vectorizer = CountVectorizer()
X_train_termcounts = vectorizer.fit_transform(data)
tfidf_transformer = TfidfTransformer()
X_train_tfidf = tfidf_transformer.fit_transform(X_train_termcounts)
print ("\nTfidf of training data:", X_train_tfidf.toarray())

X_input_termcounts = vectorizer.transform(sentce)
X_input_tfidf = tfidf_transformer.transform(X_input_termcounts)
print ("\nTfidf of training data:", X_input_tfidf.toarray())

print("\nCosine of data:",cosine_similarity(X_train_tfidf,X_input_tfidf))

题目二：性别预测

在自然语言处理中通过姓名识别性别是一个非常有意思的实验，这里采用启发式的方法，即姓名的最后几个字符可以界定性别特征，例如一个名字以“la”结尾有可能是女性如“Layla”，以“im”结尾有可能是男性如“Tim”或者“Jim”，现请运行Python脚本nltk-gender.py，将该程序运行机理以及程序是如何进行性别预测写入报告。

import random
from nltk.corpus import names
from nltk import NaiveBayesClassifier
from nltk.classify import accuracy as nltk_accuracy

# Extract features from the input word
def gender_features(word, num_letters=2):
    return {'feature': word[-num_letters:].lower()}

if __name__=='__main__':
    # Extract labeled names
    labeled_names = ([(name, 'male') for name in names.words('male.txt')] +
            [(name, 'female') for name in names.words('female.txt')])

    random.seed(7)
    random.shuffle(labeled_names)
    input_names = ['Leonardo', 'Amy', 'Sam']

    # Sweeping the parameter space
    for i in range(1, 5):
        print ('\nNumber of letters:', i)
        featuresets = [(gender_features(n, i), gender) for (n, gender) in labeled_names]
        train_set, test_set = featuresets[500:], featuresets[:500]
        classifier = NaiveBayesClassifier.train(train_set)

        # Print classifier accuracy
        print ('Accuracy ==>', str(100 * nltk_accuracy(classifier, test_set)) + str('%'))

        # Predict outputs for new inputs
        for name in input_names:
            print (name, '==>', classifier.classify(gender_features(name, i)))# -*- coding: utf-8 -*-

总结