自然言語処理

概要

このスキルは、最新の transformers、BERT、GPT、および古典的な NLP 技術を使用してテキスト分類、固有表現認識、感情分析など、包括的な NLP アプリケーション構築ツールを提供します。

使用場面

• 感情分析、トピック分類、インテント検出用のテキスト分類システムの構築
• 非構造化テキストから固有表現（人物、場所、組織）を抽出
• 機械翻訳、テキスト要約、質問応答システムの実装
• 大量のテキストデータの処理と分析
• チャットボット、バーチャルアシスタント、会話型 AI アプリケーションの作成
• ドメイン固有の NLP タスク用に事前学習済みの transformer モデルをファインチューニング

NLP の基本タスク

• テキスト分類: 感情、トピック、インテント分類
• 固有表現認識: 人物、場所、組織の識別
• 機械翻訳: 言語間でのテキスト翻訳
• テキスト要約: 重要情報の抽出
• 質問応答: ドキュメント内で回答を見つける
• テキスト生成: 一貫性のあるテキストの生成

人気のあるモデルとライブラリ

• Transformers: BERT、GPT、RoBERTa、T5
• spaCy: 産業用 NLP パイプライン
• NLTK: クラシック NLP ツールキット
• Hugging Face: 事前学習済みモデルハブ
• PyTorch/TensorFlow: ディープラーニングフレームワーク

Python 実装

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import re
import nltk
from nltk.tokenize import word_tokenize, sent_tokenize
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer, WordNetLemmatizer
import torch
from transformers import (AutoTokenizer, AutoModelForSequenceClassification,
                         AutoModelForTokenClassification, pipeline,
                         TextClassificationPipeline)
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
import warnings
warnings.filterwarnings('ignore')

# Download required NLTK resources
try:
    nltk.data.find('tokenizers/punkt')
except LookupError:
    nltk.download('punkt')

print("=== 1. Text Preprocessing ===")

def preprocess_text(text, remove_stopwords=True, lemmatize=True):
    """Complete text preprocessing pipeline"""
    # Lowercase
    text = text.lower()

    # Remove special characters and digits
    text = re.sub(r'[^a-zA-Z\s]', '', text)

    # Tokenize
    tokens = word_tokenize(text)

    # Remove stopwords
    if remove_stopwords:
        stop_words = set(stopwords.words('english'))
        tokens = [t for t in tokens if t not in stop_words]

    # Lemmatize
    if lemmatize:
        lemmatizer = WordNetLemmatizer()
        tokens = [lemmatizer.lemmatize(t) for t in tokens]

    return tokens, ' '.join(tokens)

sample_text = "The quick brown foxes are jumping over the lazy dogs! Amazing performance."
tokens, processed = preprocess_text(sample_text)
print(f"Original: {sample_text}")
print(f"Processed: {processed}")
print(f"Tokens: {tokens}\n")

# 2. Text Classification with sklearn
print("=== 2. Traditional Text Classification ===")

# Sample data
texts = [
    "I love this product, it's amazing!",
    "This movie is fantastic and entertaining.",
    "Best purchase ever, highly recommended.",
    "Terrible quality, very disappointed.",
    "Worst experience, waste of money.",
    "Horrible service and poor quality.",
    "The food was delicious and fresh.",
    "Great atmosphere and friendly staff.",
    "Bad weather today, very gloomy.",
    "The book was boring and uninteresting."
]

labels = [1, 1, 1, 0, 0, 0, 1, 1, 0, 0]  # 1: positive, 0: negative

# TF-IDF vectorization
tfidf = TfidfVectorizer(max_features=100, ngram_range=(1, 2))
X_tfidf = tfidf.fit_transform(texts)

# Train classifier
clf = MultinomialNB()
clf.fit(X_tfidf, labels)

# Evaluate
predictions = clf.predict(X_tfidf)
print(f"Accuracy: {accuracy_score(labels, predictions):.4f}")
print(f"Precision: {precision_score(labels, predictions):.4f}")
print(f"Recall: {recall_score(labels, predictions):.4f}")
print(f"F1: {f1_score(labels, predictions):.4f}\n")

# 3. Transformer-based text classification
print("=== 3. Transformer-based Classification ===")

try:
    # Use Hugging Face transformers for sentiment analysis
    sentiment_pipeline = pipeline(
        "sentiment-analysis",
        model="distilbert-base-uncased-finetuned-sst-2-english"
    )

    test_sentences = [
        "This is a wonderful movie!",
        "I absolutely hate this product.",
        "It's okay, nothing special.",
        "Amazing quality and fast delivery!"
    ]

    print("Sentiment Analysis Results:")
    for sentence in test_sentences:
        result = sentiment_pipeline(sentence)
        print(f"  Text: {sentence}")
        print(f"  Sentiment: {result[0]['label']}, Score: {result[0]['score']:.4f}\n")

except Exception as e:
    print(f"Transformer model not available: {str(e)}\n")

# 4. Named Entity Recognition (NER)
print("=== 4. Named Entity Recognition ===")

try:
    ner_pipeline = pipeline(
        "ner",
        model="distilbert-base-uncased",
        aggregation_strategy="simple"
    )

    text = "Apple Inc. was founded by Steve Jobs in Cupertino, California."
    entities = ner_pipeline(text)

    print(f"Text: {text}")
    print("Entities:")
    for entity in entities:
        print(f"  {entity['word']}: {entity['entity_group']} (score: {entity['score']:.4f})")

except Exception as e:
    print(f"NER model not available: {str(e)}\n")

# 5. Word embeddings and similarity
print("\n=== 5. Word Embeddings and Similarity ===")

from sklearn.metrics.pairwise import cosine_similarity

# Simple bag-of-words embeddings
vectorizer = CountVectorizer(max_features=50)
docs = [
    "machine learning is great",
    "deep learning uses neural networks",
    "machine learning and deep learning"
]

embeddings = vectorizer.fit_transform(docs).toarray()

# Compute similarity
similarity_matrix = cosine_similarity(embeddings)
print("Document Similarity Matrix:")
print(pd.DataFrame(similarity_matrix, columns=[f"Doc{i}" for i in range(len(docs))],
                  index=[f"Doc{i}" for i in range(len(docs))]).round(3))

# 6. Tokenization and vocabulary
print("\n=== 6. Tokenization Analysis ===")

corpus = " ".join(texts)
tokens, _ = preprocess_text(corpus)

# Vocabulary
vocab = Counter(tokens)
print(f"Vocabulary size: {len(vocab)}")
print("Top 10 most common words:")
for word, count in vocab.most_common(10):
    print(f"  {word}: {count}")

# 7. Advanced Transformer pipeline
print("\n=== 7. Advanced NLP Tasks ===")

try:
    # Zero-shot classification
    zero_shot_pipeline = pipeline(
        "zero-shot-classification",
        model="facebook/bart-large-mnli"
    )

    sequence = "Apple is discussing the possibility of acquiring startup for 1 billion dollars"
    candidate_labels = ["business", "sports", "technology", "politics"]

    result = zero_shot_pipeline(sequence, candidate_labels)
    print("Zero-shot Classification Results:")
    for label, score in zip(result['labels'], result['scores']):
        print(f"  {label}: {score:.4f}")

except Exception as e:
    print(f"Advanced pipeline not available: {str(e)}\n")

# 8. Text statistics and analysis
print("\n=== 8. Text Statistics ===")

sample_texts = [
    "Natural language processing is fascinating.",
    "Machine learning enables artificial intelligence.",
    "Deep learning revolutionizes computer vision."
]

stats_data = []
for text in sample_texts:
    words = text.split()
    chars = len(text)
    avg_word_len = np.mean([len(w) for w in words])

    stats_data.append({
        'Text': text[:40] + '...' if len(text) > 40 else text,
        'Words': len(words),
        'Characters': chars,
        'Avg Word Len': avg_word_len
    })

stats_df = pd.DataFrame(stats_data)
print(stats_df.to_string(index=False))

# 9. Visualization
print("\n=== 9. NLP Visualization ===")

fig, axes = plt.subplots(2, 2, figsize=(14, 10))

# Word frequency
word_freq = vocab.most_common(15)
words, freqs = zip(*word_freq)
axes[0, 0].barh(range(len(words)), freqs, color='steelblue')
axes[0, 0].set_yticks(range(len(words)))
axes[0, 0].set_yticklabels(words)
axes[0, 0].set_xlabel('Frequency')
axes[0, 0].set_title('Top 15 Most Frequent Words')
axes[0, 0].invert_yaxis()

# Sentiment distribution
sentiments = ['Positive', 'Negative', 'Positive', 'Negative', 'Positive']
sentiment_counts = Counter(sentiments)
axes[0, 1].pie(sentiment_counts.values(), labels=sentiment_counts.keys(),
              autopct='%1.1f%%', colors=['green', 'red'])
axes[0, 1].set_title('Sentiment Distribution')

# Document similarity heatmap
im = axes[1, 0].imshow(similarity_matrix, cmap='YlOrRd', aspect='auto')
axes[1, 0].set_xticks(range(len(docs)))
axes[1, 0].set_yticks(range(len(docs)))
axes[1, 0].set_xticklabels([f'Doc{i}' for i in range(len(docs))])
axes[1, 0].set_yticklabels([f'Doc{i}' for i in range(len(docs))])
axes[1, 0].set_title('Document Similarity Heatmap')
plt.colorbar(im, ax=axes[1, 0])

# Text length distribution
text_lengths = [len(t.split()) for t in texts]
axes[1, 1].hist(text_lengths, bins=5, color='coral', edgecolor='black')
axes[1, 1].set_xlabel('Number of Words')
axes[1, 1].set_ylabel('Frequency')
axes[1, 1].set_title('Text Length Distribution')
axes[1, 1].grid(True, alpha=0.3, axis='y')

plt.tight_layout()
plt.savefig('nlp_analysis.png', dpi=100, bbox_inches='tight')
print("\nNLP visualization saved as 'nlp_analysis.png'")

# 10. Summary
print("\n=== NLP Summary ===")
print(f"Texts processed: {len(texts)}")
print(f"Unique vocabulary: {len(vocab)} words")
print(f"Average text length: {np.mean([len(t.split()) for t in texts]):.2f} words")
print(f"Classification accuracy: {accuracy_score(labels, predictions):.4f}")

print("\nNatural language processing setup completed!")

一般的な NLP タスクとモデル

• 分類: DistilBERT、RoBERTa、ELECTRA
• NER: BioBERT、SciBERT、spaCy モデル
• 翻訳: MarianMT、M2M-100
• 要約: BART、Pegasus、T5
• QA: BERT、RoBERTa、DeBERTa

テキスト前処理パイプライン

1. 小文字化とクリーニング
2. トークン化
3. ストップワード除去
4. レンマ化/ステミング
5. ベクトル化

ベストプラクティス

• 利用可能な場合は事前学習済みモデルを使用
• タスク固有のデータでファインチューニング
• 語彙外の単語に対応
• 効率性のためにバッチ処理
• モデルのバイアスを監視

成果物

• 学習済み NLP モデル
• テキスト分類結果
• 抽出された固有表現
• パフォーマンスメトリクス
• ビジュアライゼーションダッシュボード
• 推論 API