Durdurma sözcüklerini kaldırma

"ürün sorunları", "hizmet sorunları" ve "öneriler" gibi kategorilere kullanıcı geri bildirimlerini sınıflandırmayı hedefleyen bir projede çalışıyorsun. Çoğu zaman durdurma sözcükleri kategorileri ayırt etmede pek anlam taşımaz. Görevin, bu durdurma sözcüklerini kaldırarak, geri bildirimi daha sonra doğru başlıklara ayıracak bir makinenin işine yarayacak önemli sözcüklere odaklanmak.

nltk.tokenize içindeki word_tokenize ve nltk.corpus içindeki stopwords.words işlevleri senin için içe aktarıldı. Ayrıca, NLTK kaynakları punkt_tab ve stopwords önceden indirildi.

Bu egzersiz

Python ile Natural Language Processing (NLP)

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Verilen geri bildirimi sözcüklere ayır.
İngilizce durdurma sözcükleri listesini al.
İngilizce durdurma sözcüklerini kaldır ve sonucu filtered_tokens içinde kaydet.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

feedback = "I reached out to support and got a helpful response within minutes!!! Very #impressed"

# Tokenize the text
tokens = word_tokenize(____)

# Get the list of English stop words
stop_words = stopwords.____('____')

# Remove stop words 
filtered_tokens = [____ for word in tokens if ____.lower() not in ____]

print(filtered_tokens)

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python ile Natural Language Processing (NLP)

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

Learn the essentials of text processing in Natural Language Processing (NLP). Master techniques such as tokenization, stop word and punctuation removal, and text normalization with lowercasing, stemming, and lemmatization to prepare text data for further analysis and insight extraction.

Exercise 1: Doğal dil işlemenin temellerine giriş Exercise 2: Cümle ve kelime tokenizasyonu Exercise 3: NLP iş akışı Exercise 4: Stop words ve noktalama işaretlerini işleme Exercise 5: Durdurma sözcüklerini kaldırma

Geçerli Egzersiz

Exercise 6: Noktalama işaretlerini kaldırma Exercise 7: Metin normalleştirme teknikleri Exercise 8: Küçük harfe çevirme Exercise 9: Stemming Exercise 10: Lemmatizasyon

Transform raw text into powerful numerical features. Create Bag-of-Words and TF-IDF representations to capture word importance across documents, then explore word embeddings like Word2Vec and GloVe to uncover deep semantic patterns. Visualize frequency, relevance, and similarity to bring your text data to life.

Exercise 1: Bag-of-Words representation Exercise 2: Building vocabulary from customer reviews Exercise 3: Transforming text to numbers with BoW Exercise 4: Frequency analysis of product reviews Exercise 5: Visualizing word frequencies Exercise 6: TF-IDF vectorization Exercise 7: TF-IDF representation of product feedback Exercise 8: Comparing BoW and TF-IDF representations Exercise 9: Embeddings Exercise 10: Exploring word relationships with embeddings Exercise 11: Visualizing and comparing word embeddings

Harness the power of pre-trained models to perform advanced text classification tasks. Use Hugging Face pipelines for sentiment analysis, topic classification, and natural language inference. Evaluate semantic similarity and grammatical correctness with state-of-the-art models, all without building anything from scratch.

Exercise 1: Hugging Face pipelines for sentiment analysis Exercise 2: Analyzing the sentiment of a review Exercise 3: Batch classifying multiple reviews Exercise 4: Comparing models on labeled review data Exercise 5: Zero-shot classification and QNLI Exercise 6: Zero-shot classification of support tickets Exercise 7: Does the text answer the question?Exercise 8: Question similarity and grammatical correctness Exercise 9: Detecting duplicate questions Exercise 10: Checking grammatical correctness

Dive into the core of modern NLP applications with token classification and text generation techniques. Learn to extract meaningful entities and grammatical structures using NER and PoS tagging. Master both extractive and abstractive question answering, and explore advanced generation tasks including summarization, translation, and language modeling using Hugging Face pipelines.

Exercise 1: Token classification Exercise 2: Identifying named entities in news headlines Exercise 3: Part of Speech tagging for text analysis Exercise 4: Question answering Exercise 5: Answering questions from product descriptions Exercise 6: Generating natural answers with abstractive QA Exercise 7: Sequence generation tasks Exercise 8: Summarizing news articles for quick insights Exercise 9: Translating customer reviews to French Exercise 10: Building a search completion system Exercise 11: Congratulations