Lematisasi

Saat melanjutkan analisis ulasan pengguna, Anda menyadari bahwa stemming kadang menghasilkan kata yang tidak baku seperti "fli" dari "flying", yang dapat menurunkan keterbacaan. Untuk mengatasinya, Anda akan menggunakan lematisasi, yang mengembalikan kata yang sebenarnya dan membantu meningkatkan kejelasan serta ketepatan analisis Anda.

WordNetLemmatizer telah diimpor, stop_words telah didefinisikan, dan sumber daya NLTK yang diperlukan telah diunduh.

Latihan ini adalah bagian dari kursus

Natural Language Processing (NLP) in Python

Petunjuk latihan

Buat instans lemmatizer dari kelas WordNetLemmatizer().
Gunakan lemmatizer untuk melemmatisasi lower_tokens.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

clean_tokens = ['flying', 'lot', 'lately', 'flights', 'keep', 'getting', 'delayed', 'honestly', 'traveling', 'work', 'gets', 'exhausting', 'endless', 'delays', 'every', 'travel', 'teaches', 'something', 'new']

# Create lemmatizer
lemmatizer = ____()

# Lemmatize each token
lemmatized_tokens = [____.____(____) for ____ in clean_tokens]

print(lemmatized_tokens)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Natural Language Processing (NLP) in Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

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: Pengantar natural language processing Exercise 2: Tokenisasi kalimat dan kata Exercise 3: Alur kerja NLP Exercise 4: Penanganan stop words dan tanda baca Exercise 5: Menghapus stop words Exercise 6: Menghapus tanda baca Exercise 7: Teknik normalisasi teks Exercise 8: Mengubah ke huruf kecil Exercise 9: Stemming Exercise 10: Lematisasi

Latihan Saat Ini

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