Zin- en woordtokenization

Tokenization is een belangrijke eerste stap in NLP. Je splitst tekst op in kleinere eenheden, zogenaamde tokens, wat essentieel is om met taaldata te werken. Je taak is om een stukje nieuwsartikel te tokenizen in zowel zinnen als woorden.

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Natural Language Processing (NLP) in Python

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# Import nltk
____
# Download the punkt_tab package 
____

text = """
The stock market saw a significant dip today. Experts believe the downturn may continue.
However, many investors are optimistic about future growth.
"""

# Tokenize the text into sentences
sentences = ____
print(sentences)

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Deze oefening maakt deel uit van de cursus

Natural Language Processing (NLP) in Python

SkillTag.level.intermediateSkillTag.label

4.8+

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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: Introductie tot natural language processing Exercise 2: Zin- en woordtokenization

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Exercise 3: NLP-werkstroom Exercise 4: Stopwoorden en interpunctie verwerken Exercise 5: Stopwoorden verwijderen Exercise 6: Interpunctie verwijderen Exercise 7: Technieken voor het normaliseren van tekst Exercise 8: Kleine letters (lowercasing)Exercise 9: Stemming Exercise 10: Lemmatization

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