Satz- und Wort-Tokenisierung

Die Tokenisierung ist ein wichtiger erster Schritt im NLP. Dabei wird Text in kleinere Einheiten zerlegt, sogenannte Tokens – das ist entscheidend, um mit Sprachdaten zu arbeiten. Deine Aufgabe ist es, einen Ausschnitt eines Nachrichtenartikels sowohl in Sätze als auch in Wörter zu tokenisieren.

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<Kurs>Natural Language Processing (NLP) in Python</Kurs>

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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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<Kurs>Natural Language Processing (NLP) in Python</Kurs>

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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: Einführung in die Verarbeitung natürlicher Sprache Exercise 2: Satz- und Wort-Tokenisierung

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Exercise 3: NLP-Workflow Exercise 4: Umgang mit Stoppwörtern und Satzzeichen Exercise 5: Stoppwörter entfernen Exercise 6: Satzzeichen entfernen Exercise 7: Techniken der Textnormalisierung Exercise 8: Kleinschreibung Exercise 9: Stemming Exercise 10: Lemmatisierung

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