Anotasi linguistik di spaCy

Anotasi linguistik yang tersedia di spaCy, seperti penandaan POS (POS tagging), named-entity recognition, dan dependency parsing, dapat digunakan untuk memahami kualitas tulisan dengan lebih baik serta mempelajari berbagai aspek bahasa manusia, termasuk kata, kalimat, dan makna. Dalam latihan ini, Anda akan mempraktikkan pemahaman Anda pada berbagai fitur linguistik.

Latihan ini adalah bagian dari kursus

Pemrosesan Bahasa Alami dengan spaCy

Latihan interaktif praktis

Ubah teori menjadi tindakan dengan salah satu latihan interaktif kami.

Mulai berolahraga

Latihan ini adalah bagian dari kursus

Pemrosesan Bahasa Alami dengan spaCy

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

This chapter will introduce you to NLP, some of its use cases such as named-entity recognition and AI-powered chatbots. You’ll learn how to use the powerful spaCy library to perform various natural language processing tasks such as tokenization, sentence segmentation, POS tagging, and named entity recognition.

Exercise 1: Natural Language Processing (NLP) basics Exercise 2: Doc container in spaCy Exercise 3: NER use case Exercise 4: Tokenization with spaCy Exercise 5: spaCy basics Exercise 6: Running a spaCy pipeline Exercise 7: Lemmatization with spaCy Exercise 8: Sentence segmentation with spaCy Exercise 9: Linguistic features in spaCy Exercise 10: POS tagging with spaCy Exercise 11: NER with spaCy Exercise 12: Text processing with spaCy

Learn about linguistic features, word vectors, semantic similarity, analogies, and word vector operations. In this chapter you’ll discover how to use spaCy to extract word vectors, categorize texts that are relevant to a given topic and find semantically similar terms to given words from a corpus or from a spaCy model vocabulary.

Exercise 1: Fitur linguistik Exercise 2: Anotasi linguistik di spaCy

Latihan Saat Ini

Exercise 3: Disambiguasi makna kata dengan spaCy Exercise 4: Dependency parsing dengan spaCy Exercise 5: Pengantar word vector Exercise 6: Kosakata spaCy Exercise 7: Vektor kata dalam kosakata spaCy Exercise 8: Vektor kata dan spaCy Exercise 9: Analogi dan operasi vektor Exercise 10: Proyeksi word vectors Exercise 11: Kata-kata serupa dalam sebuah kosakata Exercise 12: Mengukur kesamaan semantik dengan spaCy Exercise 13: Kesamaan Doc dengan spaCy Exercise 14: Kemiripan Span dengan spaCy Exercise 15: Kemiripan semantik untuk mengategorikan teks

Get familiar with spaCy pipeline components, how to add a pipeline component, and analyze the NLP pipeline. You will also learn about multiple approaches for rule-based information extraction using EntityRuler, Matcher, and PhraseMatcher classes in spaCy and RegEx Python package.

Exercise 1: spaCy pipelines Exercise 2: Adding pipes in spaCy Exercise 3: Analyzing pipelines in spaCy Exercise 4: spaCy EntityRuler Exercise 5: EntityRuler with blank spaCy model Exercise 6: EntityRuler for NER Exercise 7: EntityRuler with multi-patterns in spaCy Exercise 8: RegEx with spaCy Exercise 9: RegEx in Python Exercise 10: RegEx with EntityRuler in spaCy Exercise 11: spaCy Matcher and PhraseMatcher Exercise 12: Matching a single term in spaCy Exercise 13: PhraseMatcher in spaCy Exercise 14: Matching with extended syntax in spaCy

Explore multiple real-world use cases where spaCy models may fail and learn how to train them further to improve model performance. You’ll be introduced to spaCy training steps and understand how to train an existing spaCy model or from scratch, and evaluate the model at the inference time.

Exercise 1: Customizing spaCy models Exercise 2: Training spaCy models Exercise 3: Model performance on your data Exercise 4: spaCy training data format Exercise 5: Training steps Exercise 6: Annotation and preparing training data Exercise 7: Compatible training data Exercise 8: Training with spaCy Exercise 9: Training preparation steps Exercise 10: Train an existing NER model Exercise 11: Training a spaCy model from scratch Exercise 12: Wrap-up