This chapter will introduce you to the basics of text processing with spaCy. You'll learn about the data structures, how to work with statistical models, and how to use them to predict linguistic features in your text.

spaCy'ye giriş

Başlarken

Belgeler, aralıklar ve belirteçler

Sözcüksel öznitelikler

İstatistiksel modeller

Model paketleri

Modelleri yükleme

Dilbilimsel açıklamaları tahmin etme

Bağlam içinde adlandırılmış varlıkları tahmin etme

Kural tabanlı eşleştirme

Matcher'ı kullanma

Eşleşme kalıpları yazma

Finding words, phrases, names and concepts

In this chapter, you'll use your new skills to extract specific information from large volumes of text. You'll learn how to make the most of spaCy's data structures, and how to effectively combine statistical and rule-based approaches for text analysis.

Data Structures (1)

Strings to hashes

Vocab, hashes and lexemes

Data Structures (2)

Creating a Doc

Docs, spans and entities from scratch

Data structures best practices

Word vectors and similarity

Inspecting word vectors

Comparing similarities

Combining models and rules

Debugging patterns (1)

Debugging patterns (2)

Efficient phrase matching

Extracting countries and relationships

Large-scale data analysis with spaCy

This chapter will show you to everything you need to know about spaCy's processing pipeline. You'll learn what goes on under the hood when you process a text, how to write your own components and add them to the pipeline, and how to use custom attributes to add your own meta data to the documents, spans and tokens.

Processing pipelines

What happens when you call nlp?

Inspecting the pipeline

Custom pipeline components

Use cases for custom components

Simple components

Complex components

Extension attributes

Setting extension attributes (1)

Setting extension attributes (2)

Entities and extensions

Components with extensions

Scaling and performance

Processing streams

Processing data with context

Selective processing

Processing Pipelines

In this chapter, you'll learn how to update spaCy's statistical models to customize them for your use case – for example, to predict a new entity type in online comments. You'll write your own training loop from scratch, and understand the basics of how training works, along with tips and tricks that can make your custom NLP projects more successful.

Training and updating models

Purpose of training

Creating training data (1)

Creating training data (2)

The training loop

Setting up the pipeline

Building a training loop

Exploring the model

Training best practices

Good data vs. bad data

Training multiple labels

Wrapping up

Training a neural network model

Eğer çok miktarda metinle çalışıyorsan, bir süre sonra bu metinler hakkında daha fazla şey bilmek isteyeceksin. Örneğin, konu ne? Sözcükler bağlam içinde ne anlama geliyor? Kim kime ne yapıyor? Hangi şirket ve ürünlerden bahsediliyor? Hangi metinler birbirine benziyor? Bu derste, Python'da NLP için hızla endüstri standardı haline gelen spaCy kütüphanesini kullanarak, kural tabanlı ve Machine Learning yaklaşımlarını birleştirip gelişmiş doğal dil anlama sistemleri kurmayı öğreneceksin.

Introduction to Natural Language Processing in Python

SpaCy ile kural tabanlı ve makine öğrenimi yaklaşımlarıyla ileri düzey dil anlama sistemleri oluşturun.

spaCy ile İleri Düzey NLP

Kural tabanlı ve makine öğrenimi yaklaşımlarını kullanarak spaCy ile gelişmiş doğal dil anlama sistemleri oluşturmayı öğrenin.

Model paketleri

spaCy ile İleri Düzey NLP

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