1. Introduction

In this tutorial, we aim to introduce you to the Named Entity Recognition (NER), an important aspect of Natural Language Processing (NLP). By the end of this tutorial, you'll understand what NER is, why it's useful, and how to use it to extract specific entity types from text data.

Prerequisites:
Basic understanding of Python, Machine Learning, and Natural Language Processing.

2. Step-by-Step Guide

NER is a subtask of information extraction that classifies named entities, such as persons, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.

NER can be used in various fields including semantic annotation, content recommendation, social media monitoring, and search optimization.

Here is a simple step-by-step guide on how NER works:

1. Data Preprocessing: Clean the text data by removing unwanted elements such as special characters, punctuations, etc. Tokenize the cleaned text into words.
2. Feature Extraction: Extract features from the preprocessed text. These features can be the part of speech, the previous and next word, etc.
3. Model Training: Train a machine learning model on the extracted features. Commonly used models include Decision Trees, Random Forest, and Conditional Random Field (CRF).
4. Entity Recognition: Use the trained model to recognize and classify entities in new text data.

3. Code Examples

Here's a simple example using the SpaCy library:

import spacy

nlp = spacy.load('en_core_web_sm')
text = "Apple is looking at buying U.K. startup for $1 billion"
doc = nlp(text)

for ent in doc.ents:
    print(ent.text, ent.start_char, ent.end_char, ent.label_)

In this code, we first load the 'en_core_web_sm' model of SpaCy. We then apply the model on the text. The doc.ents property gives us the entities recognized in the text. For each entity, we print the entity text, start and end indices in the original text, and the entity label.

Expected output:

Apple 0 5 ORG
U.K. 27 31 GPE
$1 billion 44 54 MONEY

The labels 'ORG', 'GPE' and 'MONEY' stand for organization, geopolitical entity, and money respectively.

4. Summary

In this tutorial, we introduced Named Entity Recognition (NER) and its importance in Natural Language Processing (NLP). We also looked at how to use the SpaCy library to perform NER on text data.

For further learning, you can explore other libraries such as NLTK, StanfordNLP, and others. You can also learn about other NLP tasks such as sentiment analysis, text classification, and more.

5. Practice Exercises

1. Apply NER on the following text: "Facebook Inc. is planning to open a new office in Seattle next year."

2. Use a different NLP library to perform NER on any text of your choice.

Solutions:

1. The entities in the text are 'Facebook Inc.' (ORG), 'Seattle' (GPE), and 'next year' (DATE).
2. The solution will vary depending on the text and the NLP library chosen. The steps will be similar to those outlined in the step-by-step guide.

Remember, the best way to learn is to practice. Experiment with different texts, libraries, and models to improve your NLP skills.