1. Introduction to NLP

Introduction to Natural Language Processing (NLP)

Overview of NLP:
Natural Language Processing (NLP) is a field of artificial intelligence (AI) that focuses on the interaction between computers and human language. It involves the development of algorithms and models to enable computers to understand, interpret, and generate human language data.

Applications of NLP:
NLP has a wide range of applications across various domains, including:

• Text Classification: Categorizing text documents into predefined categories.
• Sentiment Analysis: Determining the sentiment expressed in a piece of text (positive, negative, or neutral).
• Machine Translation: Translating text from one language to another automatically.
• Named Entity Recognition (NER): Identifying and classifying named entities (e.g., names of people, organizations, locations) in text.
• Text Summarization: Generating concise summaries of longer text documents.
• Question Answering: Automatically answering questions posed in natural language.

Challenges in NLP:
Despite its advancements, NLP still faces several challenges, including:

• Ambiguity: Natural language is inherently ambiguous, leading to challenges in understanding and interpreting meaning.
• Data Sparsity: NLP models require large amounts of annotated data for training, which may not always be readily available.
• Domain Adaptation: NLP models trained on one domain may not generalize well to other domains.
• Ethical Considerations: Issues such as bias and fairness need to be carefully addressed to ensure that NLP systems are ethical and inclusive.

Basics of Text Preprocessing:
Text preprocessing is an essential step in NLP that involves transforming raw text data into a format suitable for further analysis. Common preprocessing techniques include:

• Tokenization: Splitting text into individual words or tokens.
• Stemming: Removing affixes from words to obtain their root form.
• Lemmatization: Similar to stemming, but produces valid words (lemmas) using vocabulary and morphological analysis.

Lab Activity: Implementing Basic Text Preprocessing Techniques
In this lab activity, we will implement basic text preprocessing techniques using Python libraries such as NLTK or SpaCy.

Tokenization:

        import nltk
        from nltk.tokenize import word_tokenize
        text = "Natural Language Processing is a fascinating field!"
        tokens = word_tokenize(text)
        print(tokens)
    

Stemming:

        from nltk.stem import PorterStemmer
        stemmer = PorterStemmer()
        word = "running"
        stemmed_word = stemmer.stem(word)
        print(stemmed_word)
    

Lemmatization:

        from nltk.stem import WordNetLemmatizer
        lemmatizer = WordNetLemmatizer()
        word = "better"
        lemma = lemmatizer.lemmatize(word, pos='a')  # 'a' specifies adjective
        print(lemma)