NLP and Text Preprocessing

What is NLP?

• Natural Language Processing (NLP) enables machines to understand and process human language.
• NLP is used in chatbots like Siri, Alexa, and Google Assistant.
• NLP is used in search engines like Google and Bing.
• NLP is used in machine translation like Google Translate.

Why Text Preprocessing Matters

• Raw text contains typos, special characters and inconsistent formatting.
• Text preprocessing ensures uniformity and consistency for machine learning models.
• Text preprocessing improves the accuracy, efficiency, and processing speed of NLP models.

Text Preprocessing Steps

• Text cleaning involves removing unwanted elements from the text.
• Tokenization involves splitting the text into individual words
• Sentence segmentation involves dividing the text into sentences
• Normalization involves converting words to standard forms.
• Stopword removal involves filtering out unimportant words.
• Stemming and lemmatization involves reducing words to their root forms.

Real-World Applications of Text Preprocessing

• Search engines use text preprocessing to understand user queries.
• Chatbots and AI assistants use text preprocessing to interpret user messages.
• Text preprocessing identifies unwanted emails in spam detection.
• Sentiment analysis uses text preprocessing to analyze emotions in text.

Challenges in Text Preprocessing

• Different languages require different techniques for text preprocessing.
• Noise in text can come from social media, OCR, and scanned documents.
• Variability in sentence structure, punctuation and abbreviations pose challenges.

Raw vs. Preprocessed Text

• Raw text may contain inconsistencies like "H3IIO!! How r u??".
• Preprocessed text standardizes the format, resulting in "Hello! How are you?".

How Machines Read Text

• Computers process text as numbers using binary/Unicode.
• Encoding mismatches, character corruptions, and special symbols cause challenges.

Goals of Preprocessing

• Reduce redundancy and inconsistency in text.
• Convert text into machine-readable format.
• Improve the efficiency of NLP pipelines.

What is Text Cleaning?

• Text cleaning is removing unwanted characters, symbols, and inconsistencies from the text.
• For example, converting "Hi!!! WELCOME to NLP..." to "Hi Welcome to NLP".

Handling Special Characters & Punctuation

• Remove non-alphanumeric characters like @, #, $, %, &, *.
• Retain important punctuation based on context.

Dealing with Extra Spaces & Line Breaks

• Extra spaces and line breaks are removed and the text is consolidated.
• "' NLP is awesome! '" becomes "'NLP is awesome!'".

Converting to Lowercase

• Converting text to lowercase ensures case uniformity in text processing.
• "HELLO NLP' becomes 'hello nlp'".

Expanding Contractions

• Expanding contractions helps with better word recognition in NLP models.
• "I'm learning NLP' becomes 'I am learning NLP'".

Removing Numbers & Symbols

• Numbers and symbols are removed to streamline the text.
• 'AI has 100+ use cases!!!' becomes 'AI has use cases'.

Removing URLs, Hashtags & Mentions

• URLs, hashtags, and mentions are removed to clean up the text.
• 'Follow @user on Twitter #AI' becomes 'Follow on Twitter'.

Handling Misspellings & Typos

• Misspellings and typos are corrected to ensure accuracy.
• 'Teh NLP moddle' becomes 'The NLP model'.
• Python libraries like TextBlob and pyspellchecker can be used for this.

Removing Emojis & Non-Text Characters

• Emojis and non-text characters are removed to focus on textual content.
• 'I (heart) NLP' becomes 'I love NLP'.

Example of Text Cleaning in Python

• Use the re module to import regular expressions.
• Define text: text = 'H3llo!! I'm learning NLP. Visit https://example.com'
• Clean text: clean_text = re.sub(r'\W+', ' ', text).lower()
• Print clean text: print(clean_text)

What is Tokenization?

• Tokenization involves splitting text into words or phrases.
• For example, 'I love NLP!' becomes ['I', 'love', 'NLP', '!'].

Why is Tokenization Important?

• Tokenization helps NLP models analyze sentence structure.
• Tokenization is needed for word frequency analysis and text generation.

Types of Tokenization

• Whitespace-based tokenization splits text based on spaces.
• Regex-based tokenization splits text based on regular expressions.
• Machine learning-based tokenization uses machine learning models to identify tokens.

Tokenization in English

• "I love machine learning." is tokenized as ['I', 'love', 'machine', 'learning'].

Handling Multiword Expressions

• Multiword expressions like 'New York City' should be treated as a single token.

Tokenizing Hyphenated Words

• In tokenization hyphenated words like 'state-of-the-art' become ['state', 'of', 'the', 'art'].

Example of Tokenization in Python

• Import the NLTK library.
• Use nltk.word_tokenize('I love NLP!') to tokenize the given text.

Challenges in Tokenization

• Handling contractions can be difficult
• Abbreviations can be difficult to tokenize
• Punctuation can cause issues in tokenization.
• Emoji-based text is especially challenging.

Machine Learning-Based Tokenization

• Machine learning-based tokenization uses neural networks to detect word boundaries.

What is Sentence Segmentation?

• Sentence segmentation divides text into meaningful sentences.
• For example, 'Hello. How are you? I'm fine.'

Importance of Sentence Segmentation

• Sentence segmentation is essential for chatbots to respond accurately.
• Sentence segmentation is used for summarization to extract key points.
• Sentence segmentation is used for speech recognition to transcribe spoken text.

Common Sentence Boundary Markers

• Periods (.) mark the end of a sentence.
• Question marks (?) also mark the end of a sentence.
• Exclamation marks (!) can also mark the end of a sentence.

Rule-Based vs. ML-Based Segmentation

• Rule-based segmentation uses regular expressions.
• ML-based segmentation learns from labeled data.

Example of Sentence Segmentation in Python

• Import the nltk library.
• Use nltk.sent_tokenize('Hello! How are you?') to tokenize the given text.

Key Takeaways

• Preprocessing is crucial for NLP.

Common Challenges

• Different languages require different solutions in NLP.

Future of Text Preprocessing

• AI-driven automated text cleaning represents the future of text preprocessing.