Handling unstructured data during extraction typically involves converting raw data into a structured or semi-structured format for analysis. Three primary methods include text parsing with NLP, machine learning models for non-text data, and cloud-based APIs/services.
1. Text Parsing and Natural Language Processing (NLP): For unstructured text (e.g., emails, PDFs, social media posts), tools like OCR (e.g., Tesseract) extract text from scanned documents or images. Web scraping libraries (e.g., BeautifulSoup, Scrapy) parse HTML/XML to extract specific content. NLP frameworks like spaCy or Hugging Face Transformers then identify entities, keywords, or sentiments. For example, extracting invoice details from a PDF using PyPDF2 and then using spaCy to identify vendor names and dates. This approach structures raw text into queryable data during extraction.
2. Machine Learning Models for Non-Text Data: Unstructured data like images, audio, or video requires ML models to extract meaningful patterns. Convolutional Neural Networks (CNNs) classify images or detect objects (e.g., using OpenCV or TensorFlow). Speech-to-text models (e.g., Whisper) transcribe audio files into text. For instance, a pipeline might use a pre-trained CNN to tag product categories in e-commerce images during data ingestion. These models convert unstructured inputs into structured metadata (e.g., labels, transcriptions) at extraction time.
3. Cloud APIs and Data Lakes: Cloud services like AWS Textract (for document analysis) or Google Vision API (for image labeling) process unstructured data during extraction, returning structured outputs. For example, uploading images to Google Vision API to extract object labels and store them as JSON. Data lakes (e.g., Amazon S3) can also store raw unstructured data with metadata tags (e.g., file type, upload date) for later processing. This balances immediate extraction needs with flexibility for future analysis.