Machine learning models require numerical inputs, so raw text must be converted into a numerical format called a vector or text embedding.
Binary and count vectorization create features based on the presence or frequency of words in a document, ignoring their original context.
The Word2Vec model learns numerical representations for words by analyzing their surrounding context, grouping similar words closer together in a multi-dimensional space.
A practical demonstration shows how to clean text data and train a custom Word2Vec model to generate embeddings for a specific vocabulary.
A simple yet effective method to represent an entire document is to retrieve the embedding for each word and calculate their average vector.
The classifier trained on averaged word embeddings achieves 95% accuracy, with errors often occurring on headlines with misleading topics or tones.
Transformer models use a self-attention mechanism to weigh the importance of other words in a sentence, allowing them to understand a word's meaning in its specific context.
BERT learns a deep understanding of language by being pre-trained on tasks like predicting masked words and determining correct sentence order, enabling it to be fine-tuned for specific applications.
Using the Hugging Face Transformers library, a pre-trained DistilBERT model is fine-tuned for the clickbait classification task, requiring specific tokenization with attention masks.
While the fine-tuned BERT model achieves the highest accuracy at 99%, simpler methods like count vectorization can outperform Word2Vec and may be sufficient depending on the use case.
Word embeddings can be combined with other techniques, such as TF-IDF weighting, to extract more signal and improve performance on tasks like sentiment analysis.
Preventing overfitting involves using validation sets, ensuring representative data samples, and checking for feature leakage where a feature inadvertently reveals the outcome.
For rare or out-of-vocabulary terms that models struggle with, symbolic rule-based approaches can be used as a complementary system to handle important edge cases.
Aspiring data scientists should focus on gaining hands-on experience with real-world datasets and building a portfolio of projects to develop an intuition for common issues.
29:40Jodie Burchell
57:46Adrian Schmitt
57:52Iulia Feroli
57:49Jodie Burchell
52:37Kateryna Hrytsaienko
47:28Tanmay Bakshi
56:55Ekaterina Sirazitdinova
27:23Nico Martin
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Jobs that call for the skills explored in this talk.
