Text, speech, translation, and retrieval with modern NLP techniques.
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Ever wondered how ChatGPT can finish your sentences, explain complicated topics, or write an entire story from a single prompt? It might seem like the AI understands your question and then magically produces an answer. But one of the fundamental…
There are various tasks in natural language processing, and it is a field where it is easy to confuse their meanings and differences during an actual exam. Many people are often unsure, especially about how it differs from natural language…
Why king - man + woman lands on queen: word2vec factorizes a hidden table of word-pair statistics, and the analogy works only if king is struck out. Why King − Man + Woman = Queen: What a Word Embedding Actually Is Word2vec, the 2013 method everyone called a…
NLP Landscape from 1960 to 2020: From Rule-Based Systems to Transformers A Historical Case Study of Natural Language Processing Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that enables computers to process, analyze, and generate human language. From early…
Author: Jackie H. Blankenship (j)Continue reading on Medium » The Adelic Langlands Program: vol. 33100.0, Symbolic-Branch Collatz Program XXXI Author: Jackie H. Blankenship (j) COMPENDIUM OF MATHEMATICAL RESULTS: FOS1 RESEARCH LEDGER (EI–HO)
Before discussing a token's technical implementation, try explaining its intended purpose without technical shorthand. What is the record for? What does it describe? What should a person understand from holding or viewing it? A short, ordinary-language explanation can reveal gaps…
Text classification maps a piece of text to labels: ticket → Billing / Bug / Feature, email → Spam / Not spam, review → Positive / Negative, document → Contract / Invoice / Memo, message → Urgent / Normal. Variants: The concepts — training vs. inference, precision/recall, overfitting, class imbalance — are in…
Rotary Positional Embedding (RoPE) is one of the most important positional-encoding techniques in modern Transformers, especially because it is used in models such as LLaMA-family models and many other LLM architectures. The key idea is: Instead of adding a positional…
Encoding is the process of converting categorical data into a numerical format so that Machine Learning algorithms can understand and process it. can be converted into: However, we should not use the same encoding technique for every categorical variable. The…
A new survey in Artificial Intelligence Review provides the first comprehensive map of language modeling for tabular data, tracing the evolution from from-scratch Transformers to BERT adaptations and today's large language models, and exposing an evaluation gap at the heart…