Instead of one-hot encoding vector, we use featured representation to capture relationship between words.

In simple words, what are extracted features for each word? For example, in the following matrix "Man" and "Woman" have high score for "Gender" feature and low score for "Food" feature. As a result, the computer can capture their relation.



Featured representation: Word Embedding

Each word is represented as a vector, with value 1 for the word and 0 for all other words in the dictionary (similar to one-hot encoding)

University of Michigan - Ann Arbor

Google

https://www.coursera.org/learn/nlp-sequence-models

Sequence Models

Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
https://www.deeplearning.ai/machine-learning-yearning/

Note: The content of the book is aligned with the Coursera Deeplearning.ai specialization.  https://www.deeplearning.ai/deep-learning-specialization/ 

Machine Learning Yearning (Deeplearning.ai)

- Represent input words are x<1>


- Output to match is y<1>


- Tx denotes length of X.


- Ty denotes length of y.


Sequence Models: Notation

Representing  Words

The task of predicting what word/letter comes next, unlike the FNN and CNN, in sequence modeling the current output is dependent on the previous a=input and the length of the input is not fixed.


Why sequence models? 

For natural language data composed of discrete tokens like words, sequence models are specifically called language models. They provide the capacity to evaluate the likelihood of sentences, sample new text sequences, and optimize for the most likely outputs. By applying the chain rule of probability, language modeling can be reduced to an autoregressive prediction problem, decomposing the joint density of a sequence $$P(x_1, \ldots, x_T)$$ into the product of conditional densities in a left-to-right fashion: $$P(x_1, \ldots, x_T) = P(x_1) \prod_{t=2}^T P(x_t \mid x_{t-1}, \ldots, x_1)$$. For discrete signals, the autoregressive model acts as a probabilistic classifier, outputting a full probability distribution over the vocabulary for the next word given the leftwards context.

Language Model

Sequence labeling is a machine learning methodology used across many Natural Language Processing (NLP) applications. The core idea is to assign a specific class or tag to each token within a given input sequence. The resulting sequence of labels can then be interpreted to extract linguistic annotations. Common examples of this technique include Part-of-Speech (POS) tagging, where each word is labeled with its grammatical role, and Named Entity Recognition (NER), where tokens are tagged to identify entities like names or locations.

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