Natural Language Processing

NLP is a subfield of artificial intelligence and computational linguistics that focuses on the interaction between computers and human language. NLP involves the development of algorithms and techniques to enable computers to understand, interpret, and generate human language in a way that is both meaningful and useful.

This competency area includes an understanding of the concepts of language modeling, topic modeling, sequence labeling, machine translation, named entity disambiguation, advanced text generation, RNNs, and word embeddings.

Key Competencies:

1. Language Modeling - Building statistical or neural models to predict the likelihood of text sequences
2. Topic Modeling - Ability to discover topics or themes in a collection of text documents
3. Sequence Labeling - Knowledge of assigning labels to each element in a sequence of data, such as named entity recognition or part-of-speech tagging
4. Machine Translation - Ability to translate text from one language to another using statistical or neural models
5. Named Entity Disambiguation - Ability to resolve ambiguously named entities in text, such as determining which person a pronoun refers to
6. Advanced Text Generation - Ability to generate complex and coherent texts, such as stories or essays
7. Recurrent Neural Networks (RNNs) - Understanding the architecture of RNNs and how to build and train them using deep learning libraries such as TensorFlow, Keras, or PyTorch. Understanding the basic concepts for LSTM and GRU cells, sequence-to-sequence models, and language models.
8. Word Embeddings - Knowledge of representing words as vectors in a high-dimensional space using Python libraries such as Word2Vec, GloVe, or FastText. Understanding the API concepts for training and using word embedding models.