NLP tasks and applications.

How words are arranged.

How words form meaning

Feedforward and recurrent models for NLP tasks.

Part Of Speech (POS) tags, approach to tackle the challenge of ambiguity in text. Tag sets, taggers, Hidden Markov Model are summarized.

Text classification is one of the most commonly and widely known NLP tasks. In this note, several text classification tasks, algorithms for different tasks and their evaluation are summarized.

Words, Sentences, Paragraphs, Documents, Corpus are all sparse. they are made up of characters and have uncountable variations. Let’s simplify the question: How can we learn from the meaning of a piece of text based on a given corpus? Language Models are devised to tackle this challenge. In this notes, N-gram Model, its smoothing, application and evaluation are summarized.

Preprocessing is the first step of almost all NLP tasks. What techniques are commonly used? Why is it important? Which preprocessing techniques should we use in a specific task? How it will affect the outcome?

An Introduction to Natural Language Processing, its histories, techniques and applications.