Applications
NLP tasks and applications.
Study Notes
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.
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