week1-nlp-coursera-intro

Main approaches in Nlp

Here, this video goes through all three main approaches by example of one particular task–semantic slot filling.

Rule-based method

here, the blogger will tell you what context-free grammar is

Probabilistic modeling and machine learning

First, you need a corpus with some markup. We will go into details in this course.

Deep learning

Arrangement

1. week1 text classification tasks
   
   representing your text as a bag of words

2. week2-representing text as a sequence
   Now, the next week will be about representing text not as a bag of words but as a sequence. So, what can you do when you represent a text as a sequence of words?

• One task would be language modeling. So language models are about predicting the probabilities of the next words given some previous words. So, this can be used to do text generation. And this is useful in many applications. For example, if you do machine translation, you are given some sequence of words, some sentence on English and then you need to translate it, let’s say to Russian, so you need to generate some Russian text and that is where you’ll need language model.
• Now, another important task is called sequence tagging. So this is the task when you have a sequence of words and you need to predict text for each of the words in this sequence. For example, it could be part-of-speech texts so you need to know that some words are nouns, some words are verbs and so on. Another task would be to find named entities and this is really useful. For example, you can find some names of the cities and use them as features for your previous task for text classification. Now, another task which is called semantic slot filling has been just covered in our previous video.

3. week 3-try to understand the meaning of words or some pieces of text. How do we represent the meaning?