Big

after today's lecture:
-- A few more NLP basic tasks with Apple Natural Language Framework
-- Advanced NLP: Sentiment analysis

Recap on last week

-- Introduction to NLP 🎃
-- Some basic NLP tasks solved with Apple NL framework
--- Language identification
--- Named Entity Recognition
--- Part-of-speech tagging

NLP:
from wikipedia:
-- "the application of computational techniques to the analysis and synthesis of natural language and speech.""
-- it is an interdisciplinary subfield

Example applications of NLP:
text-to-speech 🗣️
speech-to-text 👂
machine translation 🧠
image captioning 🧑‍🏫
text-to-image generation 🧑‍🎨
etc.

Extend to this week

Basic NLP tasks
--- Language identification
--- Named Entity Recognition
--- Part-of-speech tagging
--- Lemmatization (NEW❤️‍🔥)
--- Tokennization (NEW❤️‍🔥)
Higher-level NLP tasks
--- Sentiment analysis (NEW❤️‍🔥)

download and revise 🎃
---Language identification (line 1-57)
---Named Entity Recognition (line 59-101)
---Part-of-speech tagging (line 144-181)

Your turn: open a new xcode playground, import the framework:

import NaturalLanguage

import Foundation

import CoreML

Tokenization
--1. Read about Tokenization: what is it about? 🥷
--2. Copy relevant code from
line 103-142
--3. paste and run the example codes! 🕹️

Lemmatization
--1. Read about Lemmatization: what is it about? 🥷
--2. Copy relevant code from
line 183-212
--3. paste and run the example codes! 🕹️

It's a classification task
input: text (a sentence, a paragraph, etc.)
output: what are the output classes?

Sentiment analysis:
"the process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude towards a particular topic, product, etc. is positive, negative, or neutral."

It is a classification task with two or three categories: negative/positive/(neutral).

It is a classification task, which means it has almost the same principles as image classification 🥰
The only difference 😈 : different data modality, one takes text as input vs. takes image as input

Training:
-- 1. Download and unzip Data
-- 2. Read through this Apple Document
-- 3. Use this Playground for training
(don't forget to change the corresponding file path! )
(specify a cooler model name)

Deployment:
-- 1. Download and unzip app code
-- 2. Drop in your freshly trained model
-- 3. Run! (tested on MacOS)

ai artist of this week: Sofia Crespo
keywords:
biology-inspired AI; artificial biodiversity

today we talked about:

Basic NLP tasks solved with Apple NL framework
--- Lemmatization
--- Tokennization
Higher level NLP task - sentiment analysis
--- CreateML training done on playground!!!