Natural Language Processing (NLP)

see wordnet
a topic map maintained at princeton
https://wordnet.princeton.edu/
(note. three pages on wordnet have been copied into the wordnet project and should be deleted from here.)

Data Science Instructor at Metis, Chicago, Illinois
MS Northwestern University, Evanston, Ill

two hour tutorial
https://www.youtube.com/watch?v=xvqsFTUsOmc

three products

• sentiment analysis
• topic modeling
• text generation

python package for Web Scraping

• Requests, make HTTP requests
• Beautiful Soup, parse HTML documents
• Pickle, serialize python objects for later use
• Pandas, data analysis, DataFrame = table

Text data formats

1. corpus, prep as Pandas DataFrame, two-column table: author, transcript
2. Document-term matrix

procedure

• clean: remove punctuation, lowercase, remove numbers
• tokenize: words
• remove stop words (articles)
• matricize: columns=words, rows=documents, cells=word counts

two output formats:

• corpus, original text
• document-term matrix

sentiment analysis 1:08:56

• input: corpus
• nltk: natural language toolkit
• python libraries: TextBlob, built on top of nltk

sentiment

• from textblob import TextBlob
• TextBlob(“I love Naiyana”).sentiment
• output: Sentiment(polarity=0.5, subjectivity=0.6)
• polarity: -1 to +1
• subjectivity: 0 to +1, higher score means opinionated
• TextBlob uses a sentiment lexicon labeled by Tom De Smedt

word-net, compiled at Princeton, columns for each word:

• word form: great
• wordnet id: a-01123879
• POS: JJ
• Sense: very good
• Polarity: 0.8
• Subjectivity: 1.0

example: movie review database

topic modeling 1:22:52

• input: document-term matrix
• python libraries:
• nltk, for pos tagging
• gensim, built by Radim Rehurek specifically for topic modeling

Latent Dirichlet Allocation (DLA)

• latent = hidden
• Dirichlet = a type of probability distribution

goal: learn the topic mix in each document, and the word mix in each topic

• input: document-term matrix, number of topics, number of iterations
• output: the top words in each topic

other techniques, also available in gensim:

• Latent Semantic Indexing (LSI)
• Non-Negative Matrix Factorization (NMF)

id2word = dict¹⁾

Part of speech tag set
https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html

text generation 1:44:50
input: corpus, include punctuation

Markov Chains, the current word predicts the next word
LSTM, the current string of words predicts the next word

https://www.youtube.com/watch?v=bDxFvr1gpSU

History

Feed forward networks
a vanilla neural network like a multilayer perceptron with fully connected layers. A feed forward network treats all input features as unique and independent of one another, discrete.

Convolutional networks
An image processing adjacent pixels are related, and similar patterns repeated in the image are related. Proximity matters.

Recurrent networks
Process a string of words. Predict the end of the sentence given the beginning of the sentence.

LSTM networks, A variant of RNN.

Attention networks

Can match a pronoun to its noun antecedent.

Transformer
Encoder

https://m.youtube.com/watch?v=4Z_TzZJ-v3o

• BERT Google, Bi-directional encoder representation from transformers
• GPT2 OpenAI, for story-telling

Any time you're trying to do something with text.

• Classify it.
• Make use of it
• Translate
• Sentence completion
• Auto complete
• Story telling

F1 Score. A number from zero to 1. 1 is better. A way to evaluate classification problems.

Transformers have become the basic building block of most state-of-the-art architectures in NLP, replacing gated recurrent neural network models such as the long short-term memory (LSTM)

Both BERT and GPT-2 are based on transformers.

corpus, corpora

• text
• concordance
• common_contexts
• dispersion_plot
• generate
• set
• len
• sorted

Sam, coach, conversation, info capture, python server, SQL, Ajax

Python

• Read config.ini
• psycopg2
• Calc level (to be used in rapgen)
• Load grammar table. Not just grammar but infonetgrab
• Interrogation

Recognize Thai handwriting
This could be an academic project that would result in a dictionary and corpus. Which academic institutions are working on NLP for Thai?

Teach vocabulary, grammar, many subject domains simultaneously with principles of repitition, reinforcement, building gradually on previously mastered material.

Let the teacher learn even while teaching.
Let the teacher teach like a parent: continuously, while going about your day.

Databit

• Questions to illicit this databit
• Answers valid values

Network of databits

• Which question to ask next?

Chat server

Ajax server

Webserver

Ajax Chat client

GitHub frug Ajax chat
Uses Ruby socket server
Client Uses js-flash bridge or fall-back to Ajax polling

Polling every 1sec
Degrade to 5sec after disuse

Requires Ajax server

A2hosting
Run webserver
Run python scripts from Ajax post
Access postgresql from python post

Gensen
Gencon
Thirst for knowledge about person
Ask about friends
Compare answers from multiple persons
Database structure for personal data

Dialog A. Gather info
B. Drill student

Empathy, know the other's vocabulary, use it, help him expand it

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Thai Corpus - use to calculate level

Thai wordnet,
English wordnet: Princeton

Chulalongkorn U. Bangkok
http://www.arts.chula.ac.th/ling/tnc/works/

TNC Online, broken php
http://www.arts.chula.ac.th/~ling/TNCII/corp.php

Research paper about TNC:
Aroonmanakun, Wirote & Tansiri, Kachen & Nittayanuparp, Pairit. (2009). Thai National Corpus. 153-158. 10.3115/1690299.1690321.
https://www.researchgate.net/publication/271429101_Thai_National_Corpus

Khun Wannaphong, Khon Kaen U., Using Thani, 2017-2020
http://thainlp.wannaphong.com/

Dictionary
https://lexitron.nectec.or.th/2009_1/

Wordlist
https://www.expatden.com/thai/thai-frequency-lists-with-english-definitions/