Add project slides

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Oystein Kristoffer Tveit 2024-04-26 00:49:11 +02:00
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{
inputs.nixpkgs.url = "nixpkgs/nixos-22.11";
outputs = { self, nixpkgs }: let
system = "x86_64-linux";
pkgs = nixpkgs.legacyPackages.${system};
in {
apps.${system} = let
toApp = _: pkg: {
type = "app";
program = toString pkg;
};
in {
default = {
type = "app";
program = toString (pkgs.writeScript "reveal-md-tdt4310-project" ''
${pkgs.nodePackages.reveal-md}/bin/reveal-md main.md
'');
};
};
devShells.${system}.default = pkgs.mkShell {
packages = with pkgs; [
nodePackages.reveal-md
# inkscape
# gimp
# drawio
];
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}

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<link rel="stylesheet" href="./static/main.css"/>
### TDT 4310 - Intelligent Text Analysis Project
#### Sorting japanese sentences by linguistic complexity
----
### Overview
1. Introduction and motivation
1. Background
1. Datasets
1. Methodology
1. Evaluation
1. Conclusion, and further work
---
<img src="./static/graphics/jst1.png" width="18%"/>
<img src="./static/graphics/jst2.png" width="18%"/>
<img src="./static/graphics/jst3.png" width="18%"/>
<br/>
<br/>
<footer>Motivation</footer>
---
<div style="font-size: 0.8em">
| JMDict | Tatoeba / Tanaka corpus | NHK Easy News | MeCab |
|--------|-------------------------|---------------|-------|
| Open source dictionary | Multilingual sentence pairs | Easy-to-read news articles | POS and morphological analyzer |
| <img src="./static/graphics/jmdict.png" width=100%/> | <img src="./static/graphics/tatoeba.png" width=100%/> | <img src="./static/graphics/nhk.png" width=100%/> | |
</div>
<br/>
<br/>
<footer>Datasets</footer>
---
#### TF-IDF
Extract the most meaningful words of a document
<br/>
#### Sense disambiguation
Pinpoint which sense of the word is used, based on surrounding context and grammar.
<footer>Background</footer>
----
### Japanese
<div class="grid">
<div class="col-9">
#### Three writing systems
| <span style="color: red;">hiragana</span> | <span style="color: green;">katakana</span> | <span style="color: blue;">kanji</span> |
|----------|----------|-------|
| <img src="./static/graphics/hiragana.png"/> | <img src="./static/graphics/katakana.png"/> | <img src="./static/graphics/kanji2.png"/> |
</div>
<div class="col-3">
<div class="row-2">
<p>
<span style="color: green;">ページ</span>
<span style="color: red;"></span>
<span style="color: blue;">行目</span>
<span style="color: red;">をみなさい</span>
</p>
<p style="font-size: 0.8em;">
<span style="color: red;">Let's start from</span>
(the)
fifth
<span style="color: blue;">line</span>
<span style="color: red;">on</span>
<span style="color: green;">page</span>
10
</p>
##### Multiple readings per kanji
形 - katachi, kata, gyou, kei
</div>
<div class="row-1">
<br/>
##### Furigana
<ruby>
<rp>(</rp><rt>furi</rt><rp>)</rp>
<rp>(</rp><rt>ga</rt><rp>)</rp>
<rp>(</rp><rt>na</rt><rp>)</rp>
<ruby>
</div>
</div>
</div>
<footer>Background</footer>
---
#### Data ingestion, preprocessing and disambiguation
<br/>
##### Tanaka Corpus
<p>
信用█為る(する){して}█と█彼(かれ)[01]█は|1█言う{言った}
</p>
<br/>
##### NHK News Articles
Scrape -> Extract text -> MeCab + Furigana -> Try disambiguating with POS
<footer>Methodology</footer>
Note:
Disambiguation here, is not necissarily sense ambiguation, but rather disambiguating the dictionary entry.
Could exploit the english translation to disambiguate all the way down to the word senses.
----
#### TF-IDF?
<br/>
<div>
$ \text{TF-IDF} = \frac{\text{Amount of term in doc}}{\text{Amount of terms in doc}} \cdot log \frac{\text{Amount of docs}}{1 + \text{ Amount of docs containing term}} $
</div>
<br/>
<div class="fragment" data-fragment-index="0">
$ \text{TF-DF} = \frac{AVG(\text{Amount of term in doc})}{\text{Amount of terms in doc}} \cdot \frac{\text{ Amount of docs containing term}}{\text{Amount of docs}} $
</div>
<footer>Methodology</footer>
Note:
TF-IDF is usually used for finding out how meaningful a word is to a document. Here, we want to do the opposite. The value should have a higher score, if it is more common across several documents.
----
#### Word difficulty
| Commonness | Dialects | Kanji | Katakana | NHK rating |
|------------|----------|-------|----------|------------|
| 25% | 10 % | 25% | 15% | 25% |
| <img width="200px" src="./static/graphics/curves/common.png"> | <img width="200px" src="./static/graphics/curves/dialect.png"> | <img width="200px" src="./static/graphics/curves/kanji.png"> | <img width="200px" src="./static/graphics/curves/katakana.png"> | <img width="200px" src="./static/graphics/curves/nhk.png"> |
<footer>Methodology</footer>
----
#### Sentence difficulty
| Word difficulty sum | Hardest word | Sentence Length |
|------------|----------|-------|
| 50% | 20 % | 30% |
| <img width="200px" src="./static/graphics/curves/wordsum.png"> | | <img width="200px" src="./static/graphics/curves/sentence_length.png"> |
<footer>Methodology</footer>
---
<div class="columns">
<div>
<img width="80%" src="./static/graphics/examples/test1.png"/>
</div>
<div>
<img width="90%" src="./static/graphics/examples/test2.png"/>
</div>
</dic>
<footer>Evaluation</footer>
----
<div class="columns">
<div>
<img width="90%" src="./static/graphics/examples/book1.png"/>
</div>
<div>
<img width="100%" src="./static/graphics/examples/book2.png"/>
</div>
</dic>
<footer>Evaluation</footer>
----
<ul>
<div>
<li>Apart from some bugs, the system seems to be working as intended</li>
</div>
<div class="fragment" data-fragment-index="0">
<li>The factors should be more strongly grounded in linguistical research</li>
</div>
<div class="fragment" data-fragment-index="1">
<li>Alternatively a dataset that would make it possible to evaluate the accuracy of the implementation</li>
</div>
<div class="fragment" data-fragment-index="2">
<li>More data left unused.</li>
</div>
</ul>
<footer>Conclusion, and further work</footer>

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import math
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import os
# Assumes 0 <= x <= 1
def sigmoid(x, slope=0.1, offset=0, max_x=1, flip=False) -> float:
assert x <= max_x
x = x - (max_x / 2) - (offset * max_x / 2)
s = 1 / (1 + math.exp(-x / slope))
return max_x - s if flip else s
curve_dir = os.path.dirname(__file__)
for name, f in [
('common', lambda x: sigmoid(x, slope=0.05, offset=-0.6, flip=True)),
('dialect', lambda x: sigmoid(x, slope=0.08, offset=-0.2)),
('kanji', lambda x: x ** 5),
('katakana', lambda x: 0 if x > 0.5 else 1),
('nhk', lambda x: sigmoid(x, slope=0.03, offset=-0.6, flip=True)),
('wordsum', lambda x: x),
]:
plt.rc('font', size=33)
plt.xlim(-0.05, 1.05)
plt.ylim(-0.05, 1.05)
plt.locator_params(nbins=2)
space = np.linspace(0, 1, 1000)
p = [f(n) for n in space] #
plt.plot(space, p, linewidth=5)
plt.savefig(f"{curve_dir}/{name}.png")
plt.clf()
plt.rc('font', size=33)
plt.xlim(-0.05, 24.05)
plt.ylim(-0.05, 1.05)
plt.locator_params(nbins=3)
space = np.linspace(0, 24, 1000)
p = [sigmoid(n, slope=1.4, max_x=24) for n in space] #
plt.plot(space, p, linewidth=5)
plt.savefig(f"{curve_dir}/sentence_length.png")

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/* Network diagrams */
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display: inline;
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}
/* Misc */
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position: relative;
margin: auto;
}
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position:absolute;
top:0;
/* TODO: fix this properly */
left:25%;
}
/* footer { */
/* position: absolute; */
/* left: 0; */
/* bottom: 0; */
/* width: 100%; */
/* background-color: grey; */
/* color: white; */
/* text-align: center; */
/* } */
.reveal {
position: relative;
height: 100%;
}
.slides {
position: absolute;
top: 0;
left: 0;
right: 0;
bottom: 60px; /* leave space for the footer */
overflow: auto;
}
footer {
position: fixed;
bottom: 0;
left: 0;
right: 0;
height: 60px;
color: grey;
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overflow: auto;
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