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			<h1>Association Measures</h1>
			<p class="lead">Association Measures are different ways to calculate possible attraction between two metrics. In case of Stable Lexical Marker Analysis the attraction of the item as a keyword for one corpus and specifically not the other. Two measures are offered as options, they will almost always yield the same result. Only in edgecases with very differently sized corpora is there a chance that a significant difference exists. Both methods use the following table for calculations. For each word that is selected from the frequency list of the selected corpus, these cels are calculated. </p>
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			<tr><th></th><th>Selected Corpus</th><th>Contrasted Corpus</th></tr>
			<tr><th>Frequency of word/lemma</th><td>cel1</td><td>cel2</td></tr>
			<tr><th>Frequency of all other words/lemmas</th><td>cel3</td><td>cel4</td></tr>
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		<h1>Positive likelihood ratio</h1>
		<p>The positive likelihood ratio is the log of the sensitivity devided by one minus the specificity. Where the sensivitivy is the frequency of a word in the corpus over the total size of the corpus, and the specificity is the total size of the other corpus minus the number of times the selected word is present in that corpus devided by the total of the other corpus.</p>
		<p>In terms of formula:</p>
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		<img src="images/sensitivity.png" style="width: 100%;">
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		<img src="images/specificity.png" style="width: 100%;">
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		<img src="images/ratio.png" style="width: 100%;">
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		<p>And when combined: </p><img src="images/total.png" style="width: 100%;">
	</div><commit_msg>correctly visualise 4 cel table in help page<commit_after><div class="page-header" id="banner">
	<div class="row">
		<div class="col-md-12">
			<h1>Association Measures</h1>
			<p class="lead">Association Measures are different ways to calculate possible attraction between two metrics. In case of Stable Lexical Marker Analysis the attraction of the item as a keyword for one corpus and specifically not the other. Two measures are offered as options, they will almost always yield the same result. Only in edgecases with very differently sized corpora is there a chance that a significant difference exists. Both methods use the following table for calculations. For each word that is selected from the frequency list of the selected corpus, these cels are calculated. </p>
		</div>
	</div>
</div>
<div class="row">
	<div class="col-md-6 col-md-offset-3">
		<table class="table">
			<tr><th></th><th>Frequency of word/lemma</th><th>Frequency of other words/lemmas</th></tr>
			<tr><th>Selected Corpus</th><td>cel1</td><td>cel2</td></tr>
			<tr><th>Contrasted Corpus</th><td>cel3</td><td>cel4</td></tr>
		</table>
	</div>
</div>
<div class="row">
	<div class="col-md-12">
		<h1>Positive likelihood ratio</h1>
		<p>The positive likelihood ratio is the log of the sensitivity devided by one minus the specificity. Where the sensivitivy is the frequency of a word in the corpus over the total size of the corpus, and the specificity is the total size of the other corpus minus the number of times the selected word is present in that corpus devided by the total of the other corpus.</p>
		<p>In terms of formula:</p>
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	<div class="col-md-4">
		<img src="images/sensitivity.png" style="width: 100%;">
	</div>
	<div class="col-md-4">
		<img src="images/specificity.png" style="width: 100%;">
	</div>
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		<img src="images/ratio.png" style="width: 100%;">
	</div>
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		<p>And when combined: </p><img src="images/total.png" style="width: 100%;">
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{'fraction_non_alphanumeric': 0.14449597266292408, 'fraction_numerical': 0.015377105198926043, 'mean_word_length': 4.575510204081633, 'pattern_counts': {'":': 0, '<': 137, '<?xml version=': 0, '>': 137, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '3050425', 'n_tokens_mistral': 1403, 'n_tokens_neox': 1293, 'n_words': 493}