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new 4b751ce 2025/02/02 08:15:42: Generated dev website from
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commit 4b751ce26d8c6ecda68bcbc02b2387f749f729bf
Author: jenkins <[email protected]>
AuthorDate: Sun Feb 2 08:15:42 2025 +0000
2025/02/02 08:15:42: Generated dev website from groovy-website@a544ea0
---
blog/groovy-text-similarity.html | 28 +++++++++++++++++++++-------
1 file changed, 21 insertions(+), 7 deletions(-)
diff --git a/blog/groovy-text-similarity.html b/blog/groovy-text-similarity.html
index 666fa18..91704c3 100644
--- a/blog/groovy-text-similarity.html
+++ b/blog/groovy-text-similarity.html
@@ -195,7 +195,7 @@ in more general ways.</p>
<div class="ulist">
<ul>
<li>
-<p><code>org.deeplearning4j:deeplearning4j-nlp</code> for Glove and ConceptNet
models</p>
+<p><code>org.deeplearning4j:deeplearning4j-nlp</code> for Glove, ConceptNet,
and FastText models</p>
</li>
<li>
<p><code>ai.djl</code> with Pytorch for a universal-sentence-encoder model and
Tensorflow with an Angle model</p>
@@ -208,10 +208,12 @@ in more general ways.</p>
<h2 id="_simple_string_metrics">Simple String Metrics</h2>
<div class="sectionbody">
<div class="paragraph">
-<p>String metrics provide some sort of measure of the sameness of the
characters in words (or phrases). These algorithms generally compute similarity
or distance (inverse similarity).</p>
+<p>String metrics provide some sort of measure of the sameness of the
characters in words (or phrases).
+These algorithms generally compute similarity or distance (inverse
similarity).</p>
</div>
<div class="paragraph">
-<p>There are numerous tutorials that describe various string metric
algorithms. We won’t replicate those tutorials but here is a summary of
some common ones:</p>
+<p>There are numerous tutorials that describe various string metric algorithms.
+We won’t replicate those tutorials but here is a summary of some common
ones:</p>
</div>
<table class="tableblock frame-all grid-all stretch">
<colgroup>
@@ -263,12 +265,24 @@ JaroWinkler of <code>ground</code> and
<code>rgound</code> (first two letters sw
</tbody>
</table>
<div class="paragraph">
-<p>You may be wondering what practical use these algorithms might have.
-Longest commons subsequence is the algorithm behind the popular
<code>diff</code> tool.</p>
+<p>You may be wondering what practical use these algorithms might have. Here
is just a few use cases:</p>
+</div>
+<div class="ulist">
+<ul>
+<li>
+<p>Longest commons subsequence is the algorithm behind the popular
<code>diff</code> tool</p>
+</li>
+<li>
+<p>Hamming distance is an important metric when designing algorithms for error
detection, error correction and checksums</p>
+</li>
+<li>
+<p>Levenshtein is used in search engines (like Apache Lucene and Apache Solr)
+for fuzzy matching searches and for spelling correction software</p>
+</li>
+</ul>
</div>
<div class="paragraph">
-<p>Groovy has in fact a built-in example of a variant of the Levenshtein
measure
-it uses for error reporting. Groovy uses a variant known as the
Damerau-Levenshtein distance.
+<p>Groovy has in fact a built-in example of using the Damerau-Levenshtein
distance metric.
This variant counts transposing two adjacent characters within the original
word as one "edit".
The Levenshtein distance of <code>fish</code> and ifsh` is 2.
The Damerau-Levenshtein distance of <code>fish</code> and ifsh` is 1.</p>
