Update Basic_Terminologies.py

Basic_Terminologies.py

@@ -47,8 +47,8 @@ st.markdown(
 st.markdown(
     """
     <h5>Before diving deep into the concepts of NLP we must know about the frequently used terminologies in NLP</h5>
-    <h5 style="color: ##20B2AA;">1.Key Terminologies in NLP</h5>
-    <ul style="color: #d4e6f1; line-height: 1.8;">
+    <h5 style="color: ##00FF00;">1.Key Terminologies in NLP</h5>
+    <ul style="color: #008000; line-height: 1.8;">
         <li><b>Corpus:</b> A collection of text documents. Example: {d1, d2, d3, ...}</li>
         <li><b>Document:</b> A single unit of text (e.g., a sentence, paragraph, or article).</li>
         <li><b>Paragraph:</b> A collection of sentences.</li>
@@ -61,8 +61,8 @@ st.markdown(
 )
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     """
-    <h5 style="color: #20B2AA;">2.Tokenization</h5>
-    <p style="color: #d4e6f1;">Tokenization is the process of splitting text into smaller units, called tokens.</p>
+    <h5 style="color: #00FFFF;">2.Tokenization</h5>
+    <p style="color: #FFA500;">Tokenization is the process of breaking down a large piece of text into smaller units called tokens. These tokens can be words, sentences, or subwords, depending on the granularity required for the task.</p>
     <h6>Types of Tokenization:</h6>
     <ul style="color: #d4e6f1; line-height: 1.8;">
         <li><b>Sentence Tokenization:</b> Splitting text into sentences. <br> Example: "I love ice-cream. I love chocolate." → ["I love  ice-cream", "I love chocolate"]</li>
@@ -74,8 +74,8 @@ st.markdown(
 )
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     """
-    <h5 style="color: #20B2AA;">3.Stop Words</h5>
-    <p style="color: #d4e6f1;">Stop words are commonly used words in a language that are ignored during text processing as they contribute little to the overall meaning.</p>
+    <h5 style="color: #008080;">3.Stop Words</h5>
+    <p style="color: #000080;">Stop words are commonly used words in a language that carry little or no meaningful information for text analysis. </p>
     <h6>Example:</h6>
     <p style="color: #d4e6f1;">"In Hyderabad, we can eat famous biryani." <br> Stop words: ["in", "we", "can"]</p>
     """,
@@ -84,7 +84,7 @@ st.markdown(
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     """
     <h5 style="color: #20B2AA;">4.Vectorization</h5>
-    <p style="color: #d4e6f1;">Vectorization converts text data into numerical format for machine learning models. It enables text processing and analysis.</p>
+    <p style="color: #d4e6f1;">Vectorization is the process of converting text data into numerical representations so that machine learning models can process and analyze it.</p>
     <h6>Types of Vectorization:</h6>
     <ul style="color: #d4e6f1; line-height: 1.8;">
         <li><b>One-Hot Encoding:</b> Represents each word as a binary vector.</li>
@@ -100,7 +100,7 @@ st.markdown(
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     """
     <h5 style="color: #20B2AA;">5. Stemming</h5>
-    <p style="color: #d4e6f1;">Stemming reduces words to their base or root form by chopping off prefixes or suffixes. It is a rule-based heuristic process and can produce words that may not be valid in the language.</p>
+    <p style="color: #d4e6f1;">Stemming is the process of reducing words to their base or root form, often by removing prefixes or suffixes. It is a rule-based, heuristic approach to standardize words by removing derivational affixes.</p>
     <h6>Example:</h6>
     <ul style="color: #d4e6f1; line-height: 1.8;">
         <li><b>Original Words:</b> "running", "runner", "runs"</li>
@@ -112,7 +112,7 @@ st.markdown(
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     """
     <h5 style="color: #20B2AA;">6. Lemmatization</h5>
-    <p style="color: #d4e6f1;">Lemmatization reduces words to their dictionary or base form, called a lemma, while considering the context of the word in a sentence.</p>
+    <p style="color: #d4e6f1;">Lemmatization is the process of reducing a word to its base or root form (called a lemma) using linguistic rules and a vocabulary (dictionary). Unlike stemming, lemmatization ensures that the resulting word is a valid word in the language.</p>
     <h6>Example:</h6>
     <ul style="color: #d4e6f1; line-height: 1.8;">
         <li><b>Original Words:</b> "studying", "better", "carrying"</li>