pages/terminology.py

import streamlit as st 

st.markdown(
    """
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    /* List Styling */
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    """,
    unsafe_allow_html=True
)

# Page Content
st.markdown(
    """
    <h1>Basic Terminology in NLP</h1>
    """,
    unsafe_allow_html=True
)

st.markdown(
    """
    <h5>Before diving deep into the concepts of NLP, we must know about the frequently used terminologies in NLP.</h5>
    <h5>1. Key Terminologies in NLP</h5>
    <ul>
        <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>
        <li><b>Sentence:</b> A collection of words forming a meaningful expression.</li>
        <li><b>Word:</b> A collection of characters.</li>
        <li><b>Character:</b> A basic unit like an alphabet, number, or special symbol.</li>
    </ul>
    """,
    unsafe_allow_html=True
)
st.markdown(
    """
    <h5>2. Tokenization</h5>
    <p>Tokenization is the process of splitting text into smaller units, called tokens.</p>
    <h6>Types of Tokenization:</h6>
    <ul>
        <li><b>Sentence Tokenization:</b> Splitting text into sentences. <br> Example: "I love biryani. I love pizza." → ["I love biryani", "I love pizza"]</li>
        <li><b>Word Tokenization:</b> Splitting sentences into words. <br> Example: "I love NLP" → ["I", "love", "NLP"]</li>
        <li><b>Character Tokenization:</b> Splitting words into characters. <br> Example: "Cat" → ["C", "a", "t"]</li>
    </ul>
    """,
    unsafe_allow_html=True
)
st.markdown(
    """
    <h5>3. Stop Words</h5>
    <p>Stop words are commonly used words in a language that are ignored during text processing as they contribute little to the overall meaning.</p>
    <h6>Example:</h6>
    <p>"In Hyderabad, we can eat famous biryani." <br> Stop words: ["in", "we", "can"]</p>
    """,
    unsafe_allow_html=True
)
st.markdown(
    """
    <h5>4. Vectorization</h5>
    <p>Vectorization converts text data into numerical format for machine learning models. It enables text processing and analysis.</p>
    <h6>Types of Vectorization:</h6>
    <ul>
        <li><b>One-Hot Encoding:</b> Represents each word as a binary vector.</li>
        <li><b>Bag of Words (BoW):</b> Represents text based on word frequencies.</li>
        <li><b>TF-IDF:</b> Adjusts word frequency by importance.</li>
        <li><b>Word2Vec:</b> Embeds words in a vector space using deep learning.</li>
        <li><b>GloVe:</b> Uses global co-occurrence statistics for embedding.</li>
        <li><b>FastText:</b> Similar to Word2Vec but includes subword information.</li>
    </ul>
    """,
    unsafe_allow_html=True
)
st.markdown(
    """
    <h5>5. Stemming</h5>
    <p>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>
    <h6>Example:</h6>
    <ul>
        <li><b>Original Words:</b> "running", "runner", "runs"</li>
        <li><b>Stemmed Form:</b> "run"</li>
    </ul>
    """,
    unsafe_allow_html=True
)
st.markdown(
    """
    <h5>6. Lemmatization</h5>
    <p>Lemmatization reduces words to their dictionary or base form, called a lemma, while considering the context of the word in a sentence.</p>
    <h6>Example:</h6>
    <ul>
        <li><b>Original Words:</b> "running", "better", "went"</li>
        <li><b>Lemmatized Form:</b> "run", "good", "go"</li>
    </ul>
    <p>Lemmatization is more accurate than stemming but computationally more intensive as it requires a language dictionary.</p>
    """,
    unsafe_allow_html=True
)