📝 Divya Kadav — Language & NLP Mini Projects

This repository is a collection of my mini projects in Marathi Morphology and Natural Language Processing (NLP).
Each project demonstrates a focused concept: verb conjugation, pluralization, or text analysis. These are mini-project assignments I have done in my university.

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📂 Project List

1️⃣ Marathi Verb Pluralisation (Demo)

• Goal: Create a toy Python function that attaches suffixes to verb roots based on gender (masculine/feminine/neuter) and number (singular/plural).
• Skills: Morphological rules, dictionary mapping, Python functions.
• Example: marathi_verb_pluralisation("kha", "masculine", "singular") → "khaāt"

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2️⃣ Marathi Verb Tense Conjugator (Demo)

• Goal: Build a Python function to generate verb forms for present, past, future, and imperative tenses.
• Skills: Morphological suffixation, handling multiple tense categories, toy linguistic modeling.
• Example:
  • Present tense (Emma): {"first_singular": "khato", "second_singular": "khatos", "third_plural": "khatat"}
  • Past tense (Khel): {"first_singular": "khello", "third_plural": "khelle"}

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3️⃣ NLP Text Analysis with NLTK Gutenberg Corpus

• Goal: Use the NLTK library to analyze classic texts (Austen, Shakespeare, Bible).
• Tasks:
  • Load corpus texts
  • Find concordances (keyword in context)
  • Compute text statistics:
    • Average word length
    • Average sentence length
    • Vocabulary diversity
• Skills: Corpus linguistics, NLP preprocessing, Python text analysis.
• Example Output:

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4️⃣ Marathi Pluralization for Nouns and Adjectives

• Goal: Demonstrate how Marathi nouns and adjectives change in plural form.
• Focus: Feminine pluralization patterns such as -i, -a, and -ya, along with agreement in pronouns and verbs.
• Skills: Morphological agreement, applied linguistics, grammatical rules.
• Example:
• Feminine singular → mulgi (girl)
• Feminine plural → mulya (girls)

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NLP Project: Text Analysis with the Gutenberg Corpus

This project uses the NLTK (Natural Language Toolkit) library to analyze texts from the Gutenberg corpus.
It demonstrates how to load data, explore concordances, and compute basic statistics such as average word length, sentence length, and vocabulary diversity.
This is an introductory NLP project for working with real literature data.

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🔹 Step 1: Install and Import NLTK

import nltk

# Download required datasets
nltk.download('gutenberg')
nltk.download('punkt')

from nltk.corpus import gutenberg

List available texts

print(gutenberg.fileids())

['austen-emma.txt', 'austen-persuasion.txt', 'austen-sense.txt', 'bible-kjv.txt', 'shakespeare-hamlet.txt', 'shakespeare-macbeth.txt', 'shakespeare-caesar.txt']

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Marathi Verb Tense Conjugator (Demo)

This repository contains a simple Python function that generates verb forms for different tenses by attaching suffixes to a given verb root.
It is a demo model to illustrate basic tense patterns in Marathi, not a complete or linguistically accurate conjugator.

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🔹 How It Works

The function marathi_verb_tense(verb, tense) takes:

• verb: a verb root in Latin script (for simplicity, e.g., "kha", "bol", "zop")
• tense: one of "present", "past", "future", "imperative"

It then looks up suffixes for person + number (first, second, third; singular, plural) in a dictionary.
For imperatives, only second person singular/plural are supported.

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🔹 Rule Mapping

Present

• First Singular → to
• Second Singular → tos
• Third Singular → to
• First Plural → to
• Second Plural → ta
• Third Plural → tat

Past

• First Singular → lo
• Second Singular → las
• Third Singular → la
• First Plural → lo
• Second Plural → lat
• Third Plural → le

Future

• First Singular → n
• Second Singular → shil
• Third Singular → l
• First Plural → lu
• Second Plural → ɻal
• Third Plural → til

Imperative

• Second Singular → e
• Second Plural → o

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🔹 Code

def marathi_verb_tense(verb, tense):
    # Define the verb conjugation rules for different tenses
    verb_conjugation = {
        'present': {
            'first_singular': verb + 'to',
            'second_singular': verb + 'tos',
            'third_singular': verb + 'to',
            'first_plural': verb + 'to',
            'second_plural': verb + 'ta',
            'third_plural': verb + 'tat'
        },
        'past': {
            'first_singular': verb + 'lo',
            'second_singular': verb + 'las',
            'third_singular': verb + 'la',
            'first_plural': verb + 'lo',
            'second_plural': verb + 'lat',
            'third_plural': verb + 'le'
        },
        'future': {
            'first_singular': verb + 'n',
            'second_singular': verb + 'shil',
            'third_singular': verb + 'l',
            'first_plural': verb + 'lu',
            'second_plural': verb + 'ɻal',
            'third_plural': verb + 'til'
        },
        'imperative': {
            'second_singular': verb + 'e',
            'second_plural': verb + 'o'
        }
    }

    # Get the verb form based on the tense
    if tense in verb_conjugation:
        verb_form = verb_conjugation[tense]
    elif tense == 'third_singular':
        verb_form = verb_conjugation['past']['third_singular']
    elif tense == 'first_plural':
        verb_form = verb_conjugation['present']['first_plural']
    elif tense == 'second_plural':
        verb_form = verb_conjugation['present']['second_plural']
    elif tense == 'third_plural':
        verb_form = verb_conjugation['present']['third_plural']
    else:
        verb_form = verb_conjugation['past']['third_plural']

    return verb_form

Marathi Verb Pluralisation

This repository contains a simple Python function that generates verb forms by attaching suffixes to a given verb root based on gender and number.
It is a demo model to demonstrate basic morphological rules in Marathi, not a linguistically complete conjugator.

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🔹 How It Works

The function marathi_verb_pluralisation(verb, gender, number) takes:

• verb: a verb root in Latin script (technically you can take Marathi Script for this, but for my convenience I have used Latin Script as we commonly use Hinglish/Minglish on Whatsapp and I wanted to try with this first) (e.g., "kha", "ja", "bol")
• gender: "masculine", "feminine", or "neuter"
• number: "singular" or "plural"

It then looks up the suffix from a predefined dictionary and concatenates it to the root.

Rule Mapping

• Masculine + Singular → āt
• Masculine + Plural → at
• Feminine + Singular → ti
• Feminine + Plural → ti
• Neuter + Singular → t
• Neuter + Plural → at

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🔹 Code

def marathi_verb_pluralisation(verb, gender, number):
    # Define the verb conjugation rules
    verb_conjugation = {
        ('masculine', 'singular'): verb + 'āt',
        ('masculine', 'plural'): verb + 'at',
        ('feminine', 'singular'): verb + 'ti',
        ('feminine', 'plural'): verb + 'ti',
        ('neuter', 'singular'): verb + 't',
        ('neuter', 'plural'): verb + 'at'
    }

    # Get the verb form based on the subject's gender and number
    verb_form = verb_conjugation[(gender, number)]
    return verb_form

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What I coded

print(marathi_verb_pluralisation('kha', 'masculine', 'singular')) # khaāt print(marathi_verb_pluralisation('ja', 'masculine', 'plural')) # jaat print(marathi_verb_pluralisation('bol', 'feminine', 'singular')) # bolti print(marathi_verb_pluralisation('chal', 'feminine', 'plural')) # chalti print(marathi_verb_pluralisation('mar', 'neuter', 'singular')) # mart print(marathi_verb_pluralisation('radd', 'neuter', 'plural')) # raddat print(marathi_verb_pluralisation('zop', 'feminine', 'singular')) # zopti print(marathi_verb_pluralisation('bhet', 'feminine', 'plural')) # bhetti print(marathi_verb_pluralisation('pi', 'neuter', 'singular')) # pit print(marathi_verb_pluralisation('bagh', 'neuter', 'plural')) # baghat print(marathi_verb_pluralisation('sang', 'feminine', 'singular')) # sangti

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Output

khaāt jaat bolti chalti mart raddat zopti bhetti pit baghat sangti