Advanced Ngram Model

Analyze, predict, and visualize language patterns with machine learning

Input Text

Text Statistics

Characters

0

Words

0

Sentences

0

Paragraphs

0

Configuration

to

Comma-separated list of words to exclude

Filter n-grams using regular expressions

N-gram Analysis Results

Visualization

N-gram Frequency Table

N-gram Size Frequency

Advanced Statistics

Lexical Diversity

Type-Token Ratio: -
Unique Unigrams: -
Unique Bigrams: -
Unique Trigrams: -

Co-occurrence Patterns

Most Associated Words: -
PMI Score: -
Strongest Collocation: -
Jaccard Similarity: -

Entropy & Predictability

Unigram Entropy: -
Bigram Entropy: -
Conditional Entropy: -
Perplexity: -

Text Prediction

Predictable 1.0 Random

Prediction Result

Generated text will appear here...

Text Generation

Focused 1.0 Creative

Generated Text

Generated text will appear here...

Word Prediction Network

Clustering Settings

Cluster Visualization

Configure and run clustering to view results

Cluster Analysis

Cluster Statistics

Run clustering to see statistics...

Representative Features

Run clustering to see representative features...

Cluster Contents

Run clustering to see cluster contents...

Training Configuration

Model Management

Training Progress

Configure and start training to see progress...

Model Performance

Validation Metrics

Train model to see metrics...

Model Information

Train model to see information...

Learning Curve

Model Test

Model output will appear here...

Quick Guide

  • Input & Configure

    Enter text, upload files, and set basic parameters for N-gram analysis.

  • Analysis & Visualization

    View N-gram frequencies, statistics, and interactive visualizations.

  • Prediction & Generation

    Generate text based on input patterns and explore word associations.

  • Clustering & Classification

    Group similar text segments and discover patterns in your data.

  • Model Training

    Create and save custom N-gram models for advanced text generation.

Glossary

N-gram
A contiguous sequence of n items from a text.
Unigram
Single word (n=1).
Bigram
Two consecutive words (n=2).
Trigram
Three consecutive words (n=3).
Markov Chain
A stochastic model describing a sequence of events where the probability of each depends only on the state of the previous event.
Perplexity
A measurement of how well a model predicts a sample; lower is better.
Entropy
A measure of the unpredictability or randomness in text.
Smoothing
Techniques to adjust probability estimates for unseen n-grams.

Using the Ngram Model Tool

Basic Usage

  1. Enter text in the input area or upload a text file
  2. Configure N-gram settings (size, case sensitivity, etc.)
  3. Click "Process Text" to analyze your input
  4. Navigate between tabs to view different analyses and capabilities
  5. Export results or save trained models for future use

Analysis Features

  • Frequency analysis of N-grams at different sizes
  • Multiple visualization types (bar charts, networks, etc.)
  • Text statistics and lexical diversity metrics
  • Co-occurrence patterns and associations
  • Entropy and predictability measurements

Advanced Capabilities

  • Text prediction based on input patterns
  • Text generation with adjustable parameters
  • Clustering of text segments to discover patterns
  • Custom model training with various algorithms
  • Model saving and loading for persistent use

Example Code

Implementing a simple N-gram model in JavaScript:

// Simple N-gram model implementation
function createNgramModel(text, n) {
  const words = text.split(/\s+/);
  const model = {};
  
  // Build the model
  for (let i = 0; i <= words.length - n; i++) {
    const gram = words.slice(i, i + n).join(' ');
    const nextWord = words[i + n];
    
    if (!model[gram]) {
      model[gram] = {};
    }
    
    if (nextWord) {
      model[gram][nextWord] = (model[gram][nextWord] || 0) + 1;
    }
  }
  
  // Convert frequencies to probabilities
  Object.keys(model).forEach(gram => {
    const total = Object.values(model[gram])
                        .reduce((sum, count) => sum + count, 0);
    
    Object.keys(model[gram]).forEach(word => {
      model[gram][word] = model[gram][word] / total;
    });
  });
  
  return model;
}

Example Code

Implementing a simple N-gram model in Python:

def create_ngram_model(text, n):
    words = text.split()
    model = {}
    
    # Build the model
    for i in range(len(words) - n):
        gram = ' '.join(words[i:i+n])
        next_word = words[i+n]
        
        if gram not in model:
            model[gram] = {}
        
        model[gram][next_word] = model[gram].get(next_word, 0) + 1
    
    # Convert frequencies to probabilities
    for gram in model:
        total = sum(model[gram].values())
        for word in model[gram]:
            model[gram][word] = model[gram][word] / total
    
    return model

Example Code

Implementing a simple N-gram model in Java:

import java.util.*;

public Map> createNgramModel(String text, int n) {
    String[] words = text.split("\\s+");
    Map> freqModel = new HashMap<>();
    
    // Build frequency model
    for (int i = 0; i <= words.length - n - 1; i++) {
        StringBuilder gramBuilder = new StringBuilder();
        for (int j = 0; j < n; j++) {
            if (j > 0) gramBuilder.append(" ");
            gramBuilder.append(words[i + j]);
        }
        String gram = gramBuilder.toString();
        String nextWord = words[i + n];
        
        if (!freqModel.containsKey(gram)) {
            freqModel.put(gram, new HashMap<>());
        }
        
        Map nextWords = freqModel.get(gram);
        nextWords.put(nextWord, nextWords.getOrDefault(nextWord, 0) + 1);
    }
    
    // Convert to probabilities
    Map> probModel = new HashMap<>();
    for (String gram : freqModel.keySet()) {
        Map frequencies = freqModel.get(gram);
        Map probabilities = new HashMap<>();
        
        int total = 0;
        for (int count : frequencies.values()) {
            total += count;
        }
        
        for (String word : frequencies.keySet()) {
            probabilities.put(word, (double) frequencies.get(word) / total);
        }
        
        probModel.put(gram, probabilities);
    }
    
    return probModel;
}

Common Use Cases

Text Analysis

Identify patterns, word frequencies, and linguistic structure in:

  • Literary works
  • News articles
  • Scientific papers
  • Social media content
  • Historical documents

Content Generation

Create text with similar patterns and style to:

  • Generate creative writing inspiration
  • Create automated content summaries
  • Produce content variations
  • Assist with copywriting
  • Generate template text

Linguistic Research

Study language patterns and structure:

  • Identify common collocations
  • Study language evolution
  • Compare writing styles
  • Analyze dialect differences
  • Study specialized vocabulary

Educational Applications

Support language learning and teaching:

  • Vocabulary and phrase learning
  • Writing style analysis
  • Grammar pattern recognition
  • Language structure visualization
  • Text complexity assessment