PRO TEXT EDITOR & EXPERIMENTAL LAB

Professional NLP Editor & Analyzer

Type, upload Text (.txt) or Word (.docx), run advanced NLP pipelines, and inspect results with beautiful charts and structured output.

5 free NLP analyses

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Interactive Editor & File Uploader

Write or paste your text, then choose an analysis action.

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Analysis Output Results

JSON tree output, engine details, and downloadable results.

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NLP Operations

Run an individual operation and review its output above.

Text Normalizer

VISUAL ANALYTICS

Comprehensive NLP Charts & Metrics

Every analysis updates the charts below so you can inspect patterns visually.

Top Word Frequency Distribution

Bar

Part-Of-Speech Breakdown

Doughnut

Stop Words vs Meaningful Words

Pie

Heuristic Text Profile Radar

Radar

Task Spotlight Chart

A task-specific chart will appear after each analysis.

Dynamic

USER DOCUMENTATION & GUIDE

NLP Tools Detailed Guide & Examples

Comprehensive explanation and practical examples of each natural language processing operation.

Researcher

Farhad Rahimi

Specializing in Natural Language Processing, low-resource languages, and speech recognition architectures.

What is natural language processing?

Natural Language Processing (NLP) is a branch of AI that enables computers to understand and interpret text and spoken words, similar to how humans do. In today’s digital landscape, organizations accumulate vast amounts of data from different sources, such as emails, text messages, social media posts, videos, and audio recordings. NLP allows organizations to process and make sense of this data automatically. With NLP, computers can analyze the intent and sentiment behind human communication. From customer service chatbots in retailing to interpreting and summarizing electronic health records in medicine, NLP plays an important role in enhancing user experiences and interactions across industries.

Text Preprocessing in NLP

Natural Language Processing (NLP) has seen tremendous growth and development, becoming an integral part of various applications, from chatbots to sentiment analysis. One of the foundational steps in NLP is text preprocessing, which involves cleaning and preparing raw text data for further analysis or model training. Proper text preprocessing can significantly impact the performance and accuracy of NLP models. Working in natural language processing (NLP) typically involves using computational techniques to analyze and understand human language. This can include tasks such as language understanding, language generation, and language interaction. It includes steps such as Text Input and Data Collection, Text Preprocessing, Text Representation, Feature Extraction, Model Selection and Training, Model Deployment and Inference, Evaluation and Optimization, Iteration and Improvement.

1. Tokenization

Splits the input text into individual words or tokens for downstream processing.

Example: “Hello world” → [“Hello”, “world”]

2. Stop Words Removal

Filters out common meaningless words (e.g., and, in, to) to retain semantic content.

Example: “book in room” → [“book”, “room”]

3. Stemming

Cuts off word suffixes heuristically to extract root stems.

Example: “running, runner” → “run”

4. Lemmatization

Uses a configured ONNX lemma model when available; otherwise applies a transparent rule-based approximation.

Example: “better, best” → “good”

5. POS Tagging

Uses an ONNX POS model when available; otherwise returns approximate rule-based grammatical labels.

Example: “running” → VERB (rule fallback)

6. Word Frequency

Counts exact occurrences of each word in the text.

Example: “apple banana apple” → {apple: 2, banana: 1}

7. NER Entities

Uses an ONNX NER model when available; otherwise applies a limited rule-based entity fallback.

Example: “Visit Paris” → [Paris: LOCATION]

8. Chunking

Builds candidate noun phrases from the current rule-based POS cues; this is an approximate phrase heuristic.

Example: “red car” → [NP: red car]

9. Chinking

Applies a rule-based chinking rule to candidate phrase structures.

Example: Removing adjectives from “red car”

10. Sentence Segmentation

Breaks a paragraph down into individual sentences.

Example: “Hi! How are you?” → 2 sentences

11. N-Grams

Generates continuous sequences of N items (Bigrams, Trigrams).

Example: “Machine learning model” → Bigrams

12. Sentiment Analysis

Uses an ONNX sentiment model when available; otherwise applies an approximate multilingual sentiment lexicon.

Example: “I love this tool!” → Positive

13. Keywords

Extracts the most representative keywords from input text.

Example: “AI model AI” → [AI, model] (ranked by frequency)

14. Language Detection

Estimates Kurdish, Persian, or English using script signals and language-specific stop words, with a confidence score.

Example: “سڵاو” → Kurdish (KU)

15. Readability

Calculates transparent text metrics and a language-agnostic heuristic complexity score; it is not a standardized readability grade.

Example: Estimated reading time in seconds

16. Normalization

Standardizes orthography (e.g. converting Arabic k/y to standard forms).

Example: “ك” → “ک”

17. Corpus Stats

Measures lexical richness (TTR ratio) and total vocabulary statistics.

Example: TTR = Unique words / Total words

18. Prompt Simulator

Builds a prompt template for downstream AI integration. No external LLM is called and no model-generated answer is fabricated.

Example: [Text → Prompt template + integration note]