orange3-nlp 0.0.8

A collection of Orange3 widgets to perform natural language processing

orange3-nlp

This provides a collection of widgets for Natural Language Processing.

Installation

Within the Add-ons installer, click on "Add more..." and type in orange3-nlp

Widgets

• General Widgets
  • Abstractive Summary
  • Extractive Summary
  • Named Entity Recognition
  • POS Tagger
  • POS Viewer
  • Question Answering
  • Reference Library
  • Ollama RAG

• Text Splitting Widgets
  • Text Chunker
  • Tokens to Corpus

• Text Embedding Models
  • Doc2Vec
  • E5
  • FastText
  • Gemini
  • Nomic
  • OpenAI
  • Sentence Embedder (SBERT)
  • spaCy
  • USE

• Training of Text Embedding Widget
  • Train Doc2Vec

• For Polish Sentiment Analysis
  • Analiza Sentymentu

Summary Widgets

• Extractive Summary: Selects and joins key sentences or phrases from the original text.

• Abstractive Summary: Generates new sentences that paraphrase and condense the original content (more similar to how humans summarize).

Named Entity Recognition

Named Entity Recognition (NER) is a task in NLP that locates and classifies named entities in text into predefined categories such as:

• PERSON – names of people
• ORG – organizations
• GPE – countries, cities, or locations
• DATE, TIME, MONEY, etc.

Part of Speech Tagging

Part-of-speech (POS) tagging assigns grammatical categories to each word in a sentence.

Common POS Tags

Tag	Meaning	Example
NN	Noun	cat, city
VB	Verb	run, is
JJ	Adjective	fast, red
RB	Adverb	quickly
DT	Determiner	the, an
IN	Preposition	on, with

POS tagging is essential for syntactic parsing and downstream NLP tasks.

Part of Speech Viewer

This uses spaCy's displacy HTML renderer to provide a parsed dependency tree of the parts of speech of the input text.

Question Answering

Question Answering (QA) systems aim to extract or generate answers to user questions from a text or knowledge base.

Text Splitting Widgets

Tokens to Corpus

The Tokens to Corpus widget takes the tokens from the Preprocess Text widgets.

Text Chunker

Text Chunker supports 2 chunking strategies to split text. The first is LangChain's RecursiveCharacterTextSplitter and the second is semantic-text-splitter.

Reference Augmented Generation

Reference Augmented Generation (RAG) is a method of enhancing large language model (LLM) responses by providing external documents as supporting context. Instead of relying solely on the model's training data, RAG:

• Retrieves relevant snippets from a document collection (knowledge base).
• Augments the prompt to the LLM by including this retrieved content.
• Generates a more accurate and grounded answer based on the context.

Let's take a look at the Reference Library

And lastly, let's look at the Ollama RAG use.

Polish Sentiment Analysis

Since Polish sentiment analysis support in Orange was limited, Analiza Sentymentu provides a tuned model.