Revogada a IN 77/2015

Autor parece ser conceituado no tema.

Summary

1. 📚 Second Edition Overview: This is the second, revised and expanded edition of the book. The first edition was titled "Como Fazer Anotações Inteligentes. Uma Técnica Simples para Impulsionar a Escrita, Aprendizado e Pensamento - para Estudantes, Acadêmicos e Escritores de Livros de Não Ficção".

2. 🖋️ Key Focus: The book emphasizes the importance of organizing ideas and notes for effective writing. It's a guide for students, academics, and knowledge professionals to enhance their writing, learning, and long-term knowledge retention.

3. 🧠 Smart Notes Methodology: It introduces the concept of Smart Notes, based on psychological insights and the proven Zettelkasten note-taking technique, offering a comprehensive guide in English for the first time.

4. 🎯 Target Audience: The book is particularly useful for students and academics in social sciences and humanities, non-fiction writers, and anyone engaged in reading, thinking, and writing.

5. ⏳ Time Efficiency: Focuses on saving time spent searching for notes, quotes, or references, allowing more time for thinking, understanding, and developing new ideas in writing.

6. 👤 Author's Background: Written by Dr. Sönke Ahrens, a writer and researcher in education and social sciences, known for the award-winning book "Experimento e Exploração: Formas de Revelação do Mundo" (Springer).

7. 🌍 Global Reach: Since its initial release, "Como Fazer Anotações Inteligentes" has sold over 10,000 copies and has been translated into seven languages.

Alice quer que o gato diga para onde ela deve ir, mas o gato coloca essa decisão nas mãos da própria Alice ao responder "isso depende muito de onde você quer chegar". Não há ninguém ali para decidir por ela aonde ela deve ir. Somente ela é responsável por isso.

Flesch–Kincaid readability tests: These are tests that measure how easy or hard a text is to understand in English. There are two types of tests: Reading Ease and Grade Level.

Reading Ease: This test gives a score from 0 to 100, with higher scores meaning easier texts. The score depends on the number of words per sentence and syllables per word. Reader’s Digest has a score of 65, while the Harvard Law Review has a score of 30.

Grade Level: This test gives a score that matches a U.S. grade level, with higher scores meaning harder texts. The score depends on the number of words per sentence and syllables per word, but with different weights. Green Eggs and Ham by Dr. Seuss has a score of -1.3, while Swann’s Way by Marcel Proust has a score of -515.1.

Uses and limitations: These tests are used by the U.S. Department of Defense, some U.S. states, and some word processing programs. They are useful for education and legal purposes, but they have weaknesses compared to testing with real readers. They do not account for reader differences, content effects, layout effects, or retrieval aids.

Resumo

Os testes de legibilidade Flesch-Kincaid são projetados para indicar o quão difícil é entender um texto em inglês, com dois testes: o Flesch Reading-Ease e o Flesch-Kincaid Grade Level. Eles usam medidas semelhantes, como o comprimento das palavras e das sentenças, mas têm fatores de ponderação diferentes.

Fatos

• Existem dois testes de legibilidade: Flesch Reading-Ease e Flesch-Kincaid Grade Level.
• Ambos os testes usam medidas como o comprimento das palavras e das sentenças.
• Os resultados dos testes são inversamente correlacionados: uma pontuação alta no Reading Ease indica uma pontuação baixa no Grade-Level test.
• Rudolf Flesch criou o teste Reading Ease e posteriormente desenvolveu o teste Grade Level com J. Peter Kincaid para a Marinha dos EUA.
• O teste Flesch-Kincaid começou a ser usado pelo Exército dos EUA em 1978 e depois se tornou um padrão militar.
• Muitos estados dos EUA exigem que documentos legais, como apólices de seguro, tenham uma leitura de nível de nona série ou inferior.
• O teste Flesch Reading-Ease classifica a facilidade de leitura em diferentes níveis, desde muito fácil até extremamente difícil.
• O Departamento de Defesa dos EUA usa o teste Reading Ease como padrão para seus documentos.
• O teste Flesch-Kincaid Grade Level apresenta uma pontuação como um nível de série dos EUA, tornando mais fácil julgar o nível de legibilidade de vários textos.
• As fórmulas dos dois testes têm diferentes fatores de ponderação, tornando-os não diretamente comparáveis.
• As fórmulas dos testes enfatizam o comprimento das sentenças ou das palavras.
• Os testes têm limitações, pois não consideram as diferenças entre os leitores, o conteúdo, o layout e os auxílios à recuperação.

A new evidence extraction architecture called ATT-MRC improves the recognition of evidence entities in judgement documents by treating it as a question-answer problem, resulting in better performance than existing methods.

Transformer models like RoBERTa outperform traditional machine learning models in legal question answering tasks, achieving significant improvements in performance metrics such as F1-score and Mean Reciprocal Rank.

The paper proposes L-HetGRL, an unsupervised approach using a legal heterogeneous graph and incorporating legal domain-specific knowledge, to improve Legal Document Similarity Measurement (LDSM) with superior performance compared to other methods.

China's increasing digitization of legal documents has led to a focus on using information technology to extract valuable information efficiently. Legal Document Similarity Measurement (LDSM) plays a vital role in legal assistant systems by identifying similar legal documents. Early approaches relied on text content or statistical measures, but recent advances include neural network-based methods and pre-trained language models like BERT. However, these approaches require labeled data, which is expensive and challenging to obtain for legal documents. To address this, the authors propose an unsupervised approach called L-HetGRL, which utilizes a legal heterogeneous graph constructed from encyclopedia knowledge. L-HetGRL integrates heterogeneous content, document structure, and legal domain-specific knowledge. Extensive experiments show the superiority of L-HetGRL over unsupervised and even supervised methods, providing promising results for legal document analysis.