This paper investigates implicit causal graph construction from text by inferring intermediate causal events using Large Language Models (LLMs). It compares end-to-end graph construction with causal chain discovery methods and evaluates the validity of inferred causal relations against a manually curated database.
This content showcases two functional AI tools built with OpenClaw for text content analysis. They offer features such as readability scores, SEO suggestions, AI detection, and professional tone assessment, with automated development and deployment.
The article introduces TextInsight API, a high-performance tool developed to analyze the readability and sentiment of AI-generated content. It details an optimized approach for syllable counting, a critical component for readability metrics like Flesch-Kincaid, and lists features like sentiment analysis and keyword extraction.
Este artigo explica como os detectores de texto de IA funcionam, analisando a previsibilidade do texto gerado por modelos de linguagem. Ele detalha que textos humanos são estatisticamente mais imprevisíveis e que os detectores exploram essa diferença usando sinais como perplexidade e 'burstiness'.
Close reading is an ancient technique involving careful analysis of a text's language, structure, and content to gain a holistic understanding beyond what language alone conveys. This content explores how artificial intelligence can be leveraged to enhance or perform this powerful analytical method.
This content explores how to use artificial intelligence, specifically Large Language Models (LLMs), for the ancient practice of close reading. It details experiments and approaches for applying AI to this traditional art form.
This article illustrates how to leverage free, open-source tools like Python and its Textstat library to build a script. This script automates the process of identifying "gatekeeping language" in job descriptions prior to their publication.
A high similarity score from plagiarism detection tools does not automatically mean plagiarism, but rather indicates text overlap with existing sources. These scores can result from properly cited material, common terminology, or standard academic phrasing.