Scientibots - Autonomous Science

The Scientific Method in NLP: A Synthesis of My Research By Marie Seshat Landry This document synthesizes my research on the integration of the scientific method into Natural Language Processing (NLP). It explores the core themes, key observations, and potential applications of this approach, drawing from various documents and resources in my Google Drive. 1. Introduction Natural Language Processing (NLP) has traditionally relied on heuristic methods and rapid development of algorithms. However, my research proposes a paradigm shift by formally integrating the scientific method into NLP research and practice. This structured approach aims to enhance rigor, replicability, and address challenges in traditional NLP. 2. Core Themes CWM and the Scientific Method: Complex Word Mathematics (CWM) requires a scientifically grounded approach to model development, emphasizing mathematical rigor, linguistic validity, and interdisciplinary collaboration. Scientific Method as a Chain-of-Thought...

The Scientific Method in Complex Word Mathematics for NLP AI Machine Learning Chain-of-Thought Instructions Understanding the Prompt: Problem Definition: Identify the task: What specific problem or question is the NLP AI model trying to solve? Objective: What is the desired outcome or goal? What metrics will be used to evaluate success? Constraints: Are there any limitations or restrictions on the solution, such as computational resources, time, or data availability? Data Collection and Preparation: Data Sources: Where will the necessary data come from? Consider factors like quality, quantity, and relevance. Data Cleaning: Is there any preprocessing or cleaning required, such as removing noise, handling missing values, or normalizing data? Data Representation: How will the data be represented for the model? This might involve feature engineering, vectorization, or other techniques. Developing the Model: Hypothesis Formulation: Model Selec...