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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 (CWM) The scientific method provides a rigorous framework for investigating phenomena, acquiring new knowledge, and refining existing theories. In the context of Complex Word Mathematics (CWM), the scientific method can be adapted to guide the development and application of CWM models across various domains. 1. Observation & Question: Observe linguistic phenomena: Identify patterns in language, such as the relationship between word frequency and reading comprehension, or the impact of morphological complexity on sentence processing. Formulate research questions: Based on these observations, formulate specific, testable questions. Examples: "Does increased morphological complexity significantly impact reading times for native English speakers?" "Can a CWM model accurately predict sentence comprehension difficulty based on syntactic complexity and word frequency?" 2. Hypothesis Formulation: Devel...

Complex Word Mathematics (CWM) is a relatively new and evolving field that seeks to quantify and represent the complexity of language using mathematical frameworks. It aims to bridge the gap between linguistics and mathematics, providing a more rigorous and quantitative approach to understanding the intricacies of human language. Key Concepts and Approaches: Quantifying Linguistic Complexity: CWM explores various metrics to quantify the complexity of words and sentences. These metrics can include: Frequency: How often a word appears in a given corpus. 1 Semantic breadth: The number of different meanings or senses a word has. Syntactic complexity: The number and types of grammatical relationships a word participates in. Morphological complexity: The number of morphemes (meaningful units) that compose a word. Mathematical Representations: CWM employs mathematical models and techniques to represent and analyze linguistic data. These may include: Vector space mod...

The Scientific Method in Complex Word Mathematics (CWM) The scientific method provides a systematic framework for investigating phenomena, acquiring new knowledge, and refining existing theories. In the context of Complex Word Mathematics (CWM), the scientific method can be adapted to guide the development and application of CWM models across various domains. 1. Observation: Identify a phenomenon or problem related to complex words or linguistic structures that warrants investigation. Gather relevant data, such as text corpora, linguistic resources, or experimental observations. Example: Observe the ambiguity of the word "run" in different contexts (e.g., "She runs every morning" vs. "A 10-episode run of a show"). 2. Question: Formulate a specific research question or hypothesis related to the observed phenomenon. The question should be clear, focused, and testable using CWM methodologies. Example: "Can CWM models accurately disambiguate...