Tag Archives: NLP

A Researcher’s Roadmap: A Practical Framework for Rigorous Science

After many years spent in research, the scientific process—from idea to publication—becomes second nature. However, this intuition, though invaluable, deserves to be structured. The desire to describe this workflow stems not only from a need to better understand my own work but also from the desire to create a map that can help others navigate this complex terrain.

One inspiration was a humorous but accurate list from the book “We Have No Idea: A Guide to the Unknown Universe” by Jorge Cham and Daniel Whiteson:

  1. Organize what you know
  2. Look for patterns
  3. Ask questions
  4. Buy a tweed jacket with elbow patches

However, scientific work is, above all, the art of asking the right questions. It’s not about “beating the baseline” but about understanding a phenomenon. The question “why?” is a researcher’s compass. In turn, understanding often means the ability to reconstruct a mechanism (e.g., by implementing code or a formal proof), although in some areas of mathematics, a complete, verifiable line of reasoning is sufficient.

I have noticed that whether I am writing an empirical paper in Natural Language Processing (NLP) or a systematic review with a meta-analysis, a common skeleton lies beneath the surface. The result of these observations is the working framework below, which attempts to visualize this skeleton.

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Unveiling Dual Quality in Product Reviews: An NLP-Based Approach

Abstract

Consumers often face inconsistent product quality, particularly when identical products vary between markets, a situation known as the dual quality problem. To identify and address this issue, automated techniques are needed. This paper explores how natural language processing (NLP) can aid in detecting such discrepancies and presents the full process of developing a solution. First, we describe in detail the creation of a new Polish-language dataset with 1,957 reviews, 540 highlighting dual quality issues. We then discuss experiments with various approaches like SetFit with sentence-transformers, transformer-based encoders, and LLMs, including error analysis and robustness verification. Additionally, we evaluate multilingual transfer using a subset of opinions in English, French, and German. The paper concludes with insights on deployment and practical applications.