IDENTIFYING PROBLEMS AND SOLUTIONS IN THE TEXT
https://doi.org/10.5281/zenodo.20377267
Kalit so‘zlar
problem–solution structure, discourse analysis, reading comprehension, linguistic markers, natural language processingAnnotasiya
This research examines how readers and automated systems identify problem–solution structures in texts—a common but often complex pattern where difficulties and their remedies may be implied, rearranged, or interwoven. Recognizing this structure is essential for reading comprehension, education, professional communication, and artificial intelligence, yet it remains underexplored, especially when texts lack explicit transitional phrases. The study investigates three key questions: which linguistic markers (e.g., conjunctions, evaluative adjectives, modal verbs) signal problems and solutions, how textual features like paragraph boundaries and headings influence identification, and what challenges arise in narratives or arguments without clear organizational cues. The goal is to develop a practical framework for reading instruction, technical writing, and natural language processing. Ultimately, the research argues that identifying problems and solutions is a dynamic interpretive process shaped by both textual cues and the reader's prior knowledge.
Foydalanilgan adabiyotlar ro‘yhati
Bohaty, J. J. (2015). The effects of expository text structure instruction on the reading outcomes of 4th and 5th graders experiencing reading difficulties [Doctoral dissertation, University of Nebraska-Lincoln]. UNL Digital Commons.
Relevance: This dissertation provides empirical evidence on teaching problem–solution text structure to struggling readers, directly supporting claims about the practical implications of structure recognition in education.
Graesser, A. C., McNamara, D. S., & Louwerse, M. M. (2003). What do readers need to learn in order to process coherence relations in narrative and expository text. In A. P. Sweet & C. E. Snow (Eds.), Rethinking reading comprehension (pp. 82–98). Guilford Press.
Relevance: This foundational chapter explains how readers process coherence relations—including problem–solution patterns—and identifies the cognitive skills required for comprehension, supporting the theoretical framework of your research.
Heine, L. (2010). Problem solving in a foreign language. De Gruyter Mouton.
Relevance: This book examines problem-solving processes through linguistic markers and think-aloud protocols, offering methodological insights for analyzing how readers identify problems and solutions in text.
Hoey, M. (2001). Textual interaction: An introduction to written discourse analysis. Routledge.
Relevance: Hoey is a seminal figure in problem–solution pattern research. This book provides the foundational framework for identifying problem–solution structures in written discourse, including detailed analytical techniques.
McNamara, D. S., Cai, Z., & Louwerse, M. M. (2007). Optimizing LSA measures of cohesion. In T. K. Landauer, D. S. McNamara, S. Dennis, & W. Kintsch (Eds.), Handbook of latent semantic analysis (pp. 379–400). Lawrence Erlbaum Associates.
Relevance: This chapter discusses automated cohesion measurement in texts, directly relevant to your research's focus on how automated systems can identify structural patterns like problem–solution.
Shih, Y.-C. (2009). The demonstration of discourse analysis on reading teaching: The problem-solution pattern. Journal of National Taipei College of Business, (15), 137–152.
Relevance: This article provides a practical demonstration of applying problem–solution pattern analysis to reading instruction, with clear examples of linguistic signals and classroom implications.
