Domain-Specific Writing Algorithm

An Domain-Specific Writing Algorithm is a domain-specific natural language generation algorithm that can be used to create domain-specific written content (that supports domain-specific communication).

• AKA: Domain-Specific NLG Algorithm, Specialized Content Generation Algorithm, Field-Specific Text Generation Algorithm.
• Context:
  • It can be applied by Automated Domain-Specific Writing System to solve a Automated Domain-Specific Writing Task.
  • It can generate technical documentation for software applications.
  • It can produce financial reports for investment firms.
  • It can create legal documents for law practices.
  • It can assist in drafting medical records for healthcare providers.
  • It can range from being a template-based algorithm to being an AI-driven algorithm, depending on the complexity of its text generation capabilities.
  • It can leverage domain-specific corpora to ensure terminology accuracy and contextual relevance (e.g., legal precedent databases, medical journal articles).
  • It can enforce domain compliance (e.g., HIPAA in healthcare, GAAP in finance) during text generation or editing.
  • It can integrate with domain ontologies to resolve ambiguities.
  • It can adapt outputs to audience expertise level, such as simplifying technical jargon for novice users.
  • It can optimize for domain-specific quality metrics (e.g., legal clause completeness, clinical guideline adherence).
  • ...
• Example(s):
  • Arria NLG, which provides natural language generation solutions tailored to various industries.
  • Link Grammar, which serves as a syntactic parser adaptable to domain-specific applications.
  • Legal Writing Algorithms, such as:
    • LegalDoc-Gen, drafting contract clauses with jurisdiction-specific regulation checks.
    • PrecedentAnalyzer, linking legal arguments to relevant case law citations.
  • Medical Writing Algorithms, such as:
    • MedSLT, which offers domain-specific language translation in the medical field.
    • ClinNote-Algo, generating SOAP notes with automated ICD-11 code insertion.
    • DrugInteractionChecker, flagging contraindications in prescription instructions.
  • Technical Writing Algorithms, such as:
    • APIDoc-Optimizer, auto-generating software documentation from code annotations.
    • EngReport-Validator, ensuring compliance with ISO/IEC 23894 AI documentation standards.
  • ...
• Counter-Example(s):
  • General-Purpose Writing Algorithms, which lack domain-specific customization.
  • Standard Text Generators, which serve different functions.
  • Generic Natural Language Processing Algorithms, which use different approaches.
  • ...
• See: Natural Language Generation, Explanation-Based Learning, Domain-Specific Language Model, Knowledge Graph Integration, Automated Compliance Checking, Clinical NLP, Legal Document Automation, Technical Communication Workflow.

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References

2025

• (Sharma et al., 2025) ⇒ Sharma, Mishra, et al. (2025). "Incremental learning algorithm for dynamic evolution of domain-specific vocabularies". In: Nature Scientific Reports.
  • QUOTE: Domain-specific vocabulary, which is crucial in fields such as Information Retrieval and Natural Language Processing, requires continuous updates to remain effective. Incremental Learning, unlike conventional methods, updates existing knowledge without retraining from scratch. This paper presents an incremental learning algorithm for updating domain-specific vocabularies. It introduces DocLib, an archive used to capture a compact footprint of previously seen data and vocabulary terms.

    Task-based evaluation measures the effectiveness of the updated vocabulary by using vocabulary terms to perform a downstream task of text classification. The classification accuracy gauges the effectiveness of the vocabulary in discerning unseen documents related to the domain. Experiments illustrate that multiple incremental updates maintain vocabulary relevance without compromising its effectiveness. The proposed algorithm ensures bounded memory and processing requirements, distinguishing it from conventional approaches.

Novel algorithms are introduced to assess the stability and plasticity of the proposed approach, demonstrating its ability to assimilate new knowledge while retaining old insights.

2020

• (Rajput et al., 2020) ⇒ Saransh Rajput, Akshat Gahoi, Manvith Reddy, & Dipti Mishra Sharma. (2020). "N-Grams TextRank A Novel Domain Keyword Extraction Technique". In: Proceedings of the 17th International Conference on Natural Language Processing (ICON): TermTraction 2020 Shared Task.
  • QUOTE: In this paper, we present an advanced domain specific keyword extraction algorithm in order to tackle this problem of paramount importance. Our algorithm is based on a modified version of TextRank algorithm - an algorithm based on PageRank to successfully determine the keywords from a domain specific document. Furthermore, this paper proposes a modification to the traditional TextRank algorithm that takes into account bigrams and trigrams and returns results with an extremely high precision.

2013

• (Tetreault et al., 2013) ⇒ Joel Tetreault, Daniel Blanchard, Aoife Cahill, & Martin Chodorow. (2013). "Learning Domain-Specific, L1-Specific Measures of Word Readability". In: TAL.
  • QUOTE: In this work, since we have extensive writing by the L1 populations in the target domains, we compute our gold-standard scores using a log-odds-ratio, which is, in general we assume domain-specific native writing is always available). This enables each readability prediction to be made relative to the domain-specific frequency of the word. We also have one set of features (L1s-ACL) that directly encodes properties of the domain of interest.

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