Domain-Specific Writing Algorithm

A Domain-Specific Writing Algorithm is a specialized 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.
• Context:
  • 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.
  • ...
• Example(s):
  • Arria NLG, which provides natural language generation solutions tailored to various industries.
  • MedSLT, which offers domain-specific language translation in the medical field.
  • Link Grammar, which serves as a syntactic parser adaptable to domain-specific applications.
  • ...
• 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: Domain-Specific Language, Natural Language Generation, Explanation-Based Learning.

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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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