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Everything about Deep linguistic processing

Deep linguistic processing is a natural language processing framework which draws on theoretical and descriptive linguistics. It models language predominantly by way of theoretical syntactic/semantic theory. Deep linguistic processing approaches differ from "shallower" methods in that they yield more expressive and structural representations which directly capture long-distance dependencies and underlying predicate-argument structures. The knowledge-intensive approach of deep linguistic processing requires considerable computational power, and has in the past sometimes been judged as being intractable. However, research in the early 2000s had made considerable advancement in efficiency of deep processing. Today, efficiency is no longer a major problem for applications using deep linguistic processing. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Deep linguistic processingA 5-minute orientation
  2. Skim a recent paper: Causal influence of linguistic learning on perceptual and conceptual processing: A brain-constrained deep neural network study of proper names and category termsInfluential work since 2022
  3. Pick up a classic book: Flat but not shallowThe most-reprinted book on the subject
  4. Read the landmark paper: Flexible and fast: Linguistic shortcut affects both shallow and deep conceptual processingCited 42 times
  5. Go sideways: DELPH-INA closely related topic
  6. Go sideways: Head-driven phrase structure grammarA closely related topic

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🗂️ The subject at a glance

  1. Contrast to "shallow linguistic processing"
  2. Sub-communities

🏆 Landmark papers (most cited)

🆕 Influential since 2022

📚 Books worth reading

Ranked by number of editions on Open Library, a rough measure of how lasting a book is.

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