✅ Pros: Fast, transparent, zero training data.
❌ Cons: Misses implicit/contextual definitions, sensitive to style/punctuation.

🌲 Tier 2: Syntax & Dependency Parsing (More Robust)

Use an NLP parser to find equative or explanatory grammatical relationships.

Key spaCy dependency relations:

• appos: apposition (The kama, a farming knife, ...)
• attr/acomp: copular definitions (A chronometer is a precise timepiece.)
• acl/relcl: relative clauses defining a noun
• conj: coordinated explanatory phrases

spaCy example:

import spacy
nlp = spacy.load("en_core_web_sm")

def extract_syntax_definitions(text):
    doc = nlp(text)
    defs = []
    for sent in doc.sents:
        for token in sent:
            # Copular definition: X is a Y / X is Y
            if token.pos_ == "NOUN" and token.dep_ == "attr" and token.head.lemma_ == "be":
                term = list(token.head.lefts)
                if term:
                    defs.append({"term": term[0].text, "definition": token.text + " " + " ".join(c.text for c in token.rights)})
            # Apposition: Term, appos,
            if token.dep_ == "appos":
                head_term = token.head
                if head_term.pos_ == "NOUN":
                    defs.append({"term": head_term.text, "definition": token.text + " " + " ".join(c.text for c in token.children if c.head == token)})
    return defs

✅ Pros: Captures grammatical structure, less brittle than regex.
❌ Cons: Still misses semantic nuance, requires parsing overhead.

🤖 Tier 3: Machine Learning / LLM-Based Extraction (Highest Recall)

For literary text where definitions are implicit or stylistically varied, transformer models or LLMs perform best.

Option A: LLM Prompting (Zero/Few-Shot)

Extract any terms being defined or explained on the following page. 
Return a JSON list of {"term": "...", "definition": "..."} pairs. 
Only include explicit or clear contextual definitions. Ignore metaphors, examples, or general descriptions.
Text: {page_text}

Use structured output (JSON mode) and temperature ~0.2 for consistency.

Option B: Fine-Tuned Relation Extraction

• Frame as term-definition relation extraction.
• Use datasets like SemEval-2016 Task 13 (definition extraction), ACL Anthology glosses, or create a small literary annotation set.
• Fine-tune a model like deberta-v3-base or roberta-large with token/span labeling or pair classification.
• Tools: transformers, spacy-train, prodigy (for annotation).

✅ Pros: Handles implicit/contextual definitions, adapts to literary style.
❌ Cons: Requires compute, prompt engineering, or labeled data; LLMs may hallucinate.

📊 3. Practical Pipeline Recommendation

1. 1Start simple: Run regex + spaCy patterns. Evaluate precision on a few pages.
2. 2Filter false positives: Literary text uses metaphors (He was a lion in battle). Add POS filters (require NOUN/PROPN as term) and semantic similarity checks if needed.
3. 3Add LLM fallback: Pass low-confidence or unmatched sentences to an LLM with strict JSON output.
4. 4Human-in-the-loop: Randomly sample 10-20% of extractions for manual validation. Track precision/recall.
5. 5Iterate: Expand cue lists, adjust dependency filters, or fine-tune if volume justifies it.

⚠️ Key Caveats for Novels

• Low density: Most pages won't contain definitions. Optimize for precision over recall.
• Style dependence: 19th-century prose uses more appositives; modern fiction uses more dialogue glosses.
• Metaphor vs definition: She was a storm ≠ definition. Use POS, semantic plausibility, or LLM disambiguation.
• Cross-sentence definitions: Sometimes the term appears in sentence A, definition in B. Requires coreference resolution or sentence-pair modeling.

📦 Tools & Libraries to Use

• spacy + en_core_web_trf (for high-accuracy parsing)
• regex or re (pattern matching)
• openai, anthropic, or open-weight models via vllm/ollama (LLM extraction)
• prodigy or doccano (if annotating custom data)
• scikit-learn / transformers (if training a classifier/relation extractor)