Detecting definitions in a page of literary text

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When scanning the library of babel, what is the most important things, in case of images it could be scematics and engineering plans, cads. In text it could be definitions, a word(s) that preceeds a explanation of what the word means. Perhaps these are the most important things. True or false and feasibility could then be determined or a prcessable list. {html} <div style="font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, sans-serif; line-height: 1.6; color: #333; max-width: 900px; margin: 0 auto; padding: 20px;"> <h2 style="color: #2c3e50; border-bottom: 3px solid #3498db; padding-bottom: 10px; margin-top: 30px;">🔍 1. Clarify What Counts as a "Definition" in Fiction</h2> <p style="margin: 15px 0;">In novels, definitions typically appear as:</p> <ul style="padding-left: 25px; margin: 15px 0;"> <li style="margin: 8px 0;"><strong style="color: #2980b9;">Explicit cue phrases</strong>: <code style="background: #f4f4f4; padding: 2px 6px; border-radius: 3px; font-family: monospace;">X means Y</code>, <code style="background: #f4f4f4; padding: 2px 6px; border-radius: 3px; font-family: monospace;">X is defined as Y</code>, <code style="background: #f4f4f4; padding: 2px 6px; border-radius: 3px; font-family: monospace;">known as X</code>, <code style="background: #f4f4f4; padding: 2px 6px; border-radius: 3px; font-family: monospace;">called X</code></li> <li style="margin: 8px 0;"><strong style="color: #2980b9;">Appositives</strong>: <em style="color: #555;">The obsidian, a volcanic glass, glittered...</em></li> <li style="margin: 8px 0;"><strong style="color: #2980b9;">Colon/parenthetical glosses</strong>: <code style="background: #f4f4f4; padding: 2px 6px; border-radius: 3px; font-family: monospace;">Grok: to understand deeply...</code> or <em style="color: #555;">He carried a kama (a short farming knife)...</em></li> <li style="margin: 8px 0;"><strong style="color: #2980b9;">Relative/explanatory clauses</strong>: <em style="color: #555;">A quidditch seeker, who flies fastest to catch the Snitch, ...</em></li> <li style="margin: 8px 0;"><strong style="color: #2980b9;">Dialogue explanations</strong>: <em style="color: #555;">"You mean a chronometer?" "Yes. A pocket watch, but precise."</em></li> </ul> <h2 style="color: #2c3e50; border-bottom: 3px solid #3498db; padding-bottom: 10px; margin-top: 30px;">🛠️ 2. Tiered Detection Strategies</h2> <h3 style="color: #27ae60; margin-top: 25px;">✅ Tier 1: Lexical & Pattern Matching (Fast, Interpretable)</h3> <p style="margin: 15px 0;">Use regular expressions or string matching for definitional cue phrases and punctuation structures.</p> <p style="margin: 15px 0;"><strong style="color: #2980b9;">Common cue phrases</strong>:</p> <pre style="background: #2c3e50; color: #ecf0f1; padding: 15px; border-radius: 6px; overflow-x: auto; font-family: 'Courier New', monospace; font-size: 14px; margin: 15px 0;"><code>CUES = [ r"\bmeans\b", r"\bis defined as\b", r"\bis known as\b", r"\bis called\b", r"\brefers to\b", r"\bin other words\b", r"\bthat is\b", r"\bi\.e\.", r"\bnamely\b", r"\bdescribed as\b", r"\bcan be defined as\b" ]</code></pre> <p style="margin: 15px 0;"><strong style="color: #2980b9;">Structural patterns</strong>:</p> <ul style="padding-left: 25px; margin: 15px 0;"> <li style="margin: 5px 0;"><code style="background: #f4f4f4; padding: 2px 6px; border-radius: 3px; font-family: monospace;">Term : explanation</code></li> <li style="margin: 5px 0;"><code style="background: #f4f4f4; padding: 2px 6px; border-radius: 3px; font-family: monospace;">Term (explanation)</code></li> <li style="margin: 5px 0;"><code style="background: #f4f4f4; padding: 2px 6px; border-radius: 3px; font-family: monospace;">Term, a/an [noun phrase], ...</code> (appositive)</li> <li style="margin: 5px 0;"><code style="background: #f4f4f4; padding: 2px 6px; border-radius: 3px; font-family: monospace;">Term, which/that [clause explaining it], ...</code></li> </ul> <p style="margin: 15px 0;"><strong style="color: #2980b9;">Quick Python example</strong>:</p> <pre style="background: #2c3e50; color: #ecf0f1; padding: 15px; border-radius: 6px; overflow-x: auto; font-family: 'Courier New', monospace; font-size: 13px; margin: 15px 0;"><code>import re def extract_pattern_definitions(text): patterns = [ r'(?P<term&gt;\b[A-Z][a-z]+(?:\s+[a-z]+){0,3})\s*[:：]\s*(?P<def&gt;[^.\n]{5,100})', # Term: definition r'(?P<term&gt;\b[A-Z][a-z]+(?:\s+[a-z]+){0,3})\s*\((?P<def&gt;[^)]{5,80})\)', # Term (def) r'(?P<term&gt;\b\w+(?:\s+\w+){0,2})\b,\s*a\s+(?P<def&gt;[^,]{3,60}),', # Term, a ..., ] results = [] for pat in patterns: for m in re.finditer(pat, text, re.IGNORECASE): results.append({"term": m.group("term").strip(), "definition": m.group("def").strip()}) return results</code></pre> <p style="margin: 10px 0; padding: 10px; background: #e8f4fd; border-left: 4px solid #3498db; border-radius: 0 4px 4px 0;"><em style="color: #2980b9;">✅ Pros</em>: Fast, transparent, zero training data. <br><em style="color: #c0392b;">❌ Cons</em>: Misses implicit/contextual definitions, sensitive to style/punctuation.</p> <h3 style="color: #27ae60; margin-top: 25px;">🌲 Tier 2: Syntax & Dependency Parsing (More Robust)</h3> <p style="margin: 15px 0;">Use an NLP parser to find equative or explanatory grammatical relationships.</p> <p style="margin: 15px 0;"><strong style="color: #2980b9;">Key spaCy dependency relations</strong>:</p> <ul style="padding-left: 25px; margin: 15px 0;"> <li style="margin: 5px 0;"><code style="background: #f4f4f4; padding: 2px 6px; border-radius: 3px; font-family: monospace;">appos</code>: apposition (<em>The kama, a farming knife, ...</em>)</li> <li style="margin: 5px 0;"><code style="background: #f4f4f4; padding: 2px 6px; border-radius: 3px; font-family: monospace;">attr</code>/<code style="background: #f4f4f4; padding: 2px 6px; border-radius: 3px; font-family: monospace;">acomp</code>: copular definitions (<em>A chronometer is a precise timepiece.</em>)</li> <li style="margin: 5px 0;"><code style="background: #f4f4f4; padding: 2px 6px; border-radius: 3px; font-family: monospace;">acl</code>/<code style="background: #f4f4f4; padding: 2px 6px; border-radius: 3px; font-family: monospace;">relcl</code>: relative clauses defining a noun</li> <li style="margin: 5px 0;"><code style="background: #f4f4f4; padding: 2px 6px; border-radius: 3px; font-family: monospace;">conj</code>: coordinated explanatory phrases</li> </ul> <p style="margin: 15px 0;"><strong style="color: #2980b9;">spaCy example</strong>:</p> <pre style="background: #2c3e50; color: #ecf0f1; padding: 15px; border-radius: 6px; overflow-x: auto; font-family: 'Courier New', monospace; font-size: 13px; margin: 15px 0;"><code>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</code></pre> <p style="margin: 10px 0; padding: 10px; background: #e8f4fd; border-left: 4px solid #3498db; border-radius: 0 4px 4px 0;"><em style="color: #2980b9;">✅ Pros</em>: Captures grammatical structure, less brittle than regex. <br><em style="color: #c0392b;">❌ Cons</em>: Still misses semantic nuance, requires parsing overhead.</p> <h3 style="color: #27ae60; margin-top: 25px;">🤖 Tier 3: Machine Learning / LLM-Based Extraction (Highest Recall)</h3> <p style="margin: 15px 0;">For literary text where definitions are implicit or stylistically varied, transformer models or LLMs perform best.</p> <p style="margin: 15px 0;"><strong style="color: #2980b9;">Option A: LLM Prompting (Zero/Few-Shot)</strong></p> <pre style="background: #f8f9fa; color: #2c3e50; padding: 15px; border-radius: 6px; border-left: 4px solid #9b59b6; font-family: 'Courier New', monospace; font-size: 13px; margin: 15px 0; white-space: pre-wrap;">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}</pre> <p style="margin: 10px 0; font-size: 14px; color: #555;">Use structured output (JSON mode) and temperature ~0.2 for consistency.</p> <p style="margin: 15px 0;"><strong style="color: #2980b9;">Option B: Fine-Tuned Relation Extraction</strong></p> <ul style="padding-left: 25px; margin: 15px 0;"> <li style="margin: 5px 0;">Frame as <strong>term-definition relation extraction</strong>.</li> <li style="margin: 5px 0;">Use datasets like <code style="background: #f4f4f4; padding: 2px 6px; border-radius: 3px; font-family: monospace;">SemEval-2016 Task 13</code> (definition extraction), <code style="background: #f4f4f4; padding: 2px 6px; border-radius: 3px; font-family: monospace;">ACL Anthology</code> glosses, or create a small literary annotation set.</li> <li style="margin: 5px 0;">Fine-tune a model like <code style="background: #f4f4f4; padding: 2px 6px; border-radius: 3px; font-family: monospace;">deberta-v3-base</code> or <code style="background: #f4f4f4; padding: 2px 6px; border-radius: 3px; font-family: monospace;">roberta-large</code> with token/span labeling or pair classification.</li> <li style="margin: 5px 0;">Tools: <code style="background: #f4f4f4; padding: 2px 6px; border-radius: 3px; font-family: monospace;">transformers</code>, <code style="background: #f4f4f4; padding: 2px 6px; border-radius: 3px; font-family: monospace;">spacy-train</code>, <code style="background: #f4f4f4; padding: 2px 6px; border-radius: 3px; font-family: monospace;">prodigy</code> (for annotation).</li> </ul> <p style="margin: 10px 0; padding: 10px; background: #e8f4fd; border-left: 4px solid #3498db; border-radius: 0 4px 4px 0;"><em style="color: #2980b9;">✅ Pros</em>: Handles implicit/contextual definitions, adapts to literary style. <br><em style="color: #c0392b;">❌ Cons</em>: Requires compute, prompt engineering, or labeled data; LLMs may hallucinate.</p> <h2 style="color: #2c3e50; border-bottom: 3px solid #3498db; padding-bottom: 10px; margin-top: 30px;">📊 3. Practical Pipeline Recommendation</h2> <ol style="padding-left: 25px; margin: 15px 0; counter-reset: step-counter;"> <li style="margin: 12px 0; position: relative; padding-left: 30px;"><span style="position: absolute; left: 0; top: 0; width: 24px; height: 24px; background: #3498db; color: white; border-radius: 50%; text-align: center; line-height: 24px; font-size: 13px; font-weight: bold;">1</span><strong>Start simple</strong>: Run regex + spaCy patterns. Evaluate precision on a few pages.</li> <li style="margin: 12px 0; position: relative; padding-left: 30px;"><span style="position: absolute; left: 0; top: 0; width: 24px; height: 24px; background: #3498db; color: white; border-radius: 50%; text-align: center; line-height: 24px; font-size: 13px; font-weight: bold;">2</span><strong>Filter false positives</strong>: Literary text uses metaphors (<em>He was a lion in battle</em>). Add POS filters (require <code style="background: #f4f4f4; padding: 2px 6px; border-radius: 3px; font-family: monospace;">NOUN</code>/<code style="background: #f4f4f4; padding: 2px 6px; border-radius: 3px; font-family: monospace;">PROPN</code> as term) and semantic similarity checks if needed.</li> <li style="margin: 12px 0; position: relative; padding-left: 30px;"><span style="position: absolute; left: 0; top: 0; width: 24px; height: 24px; background: #3498db; color: white; border-radius: 50%; text-align: center; line-height: 24px; font-size: 13px; font-weight: bold;">3</span><strong>Add LLM fallback</strong>: Pass low-confidence or unmatched sentences to an LLM with strict JSON output.</li> <li style="margin: 12px 0; position: relative; padding-left: 30px;"><span style="position: absolute; left: 0; top: 0; width: 24px; height: 24px; background: #3498db; color: white; border-radius: 50%; text-align: center; line-height: 24px; font-size: 13px; font-weight: bold;">4</span><strong>Human-in-the-loop</strong>: Randomly sample 10-20% of extractions for manual validation. Track precision/recall.</li> <li style="margin: 12px 0; position: relative; padding-left: 30px;"><span style="position: absolute; left: 0; top: 0; width: 24px; height: 24px; background: #3498db; color: white; border-radius: 50%; text-align: center; line-height: 24px; font-size: 13px; font-weight: bold;">5</span><strong>Iterate</strong>: Expand cue lists, adjust dependency filters, or fine-tune if volume justifies it.</li> </ol> <h2 style="color: #2c3e50; border-bottom: 3px solid #3498db; padding-bottom: 10px; margin-top: 30px;">⚠️ Key Caveats for Novels</h2> <ul style="padding-left: 25px; margin: 15px 0;"> <li style="margin: 8px 0;"><strong style="color: #c0392b;">Low density</strong>: Most pages won't contain definitions. Optimize for precision over recall.</li> <li style="margin: 8px 0;"><strong style="color: #c0392b;">Style dependence</strong>: 19th-century prose uses more appositives; modern fiction uses more dialogue glosses.</li> <li style="margin: 8px 0;"><strong style="color: #c0392b;">Metaphor vs definition</strong>: <em>She was a storm</em> ≠ definition. Use POS, semantic plausibility, or LLM disambiguation.</li> <li style="margin: 8px 0;"><strong style="color: #c0392b;">Cross-sentence definitions</strong>: Sometimes the term appears in sentence A, definition in B. Requires coreference resolution or sentence-pair modeling.</li> </ul> <h2 style="color: #2c3e50; border-bottom: 3px solid #3498db; padding-bottom: 10px; margin-top: 30px;">📦 Tools & Libraries to Use</h2> <ul style="padding-left: 25px; margin: 15px 0; display: grid; grid-template-columns: repeat(auto-fit, minmax(250px, 1fr)); gap: 10px;"> <li style="background: #f8f9fa; padding: 10px 15px; border-radius: 5px; border-left: 3px solid #27ae60;"><code style="font-family: monospace; color: #2980b9;">spacy</code> + <code style="font-family: monospace; color: #2980b9;">en_core_web_trf</code> (for high-accuracy parsing)</li> <li style="background: #f8f9fa; padding: 10px 15px; border-radius: 5px; border-left: 3px solid #27ae60;"><code style="font-family: monospace; color: #2980b9;">regex</code> or <code style="font-family: monospace; color: #2980b9;">re</code> (pattern matching)</li> <li style="background: #f8f9fa; padding: 10px 15px; border-radius: 5px; border-left: 3px solid #27ae60;"><code style="font-family: monospace; color: #2980b9;">openai</code>, <code style="font-family: monospace; color: #2980b9;">anthropic</code>, or open-weight models via <code style="font-family: monospace; color: #2980b9;">vllm</code>/<code style="font-family: monospace; color: #2980b9;">ollama</code> (LLM extraction)</li> <li style="background: #f8f9fa; padding: 10px 15px; border-radius: 5px; border-left: 3px solid #27ae60;"><code style="font-family: monospace; color: #2980b9;">prodigy</code> or <code style="font-family: monospace; color: #2980b9;">doccano</code> (if annotating custom data)</li> <li style="background: #f8f9fa; padding: 10px 15px; border-radius: 5px; border-left: 3px solid #27ae60;"><code style="font-family: monospace; color: #2980b9;">scikit-learn</code> / <code style="font-family: monospace; color: #2980b9;">transformers</code> (if training a classifier/relation extractor)</li> </ul> <div style="margin-top: 30px; padding: 15px; background: #e8f8f5; border-left: 4px solid #1abc9c; border-radius: 0 4px 4px 0; font-style: italic; color: #16a085;"> If you share a sample page, I can run a quick extraction demo using one of these methods and show exactly what gets caught vs missed. </div> </div> {/html}
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