| name | file-classification |
| description | Classify academic papers and documents into subject categories using keyword-based text analysis. |
File Classification by Subject
Approach: Keyword Scoring
For classifying documents into known categories, a keyword scoring approach is effective:
- Define keyword sets for each category
- Extract text from each document
- Score text against each keyword set (count occurrences)
- Assign document to highest-scoring category
Keyword Sets for This Task
- LLM: language model, transformer, attention mechanism, GPT, BERT, token, prompt, fine-tuning, NLP, neural network, deep learning, text generation, embedding, LLM, large language, reinforcement learning from human feedback, RLHF, instruction tuning, pretraining, machine learning
- Trapped ion / Quantum computing: trapped ion, quantum computing, qubit, quantum gate, entanglement, quantum error, ion trap, quantum circuit, quantum algorithm, quantum processor, quantum information, Coulomb, motional mode, laser cooling, quantum simulation
- Black hole: black hole, event horizon, Hawking radiation, singularity, gravitational, spacetime, general relativity, accretion, Schwarzschild, Kerr, entropy, holographic, AdS/CFT, cosmological, dark energy, dark matter
- DNA: DNA, genome, gene expression, nucleotide, protein, sequencing, CRISPR, mutation, chromosome, transcription, RNA, epigenetic, genetic, molecular biology, bioinformatics, cell, amino acid
- Music history: music, composer, symphony, opera, baroque, classical period, jazz, rhythm, harmony, melody, instrument, musicology, sonata, concert, orchestra, musical
Implementation Pattern
def classify(text, keyword_sets):
text_lower = text.lower()
scores = {}
for category, keywords in keyword_sets.items():
scores[category] = sum(text_lower.count(kw.lower()) for kw in keywords)
return max(scores, key=scores.get)
