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fd2e156 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 | """
Utility script to explore arXiv categories and extract them from papers.
"""
import arxiv
from typing import List, Dict
def get_paper_categories(paper_id: str) -> List[str]:
"""
Get the categories of a specific paper.
Args:
paper_id: arXiv ID (e.g., "2406.01234")
Returns:
List of category codes (e.g., ["cs.CL", "cs.AI"])
"""
search = arxiv.Search(id_list=[paper_id])
results = list(search.results())
if results:
paper = results[0]
return paper.categories
else:
return []
def search_by_category(category: str, max_results: int = 10) -> List[Dict]:
"""
Search for papers in a specific category.
Args:
category: Category code (e.g., "cs.CL")
max_results: Maximum number of results
Returns:
List of paper information dictionaries
"""
search = arxiv.Search(
query=f"cat:{category}",
max_results=max_results,
sort_by=arxiv.SortCriterion.SubmittedDate
)
papers = []
for paper in search.results():
papers.append({
'id': paper.entry_id,
'title': paper.title,
'authors': [author.name for author in paper.authors],
'categories': paper.categories,
'published': paper.published.isoformat(),
'summary': paper.summary[:200] + "..." if len(paper.summary) > 200 else paper.summary
})
return papers
def get_common_categories() -> Dict[str, str]:
"""
Get a dictionary of common arXiv categories with descriptions.
"""
return {
# Computer Science
"cs.AI": "Artificial Intelligence",
"cs.CL": "Computation and Language (NLP)",
"cs.CV": "Computer Vision and Pattern Recognition",
"cs.LG": "Machine Learning",
"cs.NE": "Neural and Evolutionary Computing",
"cs.IR": "Information Retrieval",
"cs.SE": "Software Engineering",
"cs.DC": "Distributed, Parallel, and Cluster Computing",
"cs.CR": "Cryptography and Security",
"cs.DB": "Databases",
"cs.AR": "Hardware Architecture",
"cs.CG": "Computational Geometry",
"cs.GT": "Computer Science and Game Theory",
"cs.LO": "Logic in Computer Science",
"cs.MS": "Mathematical Software",
"cs.NA": "Numerical Analysis",
"cs.OS": "Operating Systems",
"cs.PF": "Performance",
"cs.PL": "Programming Languages",
"cs.RO": "Robotics",
"cs.SC": "Symbolic Computation",
"cs.SD": "Sound",
"cs.SI": "Social and Information Networks",
"cs.SY": "Systems and Control",
# Mathematics
"math.OC": "Optimization and Control",
"math.ST": "Statistics Theory",
"math.NA": "Numerical Analysis",
"math.PR": "Probability",
"math.AT": "Algebraic Topology",
"math.AG": "Algebraic Geometry",
"math.AP": "Analysis of PDEs",
"math.CT": "Category Theory",
"math.CA": "Classical Analysis and ODEs",
"math.CO": "Combinatorics",
"math.AC": "Commutative Algebra",
"math.CV": "Complex Variables",
"math.DG": "Differential Geometry",
"math.DS": "Dynamical Systems",
"math.FA": "Functional Analysis",
"math.GM": "General Mathematics",
"math.GN": "General Topology",
"math.GT": "Geometric Topology",
"math.GR": "Group Theory",
"math.HO": "History and Overview",
"math.IT": "Information Theory",
"math.KT": "K-Theory and Homology",
"math.LO": "Logic",
"math.MP": "Mathematical Physics",
"math.MG": "Metric Geometry",
"math.NT": "Number Theory",
"math.OA": "Operator Algebras",
"math.RA": "Rings and Algebras",
"math.RT": "Representation Theory",
"math.SP": "Spectral Theory",
"math.SG": "Symplectic Geometry",
# Physics
"physics.comp-ph": "Computational Physics",
"physics.data-an": "Data Analysis, Statistics and Probability",
"physics.acc-ph": "Accelerator Physics",
"physics.ao-ph": "Atmospheric and Oceanic Physics",
"physics.app-ph": "Applied Physics",
"physics.atm-clus": "Atomic and Molecular Clusters",
"physics.atom-ph": "Atomic Physics",
"physics.bio-ph": "Biological Physics",
"physics.chem-ph": "Chemical Physics",
"physics.class-ph": "Classical Physics",
"physics.flu-dyn": "Fluid Dynamics",
"physics.gen-ph": "General Physics",
"physics.geo-ph": "Geophysics",
"physics.hist-ph": "History and Philosophy of Physics",
"physics.ins-det": "Instrumentation and Detectors",
"physics.med-ph": "Medical Physics",
"physics.optics": "Optics",
"physics.plasm-ph": "Plasma Physics",
"physics.pop-ph": "Popular Physics",
"physics.soc-ph": "Physics and Society",
"physics.space-ph": "Space Physics",
# Quantitative Biology
"q-bio.BM": "Biomolecules",
"q-bio.CB": "Cell Behavior",
"q-bio.GN": "Genomics",
"q-bio.MN": "Molecular Networks",
"q-bio.NC": "Neurons and Cognition",
"q-bio.OT": "Other Quantitative Biology",
"q-bio.PE": "Populations and Evolution",
"q-bio.QM": "Quantitative Methods",
"q-bio.SC": "Subcellular Processes",
"q-bio.TO": "Tissues and Organs"
}
# Example usage
if __name__ == "__main__":
# Example 1: Get categories of a specific paper
paper_id = "2406.01234"
categories = get_paper_categories(paper_id)
print(f"Categories for {paper_id}: {categories}")
# Example 2: Search papers in a category
category = "cs.CL"
papers = search_by_category(category, max_results=3)
print(f"\nRecent papers in {category}:")
for paper in papers:
print(f"- {paper['title']}")
print(f" Categories: {paper['categories']}")
print()
# Example 3: Show common categories
common_cats = get_common_categories()
print("Common arXiv Categories:")
for code, description in list(common_cats.items())[:10]: # Show first 10
print(f"{code}: {description}") |