README.md

metadata

license: apache-2.0
task_categories:
  - question-answering
  - text-classification
language:
  - en
tags:
  - agent

BYU-Idaho Web Content Dataset (NLP-Enhanced)

State-of-the-art university web content dataset with full NLP enrichment: entity extraction, acronym detection, domain terminology, and semantic features. Enterprise-ready for advanced RAG, semantic search, and AI applications.

Dataset Description

Records: 2,448 ultra-high-quality pages Source: byui.edu and subdomains Format: Markdown + NLP metadata (JSON fields) Quality: 40.2% filtered + 91.5/100 avg score + Full NLP extraction Last Updated: December 2025

NLP Enrichment Features

Entity Extraction (spaCy NER)

• 15,289 organizations extracted
• 3,029 locations extracted
• 4,841 people extracted
• 94.2% page coverage

Acronym Detection

• 4,036 acronyms detected and expanded
• 81.5% page coverage
• Includes common education acronyms (FAFSA, GPA, TOEFL, etc.)

Domain Terminology

• 566 BYU-Idaho specific terms found
• 16.7% page coverage
• Includes: I-Learn, Devotional, Pathway, Honor Code, campus buildings, etc.

Key Phrases

• 187 action phrases extracted
• Common educational actions: "apply for admission", "register for classes", etc.

Domain N-grams

• Common domain-specific 3-word phrases
• Frequency-filtered for relevance

Dataset Structure

Core Fields

• index (int64): Sequential ID (1-2448)
• url (string): Source URL
• title (string): Page title
• topic (string): Main heading (H1)
• meta_description (string): SEO description
• content (string): Full Markdown content (avg 2,230 chars)
• category (string): 14 categories (Academics, Admissions, etc.)
• content_type (string): 7 types (informational, guide, FAQ, etc.)
• quality_score (int64): Quality 0-100 (avg: 91.5)
• reading_level (float64): Flesch-Kincaid grade level (avg: 14.4)

NLP Fields

entities (string - JSON):

{
  "organizations": ["BYU-Idaho", "Financial Aid Office", "College of Business"],
  "locations": ["Rexburg", "Idaho", "Manwaring Center"],
  "programs": ["Computer Science", "Nursing"],
  "people": ["David A. Bednar", "Gordon B. Hinckley"]
}

byui_terms (string - JSON):

["Devotional", "I-Learn", "Pathway", "Honor Code", "Manwaring Center"]

acronyms (string - JSON):

{
  "FAFSA": "Free Application for Federal Student Aid",
  "GPA": "Grade Point Average",
  "TOEFL": "Test of English as a Foreign Language"
}

key_phrases (string - JSON):

[
  "apply for admission",
  "register for classes",
  "submit transcripts",
  "complete the FAFSA"
]

domain_ngrams (string - JSON):

[
  "church of jesus christ",
  "brigham young university",
  "learn more about"
]

Usage

Basic Loading

from datasets import load_dataset
import json

ds = load_dataset("BYU-Idaho/Web-Content")['train']

# Parse JSON fields
row = ds[0]
entities = json.loads(row['entities'])
acronyms = json.loads(row['acronyms'])
byui_terms = json.loads(row['byui_terms'])

print(f"Organizations: {entities['organizations']}")
print(f"Acronyms: {list(acronyms.keys())}")

Entity-Based Filtering

import json

# Find pages mentioning specific organizations
def has_organization(row, org_name):
    entities = json.loads(row['entities'])
    return org_name in entities['organizations']

financial_aid_pages = ds.filter(
    lambda x: has_organization(x, 'Financial Aid Office')
)

Acronym Expansion for RAG

# Build acronym lookup table
all_acronyms = {}
for row in ds:
    acronyms = json.loads(row['acronyms'])
    all_acronyms.update(acronyms)

# Use in RAG to expand user queries
def expand_acronyms(query):
    for acronym, expansion in all_acronyms.items():
        if acronym in query:
            query += f" {expansion}"
    return query

# "What is FAFSA?" → "What is FAFSA Free Application for Federal Student Aid?"

BYU-Idaho Term Filtering

# Find pages about specific campus features
def has_byui_term(row, term):
    terms = json.loads(row['byui_terms'])
    return term in terms

devotional_pages = ds.filter(lambda x: has_byui_term(x, 'Devotional'))
pathway_pages = ds.filter(lambda x: has_byui_term(x, 'Pathway'))

Location-Based Search

# Find pages about specific locations
def mentions_location(row, location):
    entities = json.loads(row['entities'])
    return location in entities['locations']

rexburg_pages = ds.filter(lambda x: mentions_location(x, 'Rexburg'))

Advanced: Build Entity Index

from collections import defaultdict
import json

# Build inverted index: entity → list of page indices
entity_index = defaultdict(list)

for idx, row in enumerate(ds):
    entities = json.loads(row['entities'])
    for org in entities['organizations']:
        entity_index[org].append(idx)
    for loc in entities['locations']:
        entity_index[loc].append(idx)

# Quick lookup: all pages mentioning "Tutoring Center"
tutoring_pages = [ds[i] for i in entity_index['Tutoring Center']]

NLP Enrichment Statistics

Feature	Total Extracted	Page Coverage
Organizations	15,289	94.2%
Locations	3,029	94.2%
People	4,841	94.2%
Acronyms	4,036	81.5%
BYU-Idaho Terms	566	16.7%
Key Phrases	187	-

Top Entities Extracted

Organizations (most common):

• BYU-Idaho
• Brigham Young University-Idaho
• Ricks College
• Financial Aid Office
• Accessibility Services Office
• Academic Leadership Office
• Church of Jesus Christ of Latter-day Saints

Locations (most common):

• Rexburg
• Idaho
• Manwaring Center
• BYU-Idaho Center
• Taylor Chapel
• United States

BYU-Idaho Terms:

• Devotional
• Forum
• Pathway
• Honor Code
• I-Learn
• PathwayConnect
• Track System
• Manwaring Center
• Tutoring Center
• Writing Center

Common Acronyms:

• GPA, TOEFL, SAT, ACT, AP
• FAFSA, FERPA, CLEP
• NCAA, ESL, IELTS

Use Cases

Entity-Aware RAG

# Route queries based on entities mentioned
if "Financial Aid" in query_entities:
    context = filter_to_financial_aid_entities()

Acronym-Expanded Search

# Automatically expand acronyms in search
query = expand_all_acronyms(user_query)
results = semantic_search(query)

Faceted Navigation

# Filter by entity types
filters = {
    'organization': 'College of Business',
    'location': 'Manwaring Center',
    'term': 'Devotional'
}

Smart Query Routing

# Detect BYU-Idaho terms and route to specialized retrievers
if any(term in query for term in byui_terms):
    use_institutional_knowledge_retriever()

Relationship Extraction

# Find connections between entities
# "Which offices are in Manwaring Center?"
pages_with_both = find_pages_with_entities(['Manwaring Center'], ['organizations'])

Technical Details

NLP Pipeline:

1. spaCy en_core_web_sm for NER
2. Pattern matching for acronyms
3. Custom BYU-Idaho term dictionary
4. Regex for action phrase extraction
5. N-gram frequency analysis

Entity Types:

• Organizations: spaCy ORG label
• Locations: spaCy GPE label
• People: spaCy PERSON label (filtered)
• Programs: Heuristic-based extraction

Acronym Detection:

• Known education acronyms (pre-defined)
• Pattern: ACRONYM (expansion)
• Pattern: expansion (ACRONYM)

Citation

@misc{byui-web-content-nlp-2025,
  title={BYU-Idaho Web Content Dataset (NLP-Enhanced)},
  author={Ron Vallejo},
  year={2025},
  version={4.0.0},
  publisher={Brigham Young University-Idaho},
  howpublished={\url{https://huggingface.co/datasets/BYU-Idaho/Web-Content}}
}