Document Classification with NLP

Document Classification with NLP

Overview

This skill implements NLP-based document classification and information extraction for construction projects. Automate document sorting, key term extraction, and content analysis.

Document Types:

• RFIs (Requests for Information)
• Submittals and shop drawings
• Change orders and variations
• Specifications and standards
• Contracts and agreements
• Safety reports and permits

Quick Start

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
import pandas as pd

# Sample training data
documents = [
    ("Please clarify the steel reinforcement spacing for the foundation slab", "RFI"),
    ("Attached shop drawing for HVAC ductwork layout", "Submittal"),
    ("Additional cost for unforeseen soil conditions", "Change Order"),
    ("Fire-rated wall assembly specification Section 09 21 16", "Specification"),
]

texts, labels = zip(*documents)

# Train classifier
classifier = Pipeline([
    ('tfidf', TfidfVectorizer(max_features=1000, ngram_range=(1, 2))),
    ('clf', MultinomialNB())
])

classifier.fit(texts, labels)