Document similarity analysis via Natural Language Processing

Overview

Development stage

In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.

High-impact designation

Not reported

Impact justification

Not reported

Start date

2025 (year precision)

Withheld from public reporting

Not reported

Mission

Topic area

Not reported

Operational functions

No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

The AI system utilizes a sentence-transformer model (all-mpnet-base-v2) and a statistical keyword method (TF-IDF) to perform document similarity tasks. These models are deployed entirely within the agency’s secure firewalls, ensuring no external data exposure and no external access. These AI models are designed for natural language processing (NLP) tasks. Via all-mpnet-base-v2, the model will use utilize deep learning models to capture semantic meaning, ideal for conceptual similarity beyond exact wording. Via Term Frequency-Inverse Document Frequency (TF-IDF), the model will focus on lexical overlap and is effective for direct technical document comparison. Both models calculate Cosine similarity, with scores ranging from -1 to 1. Scores can be interpreted as follows: identical (1), highly similar (0.8-0.99), somewhat similar (~0.5), mostly dissimilar (0.1-0.3), completely dissimilar (0) and opposite (-1 to 0). Document similarity analysis can significantly boost productivity since a large volume of documents can quickly and efficiently be analyzed for extent of contextual or lexical similarity and can aid in routine planning for proposal administration. Cosine similarity is also a well-established method to quantify similarity and is already used within NSF tools.

Expected benefits

Not reported

System outputs

Not reported

Technology

AI classification

Not reported

System name(s)

Not reported

Custom-developed code

Not reported

Public source code

Not reported

Sourcing

How it was built

Not reported

Vendor (as reported)

Not reported

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

Not reported

Privacy Impact Assessment

Not reported

Authorization to Operate

Not reported

Demographic features

Not reported

Training and evaluation data

Not reported

Federal Data Catalog

Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported