Document similarity analysis via Natural Language Processing
Description
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.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Controls / human review
ATO: Not reported; PIA: Not published