Novel Image Caption Quality Metric using Named Entity Recognition, Semantic Similarity, and Propositional Understanding Overview This project aims to develop an innovative metric for assessing the quality of image captions using a combination of cutting-edge techniques: Named Entity Recognition (NER), Semantic Similarity, and Propositional Understanding. By leveraging these advanced methods, we aim to enhance the accuracy and depth of evaluating the relevance and coherence of image captions.
Features Named Entity Recognition (NER): Identifies and extracts entities (such as persons, locations, organizations) within captions, enriching the evaluation process by considering specific entities mentioned. Semantic Similarity: Measures the semantic closeness between the caption and the image content, providing a deeper understanding of the contextual relevance. Propositional Understanding: Analyzes the logical structure and coherence of captions, ensuring they convey meaningful and coherent messages. Key Components NER Module: Utilizes state-of-the-art NER models to extract entities from captions and images. Semantic Similarity Engine: Incorporates advanced algorithms to quantify the semantic relationship between captions and image features. Propositional Analysis Framework: Implements methodologies to assess the logical structure and coherence of captions.
pip install -r requirements.txt