Unsupervised Open Relation Extraction
This addresses the challenge of unsupervised relation extraction for natural language processing applications, representing an incremental advance.
The paper tackled the problem of extracting relations between named entities from free text in an unsupervised setting, achieving a 5.8% improvement over state-of-the-art with an F1-score of 0.416 on the NYT-FB dataset.
We explore methods to extract relations between named entities from free text in an unsupervised setting. In addition to standard feature extraction, we develop a novel method to re-weight word embeddings. We alleviate the problem of features sparsity using an individual feature reduction. Our approach exhibits a significant improvement by 5.8% over the state-of-the-art relation clustering scoring a F1-score of 0.416 on the NYT-FB dataset.