Learning Disentangled Representations of Negation and Uncertainty

Negation and uncertainty modeling are long-standing tasks in natural language processing. Linguistic theory postulates that expressions of negation and uncertainty are semantically independent from each other and the content they modify. However, previous works on representation learning do not explicitly model this independence. We therefore attempt to disentangle the representations of negation, uncertainty, and content using a Variational Autoencoder. We find that simply supervising the latent representations results in good disentanglement, but auxiliary objectives based on adversarial learning and mutual information minimization can provide additional disentanglement gains.Comment: Accepted to ACL 2022. 18 pages, 7 figures. Code and data are available at https://github.com/jvasilakes/disentanglement-va

Annotated Semantic Predications from SemMedDB

This data was collected from the Semantic MEDLINE Database (SemMedDb) ver 30, December 2016 release. It contains sentences, subject/object entity information, and predicate information as output by SemRep. It also contains annotations indicating whether each semantic predication is indeed expressed in the sentence. The data was used for the paper "Evaluating Active Learning Methods for Annotating Semantic Predications Extracted from MEDLINE", the associated manuscript is under review.National Center for Complementary & Integrative Health Award (#R01AT009457) (Zhang)Agency for Healthcare Research & Quality Grant (#1R01HS022085) (Melton)National Center for Advancing Translational Science (#U01TR002062) (Liu/Pakhomov/Jiang

Annotated Semantic Predications from SemMedDB

This data was collected from the Semantic MEDLINE Database (SemMedDb) ver 30, December 2016 release. It contains sentences, subject/object entity information, and predicate information as output by SemRep. It also contains annotations indicating whether each semantic predication is indeed expressed in the sentence

Data from: Evaluating active learning methods for annotating semantic predications

Objectives: This study evaluated and compared a variety of active learning strategies, including a novel strategy we proposed, as applied to the task of filtering incorrect SemRep semantic predications. Materials and Methods: We evaluated three types of active learning strategies – uncertainty, representative, and combined– on two datasets of semantic predications from SemMedDB covering the domains of substance interactions and clinical medicine, respectively. We also designed a novel combined strategy with dynamic β without hand-tuned hyperparameters. Each strategy was assessed by the Area under the Learning Curve (ALC) and the number of training examples required to achieve a target Area Under the ROC curve (AUC). We also visualized and compared the query patterns of the query strategies. Results: Combined strategies outperformed all other methods in terms of ALC, outperforming the baseline by over 0.05 ALC for both datasets and reducing 58% annotation efforts in the best case. While representative strategies performed well, their performance was matched or outperformed by the combined methods. All the uncertainty sampling methods beat the baseline but they were the worst performing methods overall. Our proposed AL method with dynamic β shows promising ability to achieve near-optimal performance across two datasets. Discussion: Our visual analysis of query patterns indicates that strategies which efficiently obtain a representative subsample perform better on this task. Conclusion: Active learning is shown to be effective at reducing annotation costs for filtering incorrect semantic predications from SemRep. Our proposed AL method demonstrated promising performance

Integrated Dietary Supplement Knowledge Base (iDISK)

README.md describes the structure of the knowledge base and gives installation instructions. idisk_neo4j.dump is a binary file corresponding to the Neo4j release. See the README for details. idisk_rrf.zip is an archive containing the UMLS style RRF files. See the README for details.The integrated Dietary Supplements Knowledge Base (iDISK) covers a variety of dietary supplements, including vitamins, herbs, minerals, etc. It was standardized and integrated from the Dietary Supplements Label Database (DSLD), the "About Herbs" database from Memorial Sloan Kettering Cancer Center (MSKCC), the Canadian Natural Health Products and Ingredients database (NHP), and the Natural Medicines Comprehensive Database (NMCD) developed by the Therapeutic Research Center (TRC). iDISK contains a variety of attributes and relationships describing information about each dietary supplement such as which products it is an ingredient of and what drugs it might interact with.This research was supported by National Center for Complementary & Integrative Health Award (#R01AT009457) (Zhang) and the Agency for Healthcare Research & Quality grant (#1R01HS022085) (Melton)