# LATTE: Latent Type Modeling for Biomedical Entity Linking

arXiv: https://arxiv.org/abs/1911.09787 · HTML: https://mlsanity.com/paper/1911.09787/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2019.

## Built on by (most cited first, 11 of 11)

- [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021)
- [Fast and Effective Biomedical Entity Linking Using a Dual Encoder](https://mlsanity.com/paper/2103.05028/) (2021)
- [Zero-shot Medical Entity Retrieval without Annotation: Learning From Rich Knowledge Graph Semantics](https://mlsanity.com/paper/2105.12682/) (2021)
- [Low Resource Recognition and Linking of Biomedical Concepts from a Large Ontology](https://mlsanity.com/paper/2101.10587/) (2021)
- [Entity-driven Fact-aware Abstractive Summarization of Biomedical Literature](https://mlsanity.com/paper/2203.15959/) (2022)
- [Entity Anchored ICD Coding](https://mlsanity.com/paper/2208.07444/) (2022)
- [Drug and Disease Interpretation Learning with Biomedical Entity Representation Transformer](https://mlsanity.com/paper/2101.09311/) (2021)
- [Kronecker Factorization for Preventing Catastrophic Forgetting in Large-scale Medical Entity Linking](https://mlsanity.com/paper/2111.06012/) (2021)
- [Bridging the Semantic-Numerical Gap: A Numerical Reasoning Method of Cross-modal Knowledge Graph for Material Property Prediction](https://mlsanity.com/paper/2312.09744/) (2023)
- [Iteratively Improving Biomedical Entity Linking and Event Extraction via Hard Expectation-Maximization](https://mlsanity.com/paper/2305.14645/) (2023)
- [Knowledge-injected Prompt Learning for Chinese Biomedical Entity Normalization](https://mlsanity.com/paper/2308.12025/) (2023)

## Abstract (arXiv)

Entity linking is the task of linking mentions of named entities in natural language text, to entities in a curated knowledge-base. This is of significant importance in the biomedical domain, where it could be used to semantically annotate a large volume of clinical records and biomedical literature, to standardized concepts described in an ontology such as Unified Medical Language System (UMLS). We observe that with precise type information, entity disambiguation becomes a straightforward task. However, fine-grained type information is usually not available in biomedical domain. Thus, we propose LATTE, a LATent Type Entity Linking model, that improves entity linking by modeling the latent fine-grained type information about mentions and entities. Unlike previous methods that perform entity linking directly between the mentions and the entities, LATTE jointly does entity disambiguation, and latent fine-grained type learning, without direct supervision. We evaluate our model on two biomedical datasets: MedMentions, a large scale public dataset annotated with UMLS concepts, and a de-identified corpus of dictated doctor's notes that has been annotated with ICD concepts. Extensive experimental evaluation shows our model achieves significant performance improvements over several state-of-the-art techniques.