Search Results for author:
Found 16 papers, 11 papers with code
Improving Broad-Coverage Medical Entity Linking with Semantic Type Prediction and Large-Scale Datasets
To address the dearth of annotated training data for medical entity linking, we present WikiMed and PubMedDS, two large-scale medical entity linking datasets, and demonstrate that pre-training MedType on these datasets further improves entity linking performance.
Robust Knowledge Graph Completion with Stacked Convolutions and a Student Re-Ranking Network
We develop a deep convolutional network that utilizes textual entity representations and demonstrate that our model outperforms recent KG completion methods in this challenging setting.
TextEssence: A Tool for Interactive Analysis of Semantic Shifts Between Corpora
Embeddings of words and concepts capture syntactic and semantic regularities of language; however, they have seen limited use as tools to study characteristics of different corpora and how they relate to one another.
Jointly Embedding Entities and Text with Distant Supervision
Learning representations for knowledge base entities and concepts is becoming increasingly important for NLP applications.
Introducing Information Retrieval for Biomedical Informatics Students
Introducing biomedical informatics (BMI) students to natural language processing (NLP) requires balancing technical depth with practical know-how to address application-focused needs.
Insights into Analogy Completion from the Biomedical Domain
Analogy completion has been a popular task in recent years for evaluating the semantic properties of word embeddings, but the standard methodology makes a number of assumptions about analogies that do not always hold, either in recent benchmark datasets or when expanding into other domains.
HARE: a Flexible Highlighting Annotator for Ranking and Exploration
Exploration and analysis of potential data sources is a significant challenge in the application of NLP techniques to novel information domains.
Second-Order Word Embeddings from Nearest Neighbor Topological Features
We introduce second-order vector representations of words, induced from nearest neighborhood topological features in pre-trained contextual word embeddings.
Characterizing the impact of geometric properties of word embeddings on task performance
Analysis of word embedding properties to inform their use in downstream NLP tasks has largely been studied by assessing nearest neighbors.
Automated Coding of Under-Studied Medical Concept Domains: Linking Physical Activity Reports to the International Classification of Functioning, Disability, and Health
Both classification and candidate selection approaches present distinct strengths for automated coding in under-studied domains, and we highlight that the combination of (i) a small annotated data set; (ii) expert definitions of codes of interest; and (iii) a representative text corpus is sufficient to produce high-performing automated coding systems.
Embedding Transfer for Low-Resource Medical Named Entity Recognition: A Case Study on Patient Mobility
Functioning is gaining recognition as an important indicator of global health, but remains under-studied in medical natural language processing research.
Classifying the reported ability in clinical mobility descriptions
Finally, we highlight several challenges in classifying performance assertions, including capturing information about sources of assistance, incorporating syntactic structure and negation scope, and handling new modalities at test time.
Writing habits and telltale neighbors: analyzing clinical concept usage patterns with sublanguage embeddings
Natural language processing techniques are being applied to increasingly diverse types of electronic health records, and can benefit from in-depth understanding of the distinguishing characteristics of medical document types.
Development of Natural Language Processing Tools to Support Determination of Federal Disability Benefits in the U.S.
The disability benefits programs administered by the US Social Security Administration (SSA) receive between 2 and 3 million new applications each year.
Translational NLP: A New Paradigm and General Principles for Natural Language Processing Research
Natural language processing (NLP) research combines the study of universal principles, through basic science, with applied science targeting specific use cases and settings.
Definition drives design: Disability models and mechanisms of bias in AI technologies
The increasing deployment of artificial intelligence (AI) tools to inform decision making across diverse areas including healthcare, employment, social benefits, and government policy, presents a serious risk for disabled people, who have been shown to face bias in AI implementations.
