Hua Xu

Lightweight predicate extraction for patient-level cancer information and ontology development

2017 · BMC Medical Informatics and Decision Making

BACKGROUND: Knowledge engineering for ontological knowledgebases is resource and time intensive. To alleviate these issues, especially for novices, automated tools from the natural language domain can assist in the development process of ontologies. We focus …

Clinical Named Entity Recognition Using Deep Learning Models.

2017 · PubMed

Clinical Named Entity Recognition (NER) is a critical natural language processing (NLP) task to extract important concepts (named entities) from clinical narratives. Researchers have extensively investigated machine learning models for clinical NER. Recently, there have …

Leveraging existing corpora for de-identification of psychiatric notes using domain adaptation.

2017 · PubMed

De-identification of clinical notes is a special case of named entity recognition. Supervised machine-learning (ML) algorithms have achieved promising results for this task. However, ML-based de-identification systems often require annotating a large number of clinical …

BERT-based Ranking for Biomedical Entity Normalization.

2020 · PubMed

Developing high-performance entity normalization algorithms that can alleviate the term variation problem is of great interest to the biomedical community. Although deep learning-based methods have been successfully applied to biomedical entity normalization, they often depend …

Consistent Representation Learning for Continual Relation Extraction

2022 · Findings of the Association for Computational Linguistics: ACL 2022

Continual relation extraction (CRE) aims to continuously train a model on data with new relations while avoiding forgetting old ones. Some previous work has proved that storing a few typical samples of old relations and …

Learning Discriminative Representations and Decision Boundaries for Open Intent Detection

2022 · arXiv (Cornell University)

Open intent detection is a significant problem in natural language understanding, which aims to identify the unseen open intent while ensuring known intent identification performance. However, current methods face two major challenges. Firstly, they struggle …

A Self-Adjusting Fusion Representation Learning Model for Unaligned Text-Audio Sequences

2022 · arXiv (Cornell University)

Inter-modal interaction plays an indispensable role in multimodal sentiment analysis. Due to different modalities sequences are usually non-alignment, how to integrate relevant information of each modality to learn fusion representations has been one of the …

Noise Imitation Based Adversarial Training for Robust Multimodal Sentiment Analysis

2023 · IEEE Transactions on Multimedia

As an inevitable phenomenon in real-world applications, data imperfection has emerged as one of the most critical challenges for multimodal sentiment analysis. However, existing approaches tend to overly focus on a specific type of imperfection, …

Clinical Abbreviation Disambiguation Using Neural Word Embeddings

2015

This study examined the use of neural word embeddings for clinical abbreviation disambiguation, a special case of word sense disambiguation (WSD). We investigated three different methods for deriving word embeddings from a large unlabeled clinical …

CLAMP – a toolkit for efficiently building customized clinical natural language processing pipelines

2017 · Journal of the American Medical Informatics Association

Existing general clinical natural language processing (NLP) systems such as MetaMap and Clinical Text Analysis and Knowledge Extraction System have been successfully applied to information extraction from clinical text. However, end users often have to …