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Found 48 papers, 24 papers with code
Conversational Bots for Psychotherapy: A Study of Generative Transformer Models Using Domain-specific Dialogues
Through quantitative evaluation of the linguistic quality, we observe that the dialog generation model - DialoGPT (345M) with transfer learning on video data attains scores similar to a human response baseline.
Relation Extraction Using Large Language Models: A Case Study on Acupuncture Point Locations
This study underscores the effectiveness of LLMs like GPT in extracting relations related to acupoint locations, with implications for accurately modeling acupuncture knowledge and promoting standard implementation in acupuncture training and practice.
MIntRec2.0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversations
We believe that MIntRec2. 0 will serve as a valuable resource, providing a pioneering foundation for research in human-machine conversational interactions, and significantly facilitating related applications.
Me LLaMA: Foundation Large Language Models for Medical Applications
In response to this challenge, this study introduces Me-LLaMA, a novel medical LLM family that includes foundation models - Me-LLaMA 13/70B, along with their chat-enhanced versions - Me-LLaMA 13/70B-chat, developed through continual pre-training and instruction tuning of LLaMA2 using large medical datasets.
A Span-based Model for Extracting Overlapping PICO Entities from RCT Publications
On the PICO-Corpus, PICOX obtained higher recall and F1 scores than the baseline and improved the micro recall score from 56. 66 to 67. 33.
Token-Level Contrastive Learning with Modality-Aware Prompting for Multimodal Intent Recognition
To establish an optimal multimodal semantic environment for text modality, we develop a modality-aware prompting module (MAP), which effectively aligns and fuses features from text, video and audio modalities with similarity-based modality alignment and cross-modality attention mechanism.
Ranked #2 on
Multimodal Intent Recognition
on MIntRec
Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation
This study introduces Ascle, a pioneering natural language processing (NLP) toolkit designed for medical text generation.
AI Alignment: A Comprehensive Survey
The former aims to make AI systems aligned via alignment training, while the latter aims to gain evidence about the systems' alignment and govern them appropriately to avoid exacerbating misalignment risks.
Identifying and Extracting Rare Disease Phenotypes with Large Language Models
Despite this, ChatGPT achieved similar or higher accuracy for certain entities (i. e., rare diseases and signs) in the one-shot setting (F1 of 0. 776 and 0. 725).
Complementary and Integrative Health Lexicon (CIHLex) and Entity Recognition in the Literature
Results: From the 198 unique concepts in CIHLex, 62. 1% could be matched to at least one term in the UMLS.
Large language models in biomedical natural language processing: benchmarks, baselines, and recommendations
Biomedical literature is growing rapidly, making it challenging to curate and extract knowledge manually.
A Clustering Framework for Unsupervised and Semi-supervised New Intent Discovery
New intent discovery is of great value to natural language processing, allowing for a better understanding of user needs and providing friendly services.
Improving Large Language Models for Clinical Named Entity Recognition via Prompt Engineering
Results: Using baseline prompts, GPT-3. 5 and GPT-4 achieved relaxed F1 scores of 0. 634, 0. 804 for MTSamples, and 0. 301, 0. 593 for VAERS.
Adaptive Constraint Partition based Optimization Framework for Large-scale Integer Linear Programming(Student Abstract)
Integer programming problems (IPs) are challenging to be solved efficiently due to the NP-hardness, especially for large-scale IPs.
A Self-Adjusting Fusion Representation Learning Model for Unaligned Text-Audio Sequences
In this paper, a Self-Adjusting Fusion Representation Learning Model (SA-FRLM) is proposed to learn robust crossmodal fusion representations directly from the unaligned text and audio sequences.
MIntRec: A New Dataset for Multimodal Intent Recognition
This paper introduces a novel dataset for multimodal intent recognition (MIntRec) to address this issue.
Ranked #1 on
Multimodal Intent Recognition
on MIntRec
Make Acoustic and Visual Cues Matter: CH-SIMS v2.0 Dataset and AV-Mixup Consistent Module
The designed modality mixup module can be regarded as an augmentation, which mixes the acoustic and visual modalities from different videos.
Continual Machine Reading Comprehension via Uncertainty-aware Fixed Memory and Adversarial Domain Adaptation
However, it is a great challenge to learn a new domain incrementally without catastrophically forgetting previous knowledge.
M-SENA: An Integrated Platform for Multimodal Sentiment Analysis
The platform features a fully modular video sentiment analysis framework consisting of data management, feature extraction, model training, and result analysis modules.
Learning Discriminative Representations and Decision Boundaries for Open Intent Detection
To address these issues, this paper presents an original framework called DA-ADB, which successively learns distance-aware intent representations and adaptive decision boundaries for open intent detection.
Consistent Representation Learning for Continual Relation Extraction
Specifically, supervised contrastive learning based on a memory bank is first used to train each new task so that the model can effectively learn the relation representation.
An Open Natural Language Processing Development Framework for EHR-based Clinical Research: A case demonstration using the National COVID Cohort Collaborative (N3C)
Although we use COVID-19 as a use case in this effort, our framework is general enough to be applied to other domains of interest in clinical NLP.
Simple Recurrent Neural Networks is all we need for clinical events predictions using EHR data
As a result, deep learning models developed for sequence modeling, like recurrent neural networks (RNNs) are common architecture for EHR-based clinical events predictive models.
TEXTOIR: An Integrated and Visualized Platform for Text Open Intent Recognition
It is composed of two main modules: open intent detection and open intent discovery.
An Empirical Study of UMLS Concept Extraction from Clinical Notes using Boolean Combination Ensembles
Our objective in this study is to investigate the behavior of Boolean operators on combining annotation output from multiple Natural Language Processing (NLP) systems across multiple corpora and to assess how filtering by aggregation of Unified Medical Language System (UMLS) Metathesaurus concepts affects system performance for Named Entity Recognition (NER) of UMLS concepts.
RGB Image Classification with Quantum Convolutional Ansaetze
With the rapid growth of qubit numbers and coherence times in quantum hardware technology, implementing shallow neural networks on the so-called Noisy Intermediate-Scale Quantum (NISQ) devices has attracted a lot of interest.
Discovering novel drug-supplement interactions using a dietary supplements knowledge graph generated from the biomedical literature
MATERIALS AND METHODS: We created SemRepDS (an extension of SemRep), capable of extracting semantic relations from abstracts by leveraging a DS-specific terminology (iDISK) containing 28, 884 DS terms not found in the UMLS.
Representation Iterative Fusion based on Heterogeneous Graph Neural Network for Joint Entity and Relation Extraction
Joint entity and relation extraction is an essential task in information extraction, which aims to extract all relational triples from unstructured text.
Ranked #2 on
Relation Extraction
on SemEval-2010 Task-8
Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysis
On MOSI and MOSEI datasets, our method surpasses the current state-of-the-art methods.
Deep Open Intent Classification with Adaptive Decision Boundary
In this paper, we propose a post-processing method to learn the adaptive decision boundary (ADB) for open intent classification.
Ranked #1 on
Open Intent Detection
on StackOverFlow(75%known)
Discovering New Intents with Deep Aligned Clustering
In this work, we propose an effective method, Deep Aligned Clustering, to discover new intents with the aid of the limited known intent data.
Ranked #2 on
Open Intent Discovery
on CLINC150
Cross-Modal BERT for Text-Audio Sentiment Analysis
In this paper, we propose the Cross-Modal BERT (CM-BERT), which relies on the interaction of text and audio modality to fine-tune the pre-trained BERT model.
Ranked #3 on
Multimodal Sentiment Analysis
on MOSI
COVID-19 SignSym: a fast adaptation of a general clinical NLP tool to identify and normalize COVID-19 signs and symptoms to OMOP common data model
To this end, this study aims at adapting the existing CLAMP natural language processing tool to quickly build COVID-19 SignSym, which can extract COVID-19 signs/symptoms and their 8 attributes (body location, severity, temporal expression, subject, condition, uncertainty, negation, and course) from clinical text.
CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset with Fine-grained Annotation of Modality
Previous studies in multimodal sentiment analysis have used limited datasets, which only contain unified multimodal annotations.
Finding structural hole spanners based on community forest model and diminishing marginal utility in large scale social networks
In this paper, we try to give a more visual and detailed definition of structural hole spanner based on the existing work, and propose a novel algorithm to identify structural hole spanner based on community forest model and diminishing marginal utility.
Robustly Pre-trained Neural Model for Direct Temporal Relation Extraction
Background: Identifying relationships between clinical events and temporal expressions is a key challenge in meaningfully analyzing clinical text for use in advanced AI applications.
A Post-processing Method for Detecting Unknown Intent of Dialogue System via Pre-trained Deep Neural Network Classifier
In this paper, we propose SofterMax and deep novelty detection (SMDN), a simple yet effective post-processing method for detecting unknown intent in dialogue systems based on pre-trained deep neural network classifiers.
Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinement
Identifying new user intents is an essential task in the dialogue system.
Ranked #1 on
Open Intent Discovery
on ATIS
BERT-based Ranking for Biomedical Entity Normalization
Developing high-performance entity normalization algorithms that can alleviate the term variation problem is of great interest to the biomedical community.
Deep Unknown Intent Detection with Margin Loss
With margin loss, we can learn discriminative deep features by forcing the network to maximize inter-class variance and to minimize intra-class variance.
Ranked #1 on
Open Intent Detection
on SNIPS (25% known)
The Strength of the Weakest Supervision: Topic Classification Using Class Labels
When developing topic classifiers for real-world applications, we begin by defining a set of meaningful topic labels.
Enhancing Clinical Concept Extraction with Contextual Embeddings
We explore a battery of embedding methods consisting of traditional word embeddings and contextual embeddings, and compare these on four concept extraction corpora: i2b2 2010, i2b2 2012, SemEval 2014, and SemEval 2015.
Ranked #1 on
Clinical Concept Extraction
on 2010 i2b2/VA
Finding overlapping community from social networks based on community forest model
Overlapping community detection is the key research work to discover and explore the social networks.
