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Found 27 papers, 8 papers with code
Answer Sequence Learning with Neural Networks for Answer Selection in Community Question Answering
In this paper, the answer selection problem in community question answering (CQA) is regarded as an answer sequence labeling task, and a novel approach is proposed based on the recurrent architecture for this problem.
Incorporating Label Dependency for Answer Quality Tagging in Community Question Answering via CNN-LSTM-CRF
In community question answering (cQA), the quality of answers are determined by the matching degree between question-answer pairs and the correlation among the answers.
Investigating Different Syntactic Context Types and Context Representations for Learning Word Embeddings
The number of word embedding models is growing every year.
LCQMC:A Large-scale Chinese Question Matching Corpus
In this paper, we first use a search engine to collect large-scale question pairs related to high-frequency words from various domains, then filter irrelevant pairs by the Wasserstein distance, and finally recruit three annotators to manually check the left pairs.
The BQ Corpus: A Large-scale Domain-specific Chinese Corpus For Sentence Semantic Equivalence Identification
As the largest manually annotated public Chinese SSEI corpus in the bank domain, the BQ corpus is not only useful for Chinese question semantic matching research, but also a significant resource for cross-lingual and cross-domain SSEI research.
HITSZ-ICRC: A Report for SMM4H Shared Task 2019-Automatic Classification and Extraction of Adverse Effect Mentions in Tweets
This is the system description of the Harbin Institute of Technology Shenzhen (HITSZ) team for the first and second subtasks of the fourth Social Media Mining for Health Applications (SMM4H) shared task in 2019.
A Method to Learn Embedding of a Probabilistic Medical Knowledge Graph: Algorithm Development
This paper proposes an algorithm named as PrTransH to learn embedding vectors from real world EMR data based medical knowledge.
Trigger Word Detection and Thematic Role Identification via BERT and Multitask Learning
In this paper, we present our approaches for trigger word detection (task 1) and the identification of its thematic role (task 2) in AGAC track of BioNLP Open Shared Task 2019.
A Deep Learning-Based System for PharmaCoNER
The Biological Text Mining Unit at BSC and CNIO organized the first shared task on chemical {\&} drug mention recognition from Spanish medical texts called PharmaCoNER (Pharmacological Substances, Compounds and proteins and Named Entity Recognition track) in 2019, which includes two tracks: one for NER offset and entity classification (track 1) and the other one for concept indexing (track 2).
Semi-supervised Visual Feature Integration for Pre-trained Language Models
In the framework, the visual features are obtained through a visualization and fusion mechanism.
Natural Language Inference
Natural Language Understanding
+2
Overview of the CCKS 2019 Knowledge Graph Evaluation Track: Entity, Relation, Event and QA
Knowledge graph models world knowledge as concepts, entities, and the relationships between them, which has been widely used in many real-world tasks.
Decomposing Word Embedding with the Capsule Network
We propose a Capsule network-based method to Decompose the unsupervised word Embedding of an ambiguous word into context specific Sense embedding, called CapsDecE2S.
Transforming Recurrent Neural Networks with Attention and Fixed-point Equations
It has two major parts.
CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice.
Ranked #1 on
Semantic Similarity
on CHIP-STS
Connecting Compression Spaces with Transformer for Approximate Nearest Neighbor Search
Specifically, based on transformer, we propose a new network structure to compress the feature into a low dimensional space, and an inhomogeneous neighborhood relationship preserving (INRP) loss that aims to maintain high search accuracy.
Multimodal data matters: language model pre-training over structured and unstructured electronic health records
As two important textual modalities in electronic health records (EHR), both structured data (clinical codes) and unstructured data (clinical narratives) have recently been increasingly applied to the healthcare domain.
CATNet: Cross-event Attention-based Time-aware Network for Medical Event Prediction
It is a time-aware, event-aware and task-adaptive method with the following advantages: 1) modeling heterogeneous information and temporal information in a unified way and considering temporal irregular characteristics locally and globally respectively, 2) taking full advantage of correlations among different types of events via cross-event attention.
SetGNER: General Named Entity Recognition as Entity Set Generation
Distinguished from the set-prediction NER framework, our method treats each entity as a sequence and is capable of recognizing discontinuous mentions.
Revisiting Event Argument Extraction: Can EAE Models Learn Better When Being Aware of Event Co-occurrences?
Under this framework, we experiment with 3 different training-inference schemes on 4 datasets (ACE05, RAMS, WikiEvents and MLEE) and discover that via training the model to extract all events in parallel, it can better distinguish the semantic boundary of each event and its ability to extract single event gets substantially improved.
SHAPE: A Sample-adaptive Hierarchical Prediction Network for Medication Recommendation
Effectively medication recommendation with complex multimorbidity conditions is a critical task in healthcare.
PromptCBLUE: A Chinese Prompt Tuning Benchmark for the Medical Domain
Biomedical language understanding benchmarks are the driving forces for artificial intelligence applications with large language model (LLM) back-ends.
Overview of the PromptCBLUE Shared Task in CHIP2023
Many teams from both the industry and academia participated in the shared tasks, and the top teams achieved amazing test results.
Improving Natural Language Understanding with Computation-Efficient Retrieval Representation Fusion
Retrieval-based augmentations that aim to incorporate knowledge from an external database into language models have achieved great success in various knowledge-intensive (KI) tasks, such as question-answering and text generation.
Natural Language Understanding
Neural Architecture Search
+5
Toward Robust Multimodal Learning using Multimodal Foundational Models
Recently, CLIP-based multimodal foundational models have demonstrated impressive performance on numerous multimodal tasks by learning the aligned cross-modal semantics of image and text pairs, but the multimodal foundational models are also unable to directly address scenarios involving modality absence.
Advancing Biomedical Text Mining with Community Challenges
In this article, we review the recent advances in community challenges specific to Chinese biomedical text mining.
HITSZ-ICRC: A Report for SMM4H Shared Task 2020-Automatic Classification of Medications and Adverse Effect in Tweets
This is the system description of the Harbin Institute of Technology Shenzhen (HITSZ) team for the first and second subtasks of the fifth Social Media Mining for Health Applications (SMM4H) shared task in 2020.
