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Found 70 papers, 18 papers with code
CASIA at SemEval-2022 Task 11: Chinese Named Entity Recognition for Complex and Ambiguous Entities
This paper describes our approach to develop a complex named entity recognition system in SemEval 2022 Task 11: MultiCoNER Multilingual Complex Named Entity Recognition, Track 9 - Chinese.
Set Generation Networks for End-to-End Knowledge Base Population
In this paper, we formulate end-to-end KBP as a direct set generation problem, avoiding considering the order of multiple facts.
CroAno : A Crowd Annotation Platform for Improving Label Consistency of Chinese NER Dataset
2) Inconsistency Detector: CroAno employs a detector to locate corpus-level label inconsistency and provides users an interface to correct inconsistent entities in batches.
CogKGE: A Knowledge Graph Embedding Toolkit and Benchmark for Representing Multi-source and Heterogeneous Knowledge
Designing CogKGE aims to provide a unified programming framework for KGE tasks and a series of knowledge representations for downstream tasks.
Augmentation, Retrieval, Generation: Event Sequence Prediction with a Three-Stage Sequence-to-Sequence Approach
Currently existing approach models this task as a statistical induction problem, to predict a sequence of events by exploring the similarity between the given goal and the known sequences of events.
Document-Level Relation Extraction via Pair-Aware and Entity-Enhanced Representation Learning
Document-level relation extraction aims to recognize relations among multiple entity pairs from a whole piece of article.
Generating Temporally-ordered Event Sequences via Event Optimal Transport
The other issue is that the model adopts a word-level objective to model events in texts, failing to evaluate the predicted results of the model from the perspective of event sequence.
Uncertain Local-to-Global Networks for Document-Level Event Factuality Identification
Moreover, we propose an Uncertain Information Aggregation module to leverage the global structure for integrating the local information.
Incremental Event Detection via Knowledge Consolidation Networks
However, existing incremental learning methods cannot handle semantic ambiguity and training data imbalance problems between old and new classes in the task of incremental event detection.
CASIA@SMM4H’22: A Uniform Health Information Mining System for Multilingual Social Media Texts
This paper presents a description of our system in SMM4H-2022, where we participated in task 1a, task 4, and task 6 to task 10.
Learning Reasoning Patterns for Relational Triple Extraction with Mutual Generation of Text and Graph
To capture the relation type inference logic of the paths, we propose to understand the unlabeled conceptual expressions by reconstructing the sentence from the relational graph (graph-to-text generation) in a self-supervised manner.
Chinese Named Entity Recognition via Adaptive Multi-pass Memory Network with Hierarchical Tagging Mechanism
Specifically, to reduce the errors of predicting entity boundaries, we propose an adaptive multi-pass memory network to exploit lexical knowledge.
Chinese Named Entity Recognition
named-entity-recognition
+3
Classification, Extraction, and Normalization : CASIA_Unisound Team at the Social Media Mining for Health 2021 Shared Tasks
This is the system description of the CASIA_Unisound team for Task 1, Task 7b, and Task 8 of the sixth Social Media Mining for Health Applications (SMM4H) shared task in 2021.
FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction
In this paper, we propose a privacy-preserving medical relation extraction model based on federated learning, which enables training a central model with no single piece of private local data being shared or exchanged.
Event Extraction as Machine Reading Comprehension
ii) Our model is excelled in the data-scarce scenario, for example, obtaining 49. 8{\%} in F1 for event argument extraction with only 1{\%} data, compared with 2. 2{\%} of the previous method.
Continual Few-shot Event Detection via Hierarchical Augmentation Networks
The CFED task is challenging as it involves memorizing previous event types and learning new event types with few-shot samples.
Zero-Shot Cross-Lingual Document-Level Event Causality Identification with Heterogeneous Graph Contrastive Transfer Learning
Event Causality Identification (ECI) refers to the detection of causal relations between events in texts.
SimuCourt: Building Judicial Decision-Making Agents with Real-world Judgement Documents
In this paper, (1) we introduce SimuCourt, a judicial benchmark that encompasses 420 judgment documents from real-world, spanning the three most common types of judicial cases, and a novel task Judicial Decision-Making to evaluate the judicial analysis and decision-making power of agents.
Whispers that Shake Foundations: Analyzing and Mitigating False Premise Hallucinations in Large Language Models
Large Language Models (LLMs) have shown impressive capabilities but still suffer from the issue of hallucinations.
Focus on Your Question! Interpreting and Mitigating Toxic CoT Problems in Commonsense Reasoning
Large language models exhibit high-level commonsense reasoning abilities, especially with enhancement methods like Chain-of-Thought (CoT).
Cutting Off the Head Ends the Conflict: A Mechanism for Interpreting and Mitigating Knowledge Conflicts in Language Models
Moreover, we reveal that the pivotal point at which knowledge conflicts emerge in LMs is the integration of inconsistent information flows by memory heads and context heads.
Tug-of-War Between Knowledge: Exploring and Resolving Knowledge Conflicts in Retrieval-Augmented Language Models
Then, we investigate the behavior and preference of RALMs from the following two perspectives: (1) Conflicts between internal memory and external sources: We find that stronger RALMs emerge with the Dunning-Kruger effect, persistently favoring their faulty internal memory even when correct evidence is provided.
The Da Vinci Code of Large Pre-trained Language Models: Deciphering Degenerate Knowledge Neurons
This paper provides a comprehensive definition of DKNs that covers both structural and functional aspects, pioneering the study of structures in PLMs' factual knowledge storage units.
WilKE: Wise-Layer Knowledge Editor for Lifelong Knowledge Editing
Knowledge editing aims to rectify inaccuracies in large language models (LLMs) without costly retraining for outdated or erroneous knowledge.
Enhancing Large Language Models with Pseudo- and Multisource- Knowledge Graphs for Open-ended Question Answering
In summary, our results pave the way for enhancing LLMs by incorporating Pseudo- and Multisource-KGs, particularly in the context of open-ended questions.
Oasis: Data Curation and Assessment System for Pretraining of Large Language Models
To this end, we present a pretraining corpus curation and assessment platform called Oasis -- a one-stop system for data quality improvement and quantification with user-friendly interactive interfaces.
ZhuJiu: A Multi-dimensional, Multi-faceted Chinese Benchmark for Large Language Models
To this end, we propose the ZhuJiu benchmark, which has the following strengths: (1) Multi-dimensional ability coverage: We comprehensively evaluate LLMs across 7 ability dimensions covering 51 tasks.
Journey to the Center of the Knowledge Neurons: Discoveries of Language-Independent Knowledge Neurons and Degenerate Knowledge Neurons
We design cross-lingual knowledge editing experiments, demonstrating that the PLMs can accomplish this task based on language-independent neurons; (2) The discovery of Degenerate Knowledge Neurons, a novel type of neuron showing that different knowledge neurons can store the same fact.
Learning Through Guidance: Knowledge Distillation for Endoscopic Image Classification
However, there is a lack of focus on developing lightweight models which can run in low-resource environments, which are typically encountered in medical clinics.
DIFFender: Diffusion-Based Adversarial Defense against Patch Attacks
In this paper, we propose DIFFender, a novel defense method that leverages a text-guided diffusion model to defend against adversarial patches.
Programming Language Agnostic Mining of Code and Language Pairs with Sequence Labeling Based Question Answering
In particular, we propose to apply the BIO tagging scheme instead of the conventional binary scheme to mine the code solutions which are often composed of multiple blocks of a post.
Contrastive Vision-Language Pre-training with Limited Resources
However, these works require a tremendous amount of data and computational resources (e. g., billion-level web data and hundreds of GPUs), which prevent researchers with limited resources from reproduction and further exploration.
Document-level Event Extraction via Parallel Prediction Networks
We argue that sentence-level extractors are ill-suited to the DEE task where event arguments always scatter across sentences and multiple events may co-exist in a document.
Knowledge-Enriched Event Causality Identification via Latent Structure Induction Networks
Specifically, to make use of the descriptive knowledge, we devise a Descriptive Graph Induction module to obtain and encode the graph-structured descriptive knowledge.
Automatic ICD Coding via Interactive Shared Representation Networks with Self-distillation Mechanism
The ICD coding task aims at assigning codes of the International Classification of Diseases in clinical notes.
CogIE: An Information Extraction Toolkit for Bridging Texts and CogNet
CogNet is a knowledge base that integrates three types of knowledge: linguistic knowledge, world knowledge and commonsense knowledge.
A Large-Scale Chinese Multimodal NER Dataset with Speech Clues
In this paper, we aim to explore an uncharted territory, which is Chinese multimodal named entity recognition (NER) with both textual and acoustic contents.
LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification
On the other hand, our approach employs a dual mechanism, which is a learnable augmentation framework and can interactively adjust the generation process to generate task-related sentences.
Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement
Current models for event causality identification (ECI) mainly adopt a supervised framework, which heavily rely on labeled data for training.
Jointly Extracting Explicit and Implicit Relational Triples with Reasoning Pattern Enhanced Binary Pointer Network
To explore entity pairs that may be implicitly connected by relations, we propose a binary pointer network to extract overlapping relational triples relevant to each word sequentially and retain the information of previously extracted triples in an external memory.
Probing into the Root: A Dataset for Reason Extraction of Structural Events from Financial Documents
This paper proposes a new task regarding event reason extraction from document-level texts.
CogNet: Bridging Linguistic Knowledge, World Knowledge and Commonsense Knowledge
In this paper, we present CogNet, a knowledge base (KB) dedicated to integrating three types of knowledge: (1) linguistic knowledge from FrameNet, which schematically describes situations, objects and events.
Named Entity Recognition in Multi-level Contexts
In this paper, we propose a unified framework to incorporate multi-level contexts for named entity recognition.
Reconstructing Event Regions for Event Extraction via Graph Attention Networks
Event information is usually scattered across multiple sentences within a document.
Joint Entity and Relation Extraction with Set Prediction Networks
Compared with cross-entropy loss that highly penalizes small shifts in triple order, the proposed bipartite matching loss is invariant to any permutation of predictions; thus, it can provide the proposed networks with a more accurate training signal by ignoring triple order and focusing on relation types and entities.
Ranked #1 on
Joint Entity and Relation Extraction
on NYT
How Does Context Matter? On the Robustness of Event Detection with Context-Selective Mask Generalization
Event detection (ED) aims to identify and classify event triggers in texts, which is a crucial subtask of event extraction (EE).
KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision
Modern models of event causality detection (ECD) are mainly based on supervised learning from small hand-labeled corpora.
Event Coreference Resolution via a Multi-loss Neural Network without Using Argument Information
Event coreference resolution(ECR) is an important task in Natural Language Processing (NLP) and nearly all the existing approaches to this task rely on event argument information.
Towards Causal Explanation Detection with Pyramid Salient-Aware Network
PSAN can assist in causal explanation detection via capturing the salient semantics of discourses contained in their keywords with a bottom graph-based word-level salient network.
Distantly Supervised Relation Extraction in Federated Settings
In this paper, we propose a federated denoising framework to suppress label noise in federated settings.
HyperCore: Hyperbolic and Co-graph Representation for Automatic ICD Coding
Specifically, we propose a hyperbolic representation method to leverage the code hierarchy.
Clinical-Coder: Assigning Interpretable ICD-10 Codes to Chinese Clinical Notes
In this paper, we introduce Clinical-Coder, an online system aiming to assign ICD codes to Chinese clinical notes.
Knowledge Guided Metric Learning for Few-Shot Text Classification
This is mainly due to the fact that human beings can leverage knowledge obtained from relevant tasks.
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.
Neural Cross-Lingual Event Detection with Minimal Parallel Resources
In this paper, we propose a new method for cross-lingual ED, demonstrating a minimal dependency on parallel resources.
Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network
The lack of word boundaries information has been seen as one of the main obstacles to develop a high performance Chinese named entity recognition (NER) system.
Ranked #11 on
Chinese Named Entity Recognition
on Weibo NER
Chinese Named Entity Recognition
named-entity-recognition
+2
THU\_NGN at SemEval-2019 Task 12: Toponym Detection and Disambiguation on Scientific Papers
First name: Tao Last name: Qi Email: taoqi. qt@gmail. com Affiliation: Department of Electronic Engineering, Tsinghua University First name: Suyu Last name: Ge Email: gesy17@mails. tsinghua. edu. cn Affiliation: Department of Electronic Engineering, Tsinghua University First name: Chuhan Last name: Wu Email: wuch15@mails. tsinghua. edu. cn Affiliation: Department of Electronic Engineering, Tsinghua University First name: Yubo Last name: Chen Email: chen-yb18@mails. tsinghua. edu. cn Affiliation: Department of Electronic Engineering, Tsinghua University First name: Yongfeng Last name: Huang Email: yfhuang@mail. tsinghua. edu. cn Affiliation: Department of Electronic Engineering, Tsinghua University Toponym resolution is an important and challenging task in the neural language processing field, and has wide applications such as emergency response and social media geographical event analysis.
Collective Event Detection via a Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms
Traditional approaches to the task of ACE event detection primarily regard multiple events in one sentence as independent ones and recognize them separately by using sentence-level information.
Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism
However, existing methods for Chinese NER either do not exploit word boundary information from CWS or cannot filter the specific information of CWS.
Ranked #1 on
Chinese Named Entity Recognition
on SighanNER
Chinese Named Entity Recognition
Chinese Word Segmentation
+4
Event Detection via Gated Multilingual Attention Mechanism
In specific, to alleviate data scarcity problem, we exploit the consistent information in multilingual data via context attention mechanism.
DCFEE: A Document-level Chinese Financial Event Extraction System based on Automatically Labeled Training Data
We present an event extraction framework to detect event mentions and extract events from the document-level financial news.
Neural Metaphor Detecting with CNN-LSTM Model
In addition, we compare the performance of the softmax classifier and conditional random field (CRF) for sequential labeling in this task.
Automatically Labeled Data Generation for Large Scale Event Extraction
Modern models of event extraction for tasks like ACE are based on supervised learning of events from small hand-labeled data.
Exploiting Argument Information to Improve Event Detection via Supervised Attention Mechanisms
This paper tackles the task of event detection (ED), which involves identifying and categorizing events.
