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- Unified Named Entity Recognition as Word-Word Relation Classification392
This work develops a neural framework, in which the unified NER is modeled as a 2D grid of word pairs, and proposes multi-granularity 2D convolutions for better refining the grid representations.
- A neural joint model for entity and relation extraction from biomedical text251
It is demonstrated that the model based on neural networks is effective for biomedical entity and relation extraction and parameter sharing is an alternative method for neural models to jointly process this task.
- ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network226
A Multi-Filter Residual Convolutional Neural Network (MultiResCNN) for ICD coding that utilizes a multi-filter convolutional layer to capture various text patterns with different lengths and a residual convolutionAL layer to enlarge the receptive field.
- Fine-Tuning Bidirectional Encoder Representations From Transformers (BERT)–Based Models on Large-Scale Electronic Health Record Notes: An Empirical Study178
This work shows that BERT-based models have achieved state-of-the-art performance for biomedical and clinical entity normalization and can be readily fine-tuned to normalize any kind of named entities.
- MRN: A Locally and Globally Mention-Based Reasoning Network for Document-Level Relation Extraction125
A novel mention-based reasoning (MRN) module based on explicitly and collaboratively local and global reasoning is proposed and a co-predictor module to predict entity relations based on local and global entity and relation representations jointly is designed.
- A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition123
Experimental results on multiple benchmark datasets show that the proposed span-based model can be regarded as a relation extraction paradigm essentially and is highly competitive for overlapped and discontinuous NER.
- LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model114
In this work, a novel structure-aware GLM is proposed, fully unleashing the power of syntactic knowledge for UIE, and rich task-adaptive structural bias is learned that greatly resolves the UIE crux, the long-range dependence issue and boundary identifying.
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- HiTrans: A Transformer-Based Context- and Speaker-Sensitive Model for Emotion Detection in Conversations109
A transformer-based context- and speaker-sensitive model for EDC, namely HiTrans, which consists of two hierarchical transformers, which outperforms previous state-of-the-art models.
- Encoder-Decoder Based Unified Semantic Role Labeling with Label-Aware Syntax89
A novel unified SRL framework based on the sequence-to-sequence architecture with double enhancement in both the encoder and decoder sides is investigated, and in-depth analysis demonstrates that the model can effectively capture the correlations between syntactic and SRL structures.
- Rethinking Boundaries: End-To-End Recognition of Discontinuous Mentions with Pointer Networks84
An innovative model for discontinuous NER based on pointer networks, where the pointer simultaneously decides whether a token at each decoding frame constitutes an entity mention and where the next constituent token is, which is the kernel source to NER.
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- Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning78
Deep learning models can significantly improve the performance of ADE-related information extraction and may be effective for named entity recognition and relation extraction, but it depends on the methods, data, and other factors.
- Better Combine Them Together! Integrating Syntactic Constituency and Dependency Representations for Semantic Role Labeling76
A TreeLSTM-based integration, collaboratively learning the phrasal boundaries from the constituency and the semantic relations from dependency, and a label-aware GCN solution for simultaneously modeling the syntactic edges and labels are considered.
- Inheriting the Wisdom of Predecessors: A Multiplex Cascade Framework for Unified Aspect-based Sentiment Analysis66
This work presents a novel multiplex cascade framework for unified ABSA and enhances the ABSA performances on 28 subtasks (7×4 datasets) with big margins by integrating POS tag and syntactic dependency information for term boundary and pairing identification.
- A transition-based joint model for disease named entity recognition and normalization65
A transition‐based model to jointly perform disease named entity recognition and normalization is proposed, casting the output construction process into an incremental state transition process, learning sequences of transition actions globally, which correspond to joint structural outputs.
- Emoji-Based Sentiment Analysis Using Attention Networks64
This article investigates a sentiment analysis model based on bidirectional long short-term memory, and the model has two advantages compared with the existing work: it does not need feature engineering and utilizes the attention approach to model the impact of emojis on text.
- Joint models for extracting adverse drug events from biomedical text61
Experimental results on a standard ADE corpus show that the discrete joint model outperforms a state-of-the-art baseline pipeline significantly and when discrete features are replaced by neural features, the recall is further improved.
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- Recognizing irregular entities in biomedical text via deep neural networks49
A neural network model called Bi-LSTM-CRF that consists of bidirectional (Bi) long short-term memories (LSTMs) and conditional random fields (CRFs) to identify regular entities and the components of irregular entities and it is shown that the method of automatically combining the components is as effective as the methods of manually designing rules.
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- Mastering the Explicit Opinion-Role Interaction: Syntax-Aided Neural Transition System for Unified Opinion Role Labeling43
This work investigates a novel solution by revisiting the transition architecture, and augmenting it with a pointer network (PointNet), and proposes a unified dependency-opinion graph (UDOG), co-modeling the syntactic dependency structure and the partial opinion-role structure.
- Fine-grained depression analysis based on Chinese micro-blog reviews43
A neural model for joint depression degree and cause prediction is designed and compared with several widely-used neural models such as TextCNN, BiLSTM and BERT, which shows that neural models achieve promising results, but there is still room for improvement.
- Cross-Lingual Semantic Role Labeling With Model Transfer35
This article proposes an end-to-end SRL model that incorporates a variety of universal features and transfer methods, and finds that gold syntax features are much more crucial for cross-lingual SRL, compared with the automatically-generated ones.
- TOE: A Grid-Tagging Discontinuous NER Model Enhanced by Embedding Tag/Word Relations and More Fine-Grained Tags33
This paper designs a Tag Representation Embedding Module (TREM) to force the SOTA grid-tagging model to consider not only word-word relationships but also word-tag and tag-tag relationships, and adds two new symmetric tags to model more fine-grained word- word relationships and alleviate error propagation from tag prediction.
- Global Inference with Explicit Syntactic and Discourse Structures for Dialogue-Level Relation Extraction33
A novel dialogue-level mixed dependency graph (D2G) and an argument reasoning graph (ARG) for DiaRE with a global relation reasoning mechanism is investigated, which effectively solves the long-range dependence issue, and meanwhile gives explainable predictions.
- Multimodal Emotion-Cause Pair Extraction with Holistic Interaction and Label Constraint32
This work proposes a novel model that captures holistic interaction and label constraint (HiLo) features for the MECPE task and innovatively leverages emotion transition features as pivotal cues to enhance causal inference within conversations.
- Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction32
This work proposes a feature-task alignment to explicitly model the specific emotion-&cause-specific features and the shared interactive feature in between, and an inter- task alignment, in which the label distance between the ECPE and the combinations of EE&CE are learned to be narrowed for better label consistency.
- High quality information extraction and query-oriented summarization for automatic query-reply in social network29
Experimental results show that the research is effective in filtering the redundancy and noise of social network messages, the final query-reply results outperform other commonly used methods' results in both automatic evaluation and manual evaluation.
- Harnessing Holistic Discourse Features and Triadic Interaction for Sentiment Quadruple Extraction in Dialogues27
A novel framework that not only capitalizes on comprehensive discourse feature modeling, but also captures the intrinsic interaction for optimal quadruple extraction is introduced, and a novel triadic scorer is proposed, strengthening weak token relations within a quadruple, thereby enhancing the cohesion of the quadruple extraction.
- Automatic Detection of Hypoglycemic Events From the Electronic Health Record Notes of Diabetes Patients: Empirical Study25
A deep-learning–based natural language processing (NLP) system to automatically detect hypoglycemia events from EHR notes is developed and a classical machine learning model and state-of-the-art neural network models are built and evaluated.
- A Multi-Modality Deep Network for Cold-Start Recommendation25
A tightly-coupled deep network model is proposed for fusing heterogeneous modalities, to avoid tedious feature extraction in specific domains, and to enable two-way information propagation from both content and rating information.
- BioERP: biomedical heterogeneous network-based self-supervised representation learning approach for entity relationship predictions24
A self-supervised meta path detection mechanism is proposed to train a deep Transformer encoder model that can capture the global structure and semantic feature in BioHNs, and a biomedical entity mask learning strategy is designed to reflect local associations of vertices.
- An investigation of single-domain and multidomain medication and adverse drug event relation extraction from electronic health record notes using advanced deep learning models23
The authors' MLP or CapNet model generally outperforms other state-of-the-art systems in medication and adverse drug event relation extraction, and multidomain models perform better than single-domain models.
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- Generating Classical Chinese Poems from Vernacular Chinese22
This paper proposes a novel task of generating classical Chinese poems from vernacular, which allows users to have more control over the semantic of generated poems, and adapt the approach of unsupervised machine translation (UMT) to this task.
- Argus: Online Statistical Bug Detection22
This paper presents an online statistical bug detection tool called Argus, which constructs statistics at runtime using a sliding window over the program execution, is capable of detecting bugs in a single execution and can raise an alert at runtime when bug symptoms occur.
- Computed tomography-based deep-learning prediction of lymph node metastasis risk in locally advanced gastric cancer20
The novel and noninvasive deep learning approach could provide efficient and accurate prediction of lymph node metastasis in LAGC, and benefit clinical decision making of therapeutic strategy.
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- Classification and identification of unknown network protocols based on CNN and T-SNE20
Experimental results show this artificial intelligence method has high accuracy and robustness in identifying unknown network protocols and the protocol identification adaptability for big data was also greatly improved.
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- Mutual Disentanglement Learning for Joint Fine-Grained Sentiment Classification and Controllable Text Generation19
This paper combines FGSC and FGSG as a joint dual learning system, encouraging them to learn the advantages from each other, and proposes decoupling the feature representations in two tasks into fine-grained aspect-oriented opinion variables and content variables respectively, by performing mutual disentanglement learning upon them.
- Refining and Synthesis: A Simple yet Effective Data Augmentation Framework for Cross-Domain Aspect-based Sentiment Analysis17
A simple yet effective framework to address shortages in ABSA data augmentation, called Refining and Synthesis Data Augmentation (RSDA), which refines generated labeled data using a natu-ral language inference (NLI) filter to control data quality.
- TKDP: Threefold Knowledge-Enriched Deep Prompt Tuning for Few-Shot Named Entity Recognition17
This work proposes incorporating the deep prompt tuning framework with threefold knowledge (namely TKDP), including the internal 1) context knowledge and the external 2) label knowledge & 3) sememe knowledge, and presents a knowledge-enriched model that significantly outperforms 9 strong-performing baseline systems in 5-/10-/20-shot settings.
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- Multi goals and multi scenes visual mapless navigation in indoor using meta-learning and scene priors17
A multi goals and multi scenes visual mapless navigation model, which integrate meta learning with spatial relationships between different object categories is proposed, which improves the generalization on multi goals in multi scenes but also encourages effective navigation.
- A Graph Attention Model for Dictionary-Guided Named Entity Recognition17
This work introduces a dictionary-guided graph attention model that outperforms several state-of-the-art models with different methodologies, and is comparable with BioBERT that owns huge parameters and needs large-scale pre-training.
- Deep neural model with self-training for scientific keyphrase extraction17
A neural network based approach for scientific keyphrase extraction, which employs the bidirectional long short-memory (LSTM) to represent the sentences in the article and efficiently incorporates the unlabeled data and achieve competitive performance compared with previous state-of-the-art systems.
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- An Intelligent Method for Lead User Identification in Customer Collaborative Product Innovation16
This work contributes to user innovation literature by proposing a new way of identifying highly valuable lead users and offers a decision support for the efficient user management in CCPI.
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- Context vector-based visual mapless navigation in indoor using hierarchical semantic information and meta-learning14
A novel visual mapless navigation model is introduced, which integrates hierarchical semantic information represented by context vector with meta-learning to improve the generalization performance gap between known and unknown environments.
- Performance evaluations of large language models for customer service13
Experimental results show that the open-source LLMs can also perform better than the closed-source LLMs, and the fine-tuned LLM depends on the quality of the fine-tuning dataset rather than its size, and fine-tuning has great potential in customer service scenarios.
- Multi-task learning for Chinese clinical named entity recognition with external knowledge13
A novel multi-task deep neural network model for Chinese NER in the medical domain is presented that incorporates dictionary features into neural networks, and a general secondary named entity segmentation is used as auxiliary task to improve the performance of the primary task of named entity recognition.
- MMLSCU: A Dataset for Multi-modal Multi-domain Live Streaming Comment Understanding12
This work introduces the MMLSCU dataset, containing 50,129 intention-annotated comments across multiple modalities (text, images, vi-deos, audio) from eight streaming domains, and implements an end-to-end model to sequentially perform the following tasks: viewer comment intent detection, intent cause mining, viewer comment explanation and streamer policy suggestion.
- High Quality Microblog Extraction Based on Multiple Features Fusion and Time-Frequency Transformation12
This paper proposes an extraction framework to get high quality information by considering different features globally in social media in order to reduce computing time and improve extraction precision, and shows that the proposed framework is effective to extract high quality Information.
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- Revisiting Conversation Discourse for Dialogue Disentanglement10
A structure-aware framework is developed to integrate the rich structural features for better modeling the conversational semantic context and an easy-first decoding algorithm is devised, which performs utterance pairing under the easy-to-hard manner with a global context, breaking the constraint of traditional sequential decoding order.
- Deep Learning Radiomics Analysis of CT Imaging for Differentiating Between Crohn’s Disease and Intestinal Tuberculosis10
The arterial-venous combined deep learning radiomics model for differentiating between CD and ITB showed a high prediction quality and outperformed the handcrafted radiomics model in same phase images, and exhibited good performance in differentiating between CD and ITB.
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- A Raman spectroscopy algorithm based on convolutional neural networks and multilayer perceptrons: qualitative and quantitative analyses of chemical warfare agent simulants9
A qualitative and quantitative analysis algorithm of Raman spectroscopy based on deep learning (RS-MLP) with excellent accuracy and robustness in component identification and concentration identification in complex mixtures and provides a practical solution for rapid and non-contact detection of persistent chemicals in complex environments.
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- Uzbek news corpus for named entity recognition9
A corpus of Uzbek news articles containing manually annotated named entities sourced from Qalampir, an online news source in Uzbekistan, shows that the pre-trained model CINO outperformed other multilingual models.
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- Naranjo Question Answering using End-to-End Multi-task Learning Model9
This study presents the first attempt to automatically infer the causality between a drug and an ADR from electronic health records (EHRs) by answering the Naranjo questionnaire, the validated clinical question answering set used by domain experts for ADR causality assessment.
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- MindMap: Constructing Evidence Chains for Multi-Step Reasoning in Large Language Models8
This paper proposes a simple approach MindMap, which can be integrated with existing reasoning framework, such as Chain-of-Thought (CoT) and Selection-Inference (SI), by letting the model select relevant evidence chains instead of independent facts.
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- Bacteria Biotope Relation Extraction via Lexical Chains and Dependency Graphs8
A novel approach for dependency graph construction based on lexical chains, so one dependency graph can represent one or multiple sentences, and a neural network model which consists of the bidirectional long short-term memories and an attention graph convolution neural network to learn relation extraction features from the graph is proposed.
- Chinese Information Retrieval Using Lemur: NTCIR-5 CIR Experiments at UNT8
This paper discusses Chinese information retrieval using Lemur, a freely available information retrieval toolkit that can be used for both short-term and long-term information retrieval needs.
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- Ponzi scheme detection in smart contracts using the integration of deep learning and formal verification7
Experimental results demonstrate that the improved Conv1D model outperforms other existing models in terms of detection efficiency and accuracy while also meeting the requirements of formal security verification.
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- Relation classification via sequence features and bi-directional LSTMs7
A novel neural network architecture which is a bi-directional long-short-term-memory recurrent-neural- network (Bi-LSTM-RNN) model based on low-cost sequence features such as words and part-of-speech (POS) tags, to classify the relation of two entities is proposed.
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- M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction6
A Missing Modality Construction Module (MMCM) is designed to alleviate the issues caused by missing modalities and set a reasonable standard for subsequent research on multimodal IE, and an innovative hierarchical multimodal IE model is proposed.
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- True color 3D imaging optimization with missing spectral bands based on hyperspectral LiDAR6
Impaired spectral information obtained with HSL measurements may be influenced by various factors, that further impinge on the true color 3D imaging, and the reliability of improving color reconstruction effect with missing spectral bands is demonstrated.
- A Set of Experience-Based Smart Synergy Security Mechanism in Internet of Vehicles6
A novel security mechanism that uses the Set of Experience Knowledge Structure (SOEKS) and the synergy of security methods in different domains to provide the global optimal security strategy in Internet of Vehicles (IoV).
- A preliminary analysis of the use of resources in intelligent information access research6
An exploratory analysis of the use of resources in three Intelligent Information Access (IIA) research areas: Automatic Classification, Question Answering, and Cross-Language Information Retrieval.
- ECQED: Emotion-Cause Quadruple Extraction in Dialogs5
This paper presents a new task, Emotion-Cause Quadruple Extraction in Dialogs (ECQED), which requires detecting emotion-cause utterance pairs and emotion and cause types, and presents an ECQED model based on a structural and semantic heterogeneous graph as well as a parallel grid tagging scheme.
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- Text data mining of power based on natural language processing technology5
This paper first introduces the development of text mining technology, points out the difficulties, and designs the power text mining based on natural language processing technology, and puts forward the application effect and challenge of power textmining.
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- Investigating of Disease Name Normalization Using Neural Network and Pre-Training5
The CNN and Attention models are comparable in the task of disease name normalization while the recurrent neural network performs much worse, and character information and pre-training techniques are helpful for this task while sentence information hurts the performance.
- A Neural Joint Model for Extracting Bacteria and Their Locations5
A state-of-the-art neural joint model for relation extraction in the Automatic Content Extraction (ACE) task is adapted and a novel relation is suggested in the second step to detect the errors in the first step, thus this relation can correct some errors inThe first step.
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- Generating Attribute-Aware Human Motions from Textual Prompt4
A new framework inspired by Structural Causal Causal Models is proposed to decouple action semantics from human attributes, enabling text-to-semantics prediction and attribute-controlled generation of human motion patterns.
- CMNER: A Chinese Multimodal NER Dataset based on Social Media4
A Chinese Multimodal NER dataset is compiled utilizing data sourced from Weibo, China's largest social media platform, and the results substantiate the hypothesis that Chinese and English multimodal NER data can mutually enhance the performance of the NER model.
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- TransNav: spatial sequential transformer network for visual navigation4
This work proposes a Spatial Sequential Transformer Network (SSTNet) for learning informative visual representation in deep reinforcement learning (DRL) and evaluates the model on the AI2THOR framework to show that the method attains at least $10\% improvement of average success rate over most state-of-the-art models.
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- Modeling Web Services Composition with Transaction Extension for Performance Evaluation4
This paper adopts general stochastic high-level Petri net (GSHLPN) as basic formal description tool of WSC based on WS-BPEL and model the context of LRT to provide a more real environment for evaluating and optimizing the performance of W SC based onWS- BPEL.
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- Enhancing Chinese Event Extraction with Event Trigger Structures3
This article proposes to use Event Trigger Structure for Event Extraction (ETSEE), which can compensate the inconsistency between two structures, and designs a graph-based event extraction model that jointly identifies triggers and arguments, where the graph consists of both the dependency structure and ETSs.
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- Improving Cross-lingual Aspect-based Sentiment Analysis with Sememe Bridge2
This article develops an English-Chinese parallel dataset for ABSA using a semi-automatic process involving machine translation and a word aligner, and proposes a plug-and-play transfer method based on sememe knowledge that brings significant improvements for Chinese ABSA.
- BENTO: A Visual Platform for Building Clinical NLP Pipelines Based on CodaLab2
BENTO is a workflow management platform with a graphic user interface (GUI) that is built on top of CodaLab to facilitate the process of building clinical NLP pipelines and enables researchers with limited computer background to compose tools into NLP Piping pipelines.
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- Spatial interpolation of DEM using BP artificial neural networks2
This paper simulates one spatial interpolation case in the area where there are several discrete known levelling points using input vector using BP Artificial Neural Networks (BP ANNs) and shows that the new input vectors are usually applicable and better than the classic one.
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- TeleEval-OS: Performance evaluations of large language models for operations scheduling1
Experimental results demonstrate that open-source LLMs can outperform closed-source LLMs in specific scenarios, highlighting their significant potential and value in the field of telecommunications operation scheduling.
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- TeleEval-OS: Performance evaluations of large language models for telecommunications operations scheduling1
A systematic evaluation of 14 representative LLMs under zero-shot and few-shot settings shows that DeepSeek-V3 achieves the best performance on basic NLP and structured report generation tasks, while GPT-4o demonstrates superior capabilities in operational report analysis, highlighting the complementary strengths of LLMs across different task types.
- Exploration and Practice of Promoting High-Quality Development of Higher Education with Digitization1
- Hierarchical federated learning with mobile edge computing in the Internet of Vehicles1
The experimental results show that the three-layer federated learning system of edge car network edge server cloud server can reduce the training time and improve the accuracy of the model compared with the Federated learning without introducing the edge server.
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- Chinese Event Discourse Deixis Resolution: Design of the Dataset and Model1
A deep neural network model is proposed for EDDR, which formulates the task into two subtasks, namely event anaphor recognition and event antecedent recognition, and outperforms all the baselines and achieves about 53%, 44%, 53%, and 63% F1s using standard anaphora resolution metrics.
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- Multi-Scale Multi-Target Domain Adaptation for Angle Closure Classification1
A novel Multi-scale Multi-target Domain Adversarial Network (M2DAN) for angle closure classification is proposed to transfer a model trained on one labeled source domain to multiple unlabeled target domains to classify angle closure on multiple target domains even without any annotations in these domains.
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- Research Progress of Information Behavior in Information Science&Library Science Abroad1
The shows that Journal of The American Society For Information Science and Technology is a major journal for information behavior research; America and England is most active country; and the research focus includes information retrieval/seeking/search, different users information behavior, information behavior model and information needs.
- K-Means Optimization Algorithms of Initial Clustering Center Based on Regional Density1
The improved K-means algorithm is tested in two dimensions, and the results show that the improved algorithm obtains more optimized initial clustering center with higher accuracy of clustering.
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- CogMAEC'25: The 1st Workshop on Cognition-oriented Multimodal Affective and Empathetic Computing–
The 1st Workshop on Cognition-oriented Multimodal Affective and Empathetic Computing was held at ACM Multimedia 2025, exploring how machines can not only recognize emotions but also explain their causes and simulate human-like affective reasoning.
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- Preserving Modern Journal Web Sites: How Hard Can It Be?–
A digital preservationist’s perspective on modern journal websites is provided, and how new features and functionality impact preservability for digital publications is illustrated.
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- Sentiment Analysis of Chinese Agricultural News Based on the JST Model–
A dictionary of a positive and negative sentiment of agricultural news as prior knowledge is created and a sentiment classification model is constructed with a joint emotion-subject model (JST) and the dictionary to accurately analyze the sentiment of Chinese Agricultural News.
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- Feedback Ranking Method in Topic-Based Retrieval–
A novel Feedback Ranking method is proposed, which lets two basic rankers learn from each other during the mutual process by providing each one's result as feedback to the other so as to boost the ranking performance.
- New Exploration into the Word Semantic Generation Mechanism Based on Word Representation–
It is shown that with the help of IS inference, there is possibility to perfect metonymicWSGMs and add metaphoric WSGMs to the WR of GL, which may be the most elaborate WR nowadays.
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- An Adaptive Architecture of Service Component for Persuasive Computing–
This paper proposes a framework which focuses on thoroughly resolving functional dependency among components and enabling “Delay Adaptation” and introduces an “Adaptation Table” to describe components’ interfaces and their interactions.
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- Research on the Data Conversion between XML and Relational Database–
The relational storage of XML data and the mapping tactics between XML and relational database are discussed and the feasibility of the data conversion based on different mappings is analyzed.
- A Process Definition Model and its Verification Analysis–
It's very important to find a sound algorithm to verify the workflow structure correctness because the soundness problem is normally very complex and Graph reduction algorithm is a practicable one.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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