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Works320 from public data
- Knowing What, How and Why: A Near Complete Solution for Aspect-Based Sentiment Analysis531
This paper introduces a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE), and proposes a two-stage framework to address this task, which outperforms a few strong baselines adapted from state-of-the-art related methods.
- KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction518
A Knowledge-aware Prompt-tuning approach with synergistic optimization (KnowPrompt) that injects latent knowledge contained in relation labels into prompt construction with learnable virtual type words and answer words.
- An automatic weighting scheme for collaborative filtering371
An optimization algorithm to automatically compute the weights for different items based on their ratings from training users will create a clustered distribution for user vectors in the item space by bringing users of similar interests closer and separating users of different interests more distant.
- Flexible mixture model for collaborative filtering356
FMM extends existing partitioning/clustering algorithms for collaborative filtering by clustering both users and items together simultaneously without assuming that each user and item should only belong to a single cluster.
- A statistical model for scientific readability293
Experiments show that this new method of using statistical models to estimate readability has a better performance than the widely used Flesch-Kincaid readability formula.
- Relevant document distribution estimation method for resource selection284
It is shown that the CORI algorithm does not do well in environments with a mix of "small" and "very large" databases, and a new resource selection algorithm is proposed that uses information about database sizes as well as database contents.
- CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark262
The first Chinese Biomedical Language Understanding Evaluation Evaluation (CBLUE) benchmark is presented: a collection of natural language understanding tasks including named entity recognition, information extraction, clinical diagnosis normalization, single-sentence/sentence-pair classification, and an associated online platform for model evaluation, comparison, and analysis.
- Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion260
This paper proposes a hybrid transformer architecture with unified input-output for diverse multimodal knowledge graph completion tasks, and proposes multi-level fusion, which integrates visual and text representation via coarse-grained prefix-guided interaction and fine- grained correlation-aware fusion modules.
- On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation260
It is demonstrated that 1) adapter-based tuning outperforms fine-tuning on low-resource and cross-lingual tasks; 2) it is more robust to overfitting and less sensitive to changes in learning rates.
- Expertise Retrieval260
This survey highlights advances in models and algorithms relevant to expertise retrieval as an emerging subdiscipline in information retrieval and draws connections among methods proposed in the literature and summarizes them in five groups of basic approaches.
- Document-level Relation Extraction as Semantic Segmentation236
A Document U-shaped Network for document-level relation extraction is proposed, which leverages an encoder module to capture the context information of entities and a U- shaped segmentation module over the image-style feature map to capture global interdependency among triples.
- Composite hashing with multiple information sources225
The focus of the new research problem is to design an algorithm for incorporating the features from different information sources into the binary hashing codes efficiently and effectively, and to propose an algorithm CHMIS-AW (CHMIS with Adjusted Weights) for learning the codes.
- HERCULE: attack story reconstruction via community discovery on correlated log graph190
HerCULE is an automated multi-stage log-based intrusion analysis system inspired by graph analytics research in social network analysis that can reconstruct attack behaviors from a spectrum of cyber attacks that involve multiple stages with high accuracy and low false positive rates.
- GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-supervised Learning and Explicit Policy Injection187
GALAXY is a novel pre-trained dialog model that explicitly learns dialog policy from limited labeled dialogs and large-scale unlabeled dialog corpora via semi-supervised learning and has a stronger few-shot ability than existing models under various low-resource settings.
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- Rumor Detection by Exploiting User Credibility Information, Attention and Multi-task Learning167
A new multi-task learning approach for rumor detection and stance classification tasks with a shared layer and two task specific layers that outperforms the state-of-the-art rumor detection approaches.
- A language modeling framework for resource selection and results merging165
This paper extends the language modeling approach to integrate resource selection, ad-hoc searching, and merging of results from different text databases into a single probabilistic retrieval model, designed primarily for Intranet environments.
- Perceive Your Users in Depth158
This work proposes to learn universal user representations across multiple tasks for more effective personalization and refers this work as Deep User Perception Network (DUPN) and conducts an extensive set of offline and online experiments.
- Federated Search157
The goal of this work is to provide a comprehensive summary of the previous research on the federated search challenges described above, to provide a comprehensive summary of the previous research on the result merging problem.
- RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction155
This work unify language model prompts and structured text approaches to design a structured prompt template for generating synthetic relation samples when conditioning on relation label prompts (RelationPrompt), and designs a novel Triplet Search Decoding method.
- StructuralLM: Structural Pre-training for Form Understanding153
This paper proposes a new pre-training approach, StructuralLM, to jointly leverage cell and layout information from scanned documents, with two new designs to make the most of the interactions of cell and layouts information.
- Effective automatic image annotation via a coherent language model and active learning153
A coherent language model for automatic image annotation is proposed that takes into account the word-to-word correlation by estimating a coherent language models for an image to significantly reduce the required number of annotated image examples.
- A semisupervised learning method to merge search engine results153
This article presents a semisupervised learning solution to the result merging problem and demonstrates that this method is more effective than the well-known CORI result-merging algorithm under a variety of conditions.
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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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