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Works369 from public data
- LEARNING BAYESIAN BELIEF NETWORKS: AN APPROACH BASED ON THE MDL PRINCIPLE903
A new approach for learning Bayesian belief networks from raw data is presented, based on Rissanen's minimal description length (MDL) principle, which can learn unrestricted multiply‐connected belief networks and allows for trade off accuracy and complexity in the learned model.
- A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges547
A new taxonomy for ABSA is provided which organizes existing studies from the axes of concerned sentiment elements, with an emphasis on recent advances of compound ABSA tasks.
- Transformation Networks for Target-Oriented Sentiment Classification510
A new model is proposed to overcome the drawbacks of attention mechanism and the obstacles that block CNN to perform well in this sentiment classification task, and employs a CNN layer to extract salient features from the transformed word representations originated from a bi-directional RNN layer.
- A Unified Model for Opinion Target Extraction and Target Sentiment Prediction360
This paper aims to solve the complete task of target-based sentiment analysis in an end-to-end fashion, and presents a novel unified model which applies a unified tagging scheme.
- Neural Rating Regression with Abstractive Tips Generation for Recommendation346
A deep learning based framework named NRT is proposed which can simultaneously predict precise ratings and generate abstractive tips with good linguistic quality simulating user experience and feelings.
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- Exploiting BERT for End-to-End Aspect-based Sentiment Analysis335
This work builds a series of simple yet insightful neural baselines to deal with E2E-ABSA, and shows that even with a simple linear classification layer, the BERT-based architecture can outperform state-of-the-art works.
- Aspect Sentiment Quad Prediction as Paraphrase Generation314
This work introduces the Aspect Sentiment Quad Prediction (ASQP) task, aiming to jointly detect all sentiment elements in quads for a given opinionated sentence, and proposes a novel Paraphrase modeling paradigm to cast the ASQP task to a paraphrase generation process.
- Towards Generative Aspect-Based Sentiment Analysis306
This paper proposes to tackle various ABSA tasks in a unified generative framework with two types of paradigms, namely annotation-style and extraction-style modeling, to enable the training process by formulating each ABSA task as a text generation problem.
- Aspect Term Extraction with History Attention and Selective Transformation290
Experimental results over four benchmark datasets clearly demonstrate that the new framework for tackling ATE can outperform all state-of-the-art methods.
- Deep Multi-Task Learning for Aspect Term Extraction with Memory Interaction260
A novel LSTM-based deep multi-task learning framework for aspect term extraction from user review sentences designed for jointly handling the extraction tasks of aspects and opinions via memory interactions is proposed.
- Graph Transformer for Graph-to-Sequence Learning257
A new model, known as Graph Transformer, is proposed that uses explicit relation encoding and allows direct communication between two distant nodes and provides a more efficient way for global graph structure modeling.
- Using a generalized instance set for automatic text categorization237
This work proposes a new technique known as the generalized instance set (GIS) algorithm by unifying the strengths of k-NN and linear classifiers and adapting to characteristics of text categorization problems.
- Deep Recurrent Generative Decoder for Abstractive Text Summarization218
A new framework for abstractive text summarization based on a sequence-to-sequence oriented encoder-decoder model equipped with a deep recurrent generative decoder (DRGN) achieves improvements over the state-of-the-art methods.
- Automatic text categorization and its application to text retrieval206
This work develops an automatic text categorization approach and investigates its application to text retrieval, demonstrating the effectiveness of the approach and demonstrating that the retrieval performance using automatic categorization achieves the same retrieval quality as the performance using manual categorization.
- A multilevel approach to intelligent information filtering199
A filtering model is proposed that decomposes the overall task into subsystem functionalities and highlights the need for multiple adaptation techniques to cope with uncertainties.
- Context-based generic cross-lingual retrieval of documents and automated summaries: Research Articles181
PRAW—a PRivAcy model for the Web suggested in this paper—is aimed at hiding users' navigation tracks to prevent eavesdroppers from inferring their profiles but still allowing them to be identified.
- Evaluation challenges in large-scale document summarization176
A large-scale meta evaluation of eight evaluation measures for both single-document and multi-document summarizers is presented, showing the strengths and draw-backs of all evaluation methods and how they rank the different summarizers.
- Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning169
A dynamic weighted graph based RL method to learn a policy to select the action at each conversation turn, either asking an attribute or recommending items, which significantly outperforms state-of-the-art methods but also enhances the scalability and stability of CRS.
- A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers162
This survey provides a comprehensive review of existing deep learning techniques for Relation extraction and proposes a new taxonomy to categorize existing works from three perspectives, i.e., text representation, context encoding, and triplet prediction.
- Abstractive Multi-Document Summarization via Phrase Selection and Merging161
This work proposes an abstraction-based multi-document summarization framework that can construct new sentences by exploring more fine-grained syntactic units than sentences, namely, noun/verb phrases, and achieves reasonably well results on manual linguistic quality evaluation.
- Abstractive Multi-Document Summarization via Phrase Selection and148
This work proposes an abstraction-based multi-document summarization framework that can construct new sentences by exploring more fine-grained syntactic units than sentences, namely, noun/verb phrases, and achieves reasonably well results on manual linguistic quality evaluation.
- News Sensitive Stock Trend Prediction147
This paper investigates the immediate impact of news articles on the time series based on the Efficient Markets Hypothesis and proposes an algorithm, which is called guided clustering, to filter news articles with the help of the clusters that have been obtained from trends.
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- A Theoretical Analysis of the Repetition Problem in Text Generation130
A new framework for theoretical analysis for the repetition problem is proposed and a novel rebalanced encoding approach is proposed to alleviate the high inflow problem and thus reducing the upper bound.
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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