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Bi Chen

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3 papers

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EAAI Journal 2025 Journal Article

Reading comprehension powered semantic fusion network for identification of N-ary drug combinations

Hua Zhang
Peiqian Zhan
Cheng Yang
Yongjian Yan
Zijing Cai
Guogen Shan
Bo Jiang
Bi Chen

The concurrent use of multiple medications to treat one or more diseases is prevalent. Identifying N-ary drug combinations from biomedical texts aids in uncovering significant pharmacological effects triggered by drug-drug interactions. Previous methods for this emerging task have primarily concentrated on representing drug entities using pre-trained language models, overlooking the comprehensive extraction of contextual and task-specific semantic information. To address these limitations, we develop a semantic fusion method grounded in machine reading comprehension (MRC) framework. Our model, termed Reading Comprehension powered semantic Fusion network for Identification of N-ary Drug combinations (RCFIND), first constructs relevant contexts and queries for each individual drug combination. Then, diverse information sources, including task-specific semantics, drug entity representations and contextual details, are fused by using a simplified Capsule network as well as incorporating contrastive learning. We assess RCFIND, achieving F1 scores ranging from 72. 0% to 83. 3% across four types of evaluations. Experimental results demonstrate significant performance enhancements over existing baselines, with at least a 5% F1 score improvement. Ablation studies and further analysis confirm the efficacy of the MRC framework and contrastive learning in accurately identifying N-ary drug combinations.

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EAAI Journal 2024 Journal Article

Query-induced multi-task decomposition and enhanced learning for aspect-based sentiment quadruple prediction

Hua Zhang
Xiawen Song
Xiaohui Jia
Cheng Yang
Zeqi Chen
Bi Chen
Bo Jiang
Ye Wang

A complete sentiment analysis of product and service reviews has attracted growing concerns from merchants to enhance personalized marketing activities. Aspect sentiment quadruple prediction (ASQP) is a demanding and challenging task with the objective to predict four sentiment elements from given reviews. Existing methods for ASQP face certain issues, with pipeline-based non-generative approaches prone to error propagation and generative models at the potential risk of producing unexpected outputs or longer inference times. To avoid these shortcomings, we develop a novel end-to-end non-generative model for ASQP involving multi-task decomposition within machine reading comprehension (MRC) framework. Specifically, the ASQP task is decomposed into six query-induced subtasks by introducing task-specific question templates. The proposed model, named MRC-CLRI, is trained with multi-task joint learning. It also incorporates contrastive learning for category identification and sentiment classification to enhance the correlation of the six subtasks. To further promote the quadruple prediction, we present a refined inference algorithm in a bidirectional multi-turn inference procedure to effectively match aspect and opinion terms and optimize two inference hyperparameters: distance threshold and probability threshold. Experimental results exhibit superior performance compared to existing two non-generative and seven generative baselines. Our proposed MRC-CLRI, as a novel non-generative model, outperforms the best existing generative method by an average F1 score improvement of 1. 69% and the best previous non-generative method by an average F1 score improvement of 15. 77% across four review datasets. Ablation experiments further validate the efficacy of the designed contrastive learning and the refined inference algorithm.

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AAAI Conference 2010 Conference Paper

What Is an Opinion About? Exploring Political Standpoints Using Opinion Scoring Model

Bi Chen
Leilei Zhu
Daniel Kifer
Dongwon Lee

In this paper, we propose a generative model to automatically discover the hidden associations between topics words and opinion words. By applying those discovered hidden associations, we construct the opinion scoring models to extract statements which best express opinionists’ standpoints on certain topics. For experiments, we apply our model to the political area. First, we visualize the similarities and dissimilarities between Republican and Democratic senators with respect to various topics. Second, we compare the performance of the opinion scoring models with 14 kinds of methods to find the best ones. We find that sentences extracted by our opinion scoring models can effectively express opinionists’ standpoints.

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