Publications

Relation construction for aspect-level sentiment classification

Published in 2022

Aspect-level sentiment classification aims to obtain fine-grained sentiment polarities of different aspects in one sentence…

Recommended citation: Zeng J, **Liu T**, Jia W, et al. Relation construction for aspect-level sentiment classification[J]. Information Sciences, 2022, 586: 209-223. https://www.sciencedirect.com/science/article/pii/S0020025521012032/pdfft?md5=82435bdd06f0b06e0f3fdbd5f05232ce&pid=1-s2.0-S0020025521012032-main.pdf

Distantly Supervised Relation Extraction using Multi-Layer Revision Network and Confidence-based Multi-Instance Learning

Published in 2021

Distantly supervised relation extraction is widely used in the construction of knowledge bases due to its high efficiency…

Recommended citation: Lin X, **Liu T**, Jia W, et al. Distantly Supervised Relation Extraction using Multi-Layer Revision Network and Confidence-based Multi-Instance Learning[C]//Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021: 165-174. https://aclanthology.org/2021.emnlp-main.15.pdf

Fine-grained Question-Answer Sentiment Classification with Hierarchical Graph Attention Network

Published in 2021

User-oriented Question-Answer (QA) text pair plays an increasingly important role in online e-commerce platforms, and expresses sentiment information with complicated semantic relations, causing great challenges for accurate sentiment analysis…

Recommended citation: Zeng J, **Liu T**, Jia W, et al. Fine-grained Question-Answer sentiment classification with hierarchical graph attention network[J]. Neurocomputing, 2021, 457: 214-224. https://www.sciencedirect.com/science/article/pii/S0925231221009449/pdfft?md5=38db7c36bab4817be05d4e70c7aebdd4&pid=1-s2.0-S0925231221009449-main.pdf

Regularized Attentive Capsule Network for Overlapped Relation Extraction

Published in 2020

Distantly supervised relation extraction has been widely applied in knowledge base construction due to its less requirement of human efforts…

Recommended citation: **Liu T**, Lin X, Jia W, et al. Regularized Attentive Capsule Network for Overlapped Relation Extraction[C]//Proceedings of the 28th International Conference on Computational Linguistics. 2020: 6388-6398. https://aclanthology.org/2020.coling-main.562.pdf

Robust Neural Relation Extraction via Multi-Granularity Noises Reduction

Published in 2020

Distant supervision is widely used to extract relational facts with automatically labeled datasets to reduce high cost of human annotation…

Recommended citation: Zhang X, **Liu T**, Li P, et al. Robust neural relation extraction via multi-granularity noises reduction[J]. IEEE Transactions on Knowledge and Data Engineering, 2020, 33(9): 3297-3310. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8952645

Improving Abstractive Document Summarization with Salient Information Modeling

Published in 2019

Comprehensive document encoding and salient information selection are two major difficulties for generating summaries with adequate salient information…

Recommended citation: You Y, Jia W, **Liu T**, et al. Improving abstractive document summarization with salient information modeling[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019: 2132-2141. https://aclanthology.org/P19-1205.pdf

Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning

Published in 2018

Extracting relations is critical for knowledge base completion and construction in which distant supervised methods are widely used to extract relational facts automatically with the existing knowledge bases…

Recommended citation: **Liu T**, Zhang X, Zhou W, et al. Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018: 2195-2204. https://aclanthology.org/D18-1243.pdf