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  1. AU="Liu, Tingwen"
  2. AU="Cable, Jo"
  3. AU="Orsetta Zuffardi"
  4. AU="Brunner, David"
  5. AU="Monserrat, Nuria"
  6. AU="Dufresne, Philippe J"
  7. AU="Dickey, Erin M"
  8. AU="Alessia Nava"
  9. AU="Yamoah, Peter"
  10. AU="Solit, David"
  11. AU="Raymond, Benjamin"
  12. AU="Maddi, Abhiram"
  13. AU="Rodríguez, Johanna G"
  14. AU="Frans, J"
  15. AU="Elisa Palazzari"

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  1. Buch ; Online: Adaptive Data Augmentation for Aspect Sentiment Quad Prediction

    Zhang, Wenyuan / Zhang, Xinghua / Cui, Shiyao / Huang, Kun / Wang, Xuebin / Liu, Tingwen

    2024  

    Abstract: Aspect sentiment quad prediction (ASQP) aims to predict the quad sentiment elements for a given sentence, which is a critical task in the field of aspect-based sentiment analysis. However, the data imbalance issue has not received sufficient attention in ...

    Abstract Aspect sentiment quad prediction (ASQP) aims to predict the quad sentiment elements for a given sentence, which is a critical task in the field of aspect-based sentiment analysis. However, the data imbalance issue has not received sufficient attention in ASQP task. In this paper, we divide the issue into two-folds, quad-pattern imbalance and aspect-category imbalance, and propose an Adaptive Data Augmentation (ADA) framework to tackle the imbalance issue. Specifically, a data augmentation process with a condition function adaptively enhances the tail quad patterns and aspect categories, alleviating the data imbalance in ASQP. Following previous studies, we also further explore the generative framework for extracting complete quads by introducing the category prior knowledge and syntax-guided decoding target. Experimental results demonstrate that data augmentation for imbalance in ASQP task can improve the performance, and the proposed ADA method is superior to naive data oversampling.

    Comment: Accepted by ICASSP 2024, 5 pages
    Schlagwörter Computer Science - Computation and Language ; Computer Science - Artificial Intelligence
    Thema/Rubrik (Code) 400
    Erscheinungsdatum 2024-01-12
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  2. Buch ; Online: Contrastive Cross-Domain Sequential Recommendation

    Cao, Jiangxia / Cong, Xin / Sheng, Jiawei / Liu, Tingwen / Wang, Bin

    2023  

    Abstract: Cross-Domain Sequential Recommendation (CDSR) aims to predict future interactions based on user's historical sequential interactions from multiple domains. Generally, a key challenge of CDSR is how to mine precise cross-domain user preference based on ... ...

    Abstract Cross-Domain Sequential Recommendation (CDSR) aims to predict future interactions based on user's historical sequential interactions from multiple domains. Generally, a key challenge of CDSR is how to mine precise cross-domain user preference based on the intra-sequence and inter-sequence item interactions. Existing works first learn single-domain user preference only with intra-sequence item interactions, and then build a transferring module to obtain cross-domain user preference. However, such a pipeline and implicit solution can be severely limited by the bottleneck of the designed transferring module, and ignores to consider inter-sequence item relationships. In this paper, we propose C^2DSR to tackle the above problems to capture precise user preferences. The main idea is to simultaneously leverage the intra- and inter- sequence item relationships, and jointly learn the single- and cross- domain user preferences. Specifically, we first utilize a graph neural network to mine inter-sequence item collaborative relationship, and then exploit sequential attentive encoder to capture intra-sequence item sequential relationship. Based on them, we devise two different sequential training objectives to obtain user single-domain and cross-domain representations. Furthermore, we present a novel contrastive cross-domain infomax objective to enhance the correlation between single- and cross- domain user representations by maximizing their mutual information. To validate the effectiveness of C^2DSR, we first re-split four e-comerce datasets, and then conduct extensive experiments to demonstrate the effectiveness of our approach C^2DSR.

    Comment: This paper has been accepted by CIKM 2022
    Schlagwörter Computer Science - Information Retrieval ; Computer Science - Social and Information Networks
    Thema/Rubrik (Code) 005
    Erscheinungsdatum 2023-04-07
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  3. Artikel: Intraluminal Therapy for

    Liu, Ting-Wen / Chen, Yen-Po / Ho, Cheng-Yu / Chen, Ming-Jen / Wang, Horng-Yuan / Shih, Shou-Chuan / Liou, Tai-Cherng

    Biomedicines

    2023  Band 11, Heft 4

    Abstract: Helicobacter pylori (H. pylori) ...

    Abstract Helicobacter pylori (H. pylori)
    Sprache Englisch
    Erscheinungsdatum 2023-04-03
    Erscheinungsland Switzerland
    Dokumenttyp Journal Article
    ZDB-ID 2720867-9
    ISSN 2227-9059
    ISSN 2227-9059
    DOI 10.3390/biomedicines11041084
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  4. Artikel ; Online: PD‑1/PD‑L1 immune checkpoint inhibitors in neoadjuvant therapy for solid tumors (Review).

    Tang, Quanying / Zhao, Shikang / Zhou, Ning / He, Jinling / Zu, Lingling / Liu, Tingwen / Song, Zuoqing / Chen, Jun / Peng, Ling / Xu, Song

    International journal of oncology

    2023  Band 62, Heft 4

    Abstract: A comprehensive search regarding programmed cell death protein 1 (PD‑1)/programmed death‑ligand 1 (PD‑L1) inhibitor monotherapy or combination therapy in neoadjuvant settings of 11 types of solid cancer was performed using the PubMed, Cochrane and Embase ...

    Abstract A comprehensive search regarding programmed cell death protein 1 (PD‑1)/programmed death‑ligand 1 (PD‑L1) inhibitor monotherapy or combination therapy in neoadjuvant settings of 11 types of solid cancer was performed using the PubMed, Cochrane and Embase databases, and the abstracts of various conferences were screened. Data presented in 99 clinical trials indicated that preoperative treatment with PD‑1/PD‑L1 combined therapy, particularly immunotherapy plus chemotherapy, could achieve a higher objective response rate, a higher major pathologic response rate and a higher pathologic complete response rate, as well as a lower number of immune‑related adverse events compared with PD‑1/PD‑L1 monotherapy or dual immunotherapy. Although PD‑1/PD‑L1 inhibitor combination caused more treatment‑related adverse events (TRAEs) in patients, most of the TRAEs were acceptable and did not cause marked delays in operation. The data suggest that patients with pathological remission after neoadjuvant immunotherapy exhibit improved postoperative disease‑free survival compared with those without pathological remission. Further studies are still required to evaluate the long‑term survival benefit of neoadjuvant immunotherapy.
    Mesh-Begriff(e) Humans ; B7-H1 Antigen ; Immune Checkpoint Inhibitors/pharmacology ; Immune Checkpoint Inhibitors/therapeutic use ; Neoadjuvant Therapy ; Neoplasms/drug therapy ; Programmed Cell Death 1 Receptor
    Chemische Substanzen B7-H1 Antigen ; Immune Checkpoint Inhibitors ; Programmed Cell Death 1 Receptor
    Sprache Englisch
    Erscheinungsdatum 2023-03-03
    Erscheinungsland Greece
    Dokumenttyp Journal Article ; Review
    ZDB-ID 1154403-x
    ISSN 1791-2423 ; 1019-6439
    ISSN (online) 1791-2423
    ISSN 1019-6439
    DOI 10.3892/ijo.2023.5497
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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    Verfügbar in ZB MED Köln/Königswinter

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    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
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  5. Buch ; Online: Event Causality Extraction with Event Argument Correlations

    Cui, Shiyao / Sheng, Jiawei / Cong, Xin / Li, QuanGang / Liu, Tingwen / Shi, Jinqiao

    2023  

    Abstract: Event Causality Identification (ECI), which aims to detect whether a causality relation exists between two given textual events, is an important task for event causality understanding. However, the ECI task ignores crucial event structure and cause- ... ...

    Abstract Event Causality Identification (ECI), which aims to detect whether a causality relation exists between two given textual events, is an important task for event causality understanding. However, the ECI task ignores crucial event structure and cause-effect causality component information, making it struggle for downstream applications. In this paper, we explore a novel task, namely Event Causality Extraction (ECE), aiming to extract the cause-effect event causality pairs with their structured event information from plain texts. The ECE task is more challenging since each event can contain multiple event arguments, posing fine-grained correlations between events to decide the causeeffect event pair. Hence, we propose a method with a dual grid tagging scheme to capture the intra- and inter-event argument correlations for ECE. Further, we devise a event type-enhanced model architecture to realize the dual grid tagging scheme. Experiments demonstrate the effectiveness of our method, and extensive analyses point out several future directions for ECE.

    Comment: Accepted to COLING2022
    Schlagwörter Computer Science - Computation and Language
    Thema/Rubrik (Code) 380
    Erscheinungsdatum 2023-01-27
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  6. Buch ; Online: Cross-Domain Recommendation to Cold-Start Users via Variational Information Bottleneck

    Cao, Jiangxia / Sheng, Jiawei / Cong, Xin / Liu, Tingwen / Wang, Bin

    2022  

    Abstract: Recommender systems have been widely deployed in many real-world applications, but usually suffer from the long-standing user cold-start problem. As a promising way, Cross-Domain Recommendation (CDR) has attracted a surge of interest, which aims to ... ...

    Abstract Recommender systems have been widely deployed in many real-world applications, but usually suffer from the long-standing user cold-start problem. As a promising way, Cross-Domain Recommendation (CDR) has attracted a surge of interest, which aims to transfer the user preferences observed in the source domain to make recommendations in the target domain. Previous CDR approaches mostly achieve the goal by following the Embedding and Mapping (EMCDR) idea which attempts to learn a mapping function to transfer the pre-trained user representations (embeddings) from the source domain into the target domain. However, they pre-train the user/item representations independently for each domain, ignoring to consider both domain interactions simultaneously. Therefore, the biased pre-trained representations inevitably involve the domain-specific information which may lead to negative impact to transfer information across domains. In this work, we consider a key point of the CDR task: what information needs to be shared across domains? To achieve the above idea, this paper utilizes the information bottleneck (IB) principle, and proposes a novel approach termed as CDRIB to enforce the representations encoding the domain-shared information. To derive the unbiased representations, we devise two IB regularizers to model the cross-domain/in-domain user-item interactions simultaneously and thereby CDRIB could consider both domain interactions jointly for de-biasing.

    Comment: This paper has been accepted by ICDE 2022
    Schlagwörter Computer Science - Information Retrieval ; Computer Science - Social and Information Networks
    Thema/Rubrik (Code) 005
    Erscheinungsdatum 2022-03-31
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  7. Buch ; Online: Enhancing Multimodal Entity and Relation Extraction with Variational Information Bottleneck

    Cui, Shiyao / Cao, Jiangxia / Cong, Xin / Sheng, Jiawei / Li, Quangang / Liu, Tingwen / Shi, Jinqiao

    2023  

    Abstract: This paper studies the multimodal named entity recognition (MNER) and multimodal relation extraction (MRE), which are important for multimedia social platform analysis. The core of MNER and MRE lies in incorporating evident visual information to enhance ... ...

    Abstract This paper studies the multimodal named entity recognition (MNER) and multimodal relation extraction (MRE), which are important for multimedia social platform analysis. The core of MNER and MRE lies in incorporating evident visual information to enhance textual semantics, where two issues inherently demand investigations. The first issue is modality-noise, where the task-irrelevant information in each modality may be noises misleading the task prediction. The second issue is modality-gap, where representations from different modalities are inconsistent, preventing from building the semantic alignment between the text and image. To address these issues, we propose a novel method for MNER and MRE by Multi-Modal representation learning with Information Bottleneck (MMIB). For the first issue, a refinement-regularizer probes the information-bottleneck principle to balance the predictive evidence and noisy information, yielding expressive representations for prediction. For the second issue, an alignment-regularizer is proposed, where a mutual information-based item works in a contrastive manner to regularize the consistent text-image representations. To our best knowledge, we are the first to explore variational IB estimation for MNER and MRE. Experiments show that MMIB achieves the state-of-the-art performances on three public benchmarks.
    Schlagwörter Computer Science - Multimedia ; Computer Science - Computation and Language
    Thema/Rubrik (Code) 004
    Erscheinungsdatum 2023-04-05
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  8. Buch ; Online: FFT

    Cui, Shiyao / Zhang, Zhenyu / Chen, Yilong / Zhang, Wenyuan / Liu, Tianyun / Wang, Siqi / Liu, Tingwen

    Towards Harmlessness Evaluation and Analysis for LLMs with Factuality, Fairness, Toxicity

    2023  

    Abstract: The widespread of generative artificial intelligence has heightened concerns about the potential harms posed by AI-generated texts, primarily stemming from factoid, unfair, and toxic content. Previous researchers have invested much effort in assessing ... ...

    Abstract The widespread of generative artificial intelligence has heightened concerns about the potential harms posed by AI-generated texts, primarily stemming from factoid, unfair, and toxic content. Previous researchers have invested much effort in assessing the harmlessness of generative language models. However, existing benchmarks are struggling in the era of large language models (LLMs), due to the stronger language generation and instruction following capabilities, as well as wider applications. In this paper, we propose FFT, a new benchmark with 2116 elaborated-designed instances, for LLM harmlessness evaluation with factuality, fairness, and toxicity. To investigate the potential harms of LLMs, we evaluate 9 representative LLMs covering various parameter scales, training stages, and creators. Experiments show that the harmlessness of LLMs is still under-satisfactory, and extensive analysis derives some insightful findings that could inspire future research for harmless LLM research.

    Comment: Work in progress
    Schlagwörter Computer Science - Computation and Language ; Computer Science - Cryptography and Security
    Erscheinungsdatum 2023-11-30
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  9. Buch ; Online: ID-MixGCL

    Zhang, Gehang / Yu, Bowen / Cao, Jiangxia / Zhang, Xinghua / Sheng, Jiawei / Zhou, Chuan / Liu, Tingwen

    Identity Mixup for Graph Contrastive Learning

    2023  

    Abstract: Graph contrastive learning (GCL) has recently achieved substantial advancements. Existing GCL approaches compare two different ``views'' of the same graph in order to learn node/graph representations. The underlying assumption of these studies is that ... ...

    Abstract Graph contrastive learning (GCL) has recently achieved substantial advancements. Existing GCL approaches compare two different ``views'' of the same graph in order to learn node/graph representations. The underlying assumption of these studies is that the graph augmentation strategy is capable of generating several different graph views such that the graph views are structurally different but semantically similar to the original graphs, and thus the ground-truth labels of the original and augmented graph/nodes can be regarded identical in contrastive learning. However, we observe that this assumption does not always hold. For instance, the deletion of a super-node within a social network can exert a substantial influence on the partitioning of communities for other nodes. Similarly, any perturbation to nodes or edges in a molecular graph will change the labels of the graph. Therefore, we believe that augmenting the graph, accompanied by an adaptation of the labels used for the contrastive loss, will facilitate the encoder to learn a better representation. Based on this idea, we propose ID-MixGCL, which allows the simultaneous interpolation of input nodes and corresponding identity labels to obtain soft-confidence samples, with a controllable degree of change, leading to the capture of fine-grained representations from self-supervised training on unlabeled graphs. Experimental results demonstrate that ID-MixGCL improves performance on graph classification and node classification tasks, as demonstrated by significant improvements on the Cora, IMDB-B, IMDB-M, and PROTEINS datasets compared to state-of-the-art techniques, by 3-29% absolute points.

    Comment: 10 pages, 7 figures, accepted by IEEE BigData 2023
    Schlagwörter Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2023-04-19
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  10. Artikel ; Online: Strong Baselines for Author Name Disambiguation with and Without Neural Networks

    Zhang, Zhenyu / Yu, Bowen / Liu, Tingwen / Wang, Dong

    Advances in Knowledge Discovery and Data Mining

    Abstract: Author name disambiguation (AND) is one of the most vital problems in scientometrics, which has become a great challenge with the rapid growth of academic digital libraries. Existing approaches for this task substantially rely on complex clustering-like ... ...

    Abstract Author name disambiguation (AND) is one of the most vital problems in scientometrics, which has become a great challenge with the rapid growth of academic digital libraries. Existing approaches for this task substantially rely on complex clustering-like architectures, and they usually assume the number of clusters is known beforehand or predict the number by applying another model, which involve increasingly complex and time-consuming architectures. In this paper, we combine simple neural networks with two sets of heuristic rules to explore strong baselines for the author name disambiguation problem without any priori knowledge or estimation about cluster size, which frees the model from unnecessary complexity. On a popular benchmark dataset AMiner, our solution significantly outperforms several state-of-the-art methods both in performance and efficiency, and it still achieves comparable performance with many complex models when only using a group of rules. Experimental results also indicate that gains from sophisticated deep learning techniques are quite modest in the author name disambiguation problem.
    Schlagwörter covid19
    Verlag PMC
    Dokumenttyp Artikel ; Online
    DOI 10.1007/978-3-030-47426-3_29
    Datenquelle COVID19

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