Graph Neural Collaborative Topic Model for Citation Recommendation

2022 ◽  
Vol 40 (3) ◽  
pp. 1-30
Author(s):  
Qianqian Xie ◽  
Yutao Zhu ◽  
Jimin Huang ◽  
Pan Du ◽  
Jian-Yun Nie
Keyword(s):  
Topic Model ◽  
Topic Models ◽  
Joint Modeling ◽  
Research Problem ◽  
High Order ◽  
Semantic Structure ◽  
Critical Research ◽  
First Order ◽  
Latent Topic ◽  
Graph Neural Networks

Due to the overload of published scientific articles, citation recommendation has long been a critical research problem for automatically recommending the most relevant citations of given articles. Relational topic models (RTMs) have shown promise on citation prediction via joint modeling of document contents and citations. However, existing RTMs can only capture pairwise or direct (first-order) citation relationships among documents. The indirect (high-order) citation links have been explored in graph neural network–based methods, but these methods suffer from the well-known explainability problem. In this article, we propose a model called Graph Neural Collaborative Topic Model that takes advantage of both relational topic models and graph neural networks to capture high-order citation relationships and to have higher explainability due to the latent topic semantic structure. Experiments on three real-world citation datasets show that our model outperforms several competitive baseline methods on citation recommendation. In addition, we show that our approach can learn better topics than the existing approaches. The recommendation results can be well explained by the underlying topics.

scholarly journals Mining heterogeneous clinical notes by multi-modal latent topic model

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249622
Author(s):  
Zhi Wen ◽  
Pratheeksha Nair ◽  
Chih-Ying Deng ◽  
Xing Han Lu ◽  
Edward Moseley ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Topic Model ◽  
Prolonged Mechanical Ventilation ◽  
Topic Models ◽  
Clinical Notes ◽  
Topic Distribution ◽  
Domain Expertise ◽  
Latent Topic ◽  
Poor Outcomes ◽  
Mimic Iii

Latent knowledge can be extracted from the electronic notes that are recorded during patient encounters with the health system. Using these clinical notes to decipher a patient’s underlying comorbidites, symptom burdens, and treatment courses is an ongoing challenge. Latent topic model as an efficient Bayesian method can be used to model each patient’s clinical notes as “documents” and the words in the notes as “tokens”. However, standard latent topic models assume that all of the notes follow the same topic distribution, regardless of the type of note or the domain expertise of the author (such as doctors or nurses). We propose a novel application of latent topic modeling, using multi-note topic model (MNTM) to jointly infer distinct topic distributions of notes of different types. We applied our model to clinical notes from the MIMIC-III dataset to infer distinct topic distributions over the physician and nursing note types. Based on manual assessments made by clinicians, we observed a significant improvement in topic interpretability using MNTM modeling over the baseline single-note topic models that ignore the note types. Moreover, our MNTM model led to a significantly higher prediction accuracy for prolonged mechanical ventilation and mortality using only the first 48 hours of patient data. By correlating the patients’ topic mixture with hospital mortality and prolonged mechanical ventilation, we identified several diagnostic topics that are associated with poor outcomes. Because of its elegant and intuitive formation, we envision a broad application of our approach in mining multi-modality text-based healthcare information that goes beyond clinical notes. Code available at https://github.com/li-lab-mcgill/heterogeneous_ehr.

scholarly journals Semi-supervised Max-margin Topic Model with Manifold Posterior Regularization

2017 ◽  
Author(s):  
Wenbo Hu ◽  
Jun Zhu ◽  
Hang Su ◽  
Jingwei Zhuo ◽  
Bo Zhang
Keyword(s):  
Supervised Learning ◽  
Topic Model ◽  
Topic Models ◽  
Stochastic Gradient ◽  
Mcmc Method ◽  
Tight Coupling ◽  
Label Information ◽  
Latent Topic ◽  
Latent Topics ◽  
Qualitative Performance

Supervised topic models leverage label information to learn discriminative latent topic representations. As collecting a fully labeled dataset is often time-consuming, semi-supervised learning is of high interest. In this paper, we present an effective semi-supervised max-margin topic model by naturally introducing manifold posterior regularization to a regularized Bayesian topic model, named LapMedLDA. The model jointly learns latent topics and a related classifier with only a small fraction of labeled documents. To perform the approximate inference, we derive an efficient stochastic gradient MCMC method. Unlike the previous semi-supervised topic models, our model adopts a tight coupling between the generative topic model and the discriminative classifier. Extensive experiments demonstrate that such tight coupling brings significant benefits in quantitative and qualitative performance.

scholarly journals Efficient Graph Collaborative Filtering via Contrastive Learning

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4666
Author(s):  
Zhiqiang Pan ◽  
Honghui Chen
Keyword(s):  
Collaborative Filtering ◽  
State Of The Art ◽  
Representation Learning ◽  
First Order ◽  
Satisfactory Performance ◽  
Potential Applications ◽  
Benchmark Datasets ◽  
Bayesian Personalized Ranking ◽  
Graph Neural Networks ◽  
Training Efficiency

Collaborative filtering (CF) aims to make recommendations for users by detecting user’s preference from the historical user–item interactions. Existing graph neural networks (GNN) based methods achieve satisfactory performance by exploiting the high-order connectivity between users and items, however they suffer from the poor training efficiency problem and easily introduce bias for information propagation. Moreover, the widely applied Bayesian personalized ranking (BPR) loss is insufficient to provide supervision signals for training due to the extremely sparse observed interactions. To deal with the above issues, we propose the Efficient Graph Collaborative Filtering (EGCF) method. Specifically, EGCF adopts merely one-layer graph convolution to model the collaborative signal for users and items from the first-order neighbors in the user–item interactions. Moreover, we introduce contrastive learning to enhance the representation learning of users and items by deriving the self-supervisions, which is jointly trained with the supervised learning. Extensive experiments are conducted on two benchmark datasets, i.e., Yelp2018 and Amazon-book, and the experimental results demonstrate that EGCF can achieve the state-of-the-art performance in terms of Recall and normalized discounted cumulative gain (NDCG), especially on ranking the target items at right positions. In addition, EGCF shows obvious advantages in the training efficiency compared with the competitive baselines, making it practicable for potential applications.

Latent Topic Model for Indexing Arabic Documents

2014 ◽  
Vol 4 (1) ◽  
pp. 29-45 ◽  
Author(s):  
Rami Ayadi ◽  
Mohsen Maraoui ◽  
Mounir Zrigui
Keyword(s):  
Topic Model ◽  
Inflectional Morphology ◽  
Arabic Text ◽  
Text Representation ◽  
Text Documents ◽  
Latent Topic ◽  
Latent Topics ◽  
F Measure

In this paper, the authors present latent topic model to index and represent the Arabic text documents reflecting more semantics. Text representation in a language with high inflectional morphology such as Arabic is not a trivial task and requires some special treatments. The authors describe our approach for analyzing and preprocessing Arabic text then we describe the stemming process. Finally, the latent model (LDA) is adapted to extract Arabic latent topics, the authors extracted significant topics of all texts, each theme is described by a particular distribution of descriptors then each text is represented on the vectors of these topics. The experiment of classification is conducted on in house corpus; latent topics are learned with LDA for different topic numbers K (25, 50, 75, and 100) then the authors compare this result with classification in the full words space. The results show that performances, in terms of precision, recall and f-measure, of classification in the reduced topics space outperform classification in full words space and when using LSI reduction.

Author–Subject–Topic model for reviewer recommendation

2018 ◽  
Vol 45 (4) ◽  
pp. 554-570 ◽  
Author(s):  
Jian Jin ◽  
Qian Geng ◽  
Haikun Mou ◽  
Chong Chen
Keyword(s):  
Information System ◽  
Topic Model ◽  
Academic Library ◽  
Topic Models ◽  
Interdisciplinary Studies ◽  
Distribution Analysis ◽  
Topic Distribution ◽  
Research Domains

Interdisciplinary studies are becoming increasingly popular, and research domains of many experts are becoming diverse. This phenomenon brings difficulty in recommending experts to review interdisciplinary submissions. In this study, an Author–Subject–Topic (AST) model is proposed with two versions. In the model, reviewers’ subject information is embedded to analyse topic distributions of submissions and reviewers’ publications. The major difference between the AST and Author–Topic models lies in the introduction of a ‘Subject’ layer, which supervises the generation of hierarchical topics and allows sharing of subjects among authors. To evaluate the performance of the AST model, papers in Information System and Management (a typical interdisciplinary domain) in a famous Chinese academic library are investigated. Comparative experiments are conducted, which show the effectiveness of the AST model in topic distribution analysis and reviewer recommendation for interdisciplinary studies.

Context-Based Query Using Dependency Structures Based on Latent Topic Model

2013 ◽  
Vol 3 (3) ◽  
pp. 157-168
Author(s):  
Masato Shirai ◽  
Takashi Yanagisawa ◽  
Takao Miura
Keyword(s):  
Topic Model ◽  
Latent Topic ◽  
Dependency Structures

scholarly journals IDENTIFICATION OF ARGUMENTATIVE RELATIONS IN POPULAR SCIENCE TEXTS

2020 ◽  
Author(s):  
Natalia Vasilievna Salomatina ◽  
◽  
Irina Semenovna Kononenko ◽  
Elena Anatolvna Sidorova ◽  
Ivan Sergeevich Pimenov ◽  
...  
Keyword(s):  
Topic Model ◽  
Topic Models ◽  
Popular Science ◽  
Automatic Recognition ◽  
Scan Statistics ◽  
Science Texts

The presented work describes the analysis of argumentative statements included into the same text topic fragment as a recognition feature in terms of its efficiency. This study is performed with the purpose of using this feature in automatic recognition of argumentative structures presented in the popular science texts written in Russian. The topic model of a text is constructed based on superphrasal units (text fragments united by one topic) that are identified by detecting clusters of words and word-combinations with the use of scan statistics. Potential relations, extracted from topic models, are verified through the use of texts with manually annotated argumentation structures. The comparison between potential (based on topic models) and manually constructed relations is performed automatically. Macro-average scores of precision and recall are equal to 48.6% and 76.2% correspondingly.

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