Aiming at the problem that existing models in aspect-level sentiment analysis cannot fully and effectively utilize sentence semantic and syntactic structure information, this paper proposes a graph neural network-base...Aiming at the problem that existing models in aspect-level sentiment analysis cannot fully and effectively utilize sentence semantic and syntactic structure information, this paper proposes a graph neural network-based aspect-level sentiment classification model. Self-attention, aspectual word multi-head attention and dependent syntactic relations are fused and the node representations are enhanced with graph convolutional networks to enable the model to fully learn the global semantic and syntactic structural information of sentences. Experimental results show that the model performs well on three public benchmark datasets Rest14, Lap14, and Twitter, improving the accuracy of sentiment classification.展开更多
Many datasets in E-commerce have rich information about items and users who purchase or rate them. This information can enable advanced machine learning algorithms to extract and assign user sentiments to various aspe...Many datasets in E-commerce have rich information about items and users who purchase or rate them. This information can enable advanced machine learning algorithms to extract and assign user sentiments to various aspects of the items thus leading to more sophisticated and justifiable recommendations. However, most Collaborative Filtering (CF) techniques rely mainly on the overall preferences of users toward items only. And there is lack of conceptual and computational framework that enables an understandable aspect-based AI approach to recommending items to users. In this paper, we propose concepts and computational tools that can sharpen the logic of recommendations and that rely on users’ sentiments along various aspects of items. These concepts include: The sentiment of a user towards a specific aspect of a specific item, the emphasis that a given user places on a specific aspect in general, the popularity and controversy of an aspect among groups of users, clusters of users emphasizing a given aspect, clusters of items that are popular among a group of users and so forth. The framework introduced in this study is developed in terms of user emphasis, aspect popularity, aspect controversy, and users and items similarity. Towards this end, we introduce the Aspect-Based Collaborative Filtering Toolbox (ABCFT), where the tools are all developed based on the three-index sentiment tensor with the indices being the user, item, and aspect. The toolbox computes solutions to the questions alluded to above. We illustrate the methodology using a hotel review dataset having around 6000 users, 400 hotels and 6 aspects.展开更多
Introduction: Biermer’s disease is an autoimmune disease characterized by a lack of absorption of vitamin B12 in connection with the production of antibodies (A) destroying the intrinsic factor (IF) which allows the ...Introduction: Biermer’s disease is an autoimmune disease characterized by a lack of absorption of vitamin B12 in connection with the production of antibodies (A) destroying the intrinsic factor (IF) which allows the absorption of vitamin B12 (cobalamin). These clinical manifestations are polymorphic and severe in our context. The objective of this work is to identify the epidemiological-clinical, therapeutic and evolutionary characteristics of Biermer’s disease in Guinean population. Materials and methods: This was a retrospective of patient files followed for Biermer’s disease at the internal medicine department of Donka National Hospital from January 2012 to December 2021. Results: Eight patients were included including 5 women and 3 men. The average age of the patients was 48 years old. The diagnostic delay was 3.6 years on average. All our patients had bioclinical anemia (8 cases, i.e. 100%) followed by epigastralgia in 4 cases (50%), neurological damage such as sensitive polyneuropathy in 3 cases (37.5%). Four patients had acquired melanoderma (50%). Hypovitaminosis B12 was found in 4 patients. The myelogram performed in three patients (37.5%) found medullary megaloblastosis. One patient had Hashimoto’s disease associated with Biermer’s disease in endoscopy, (FOGD) found fundica trophy on macroscopy in 4 cases (50%). Treatment consisted of B12 vitamin therapy in all cases with a favorable clinical and biological outcome. Conclusion: Biermer’s disease remains common in Africa and is characterized at a younger age in addition to the severity of clinical and biological manifestations. The care consists of taking vitamin B12 which remains accessible in our context.展开更多
To address the difficulty of training high-quality models in some specific domains due to the lack of fine-grained annotation resources, we propose in this paper a knowledge-integrated cross-domain data generation met...To address the difficulty of training high-quality models in some specific domains due to the lack of fine-grained annotation resources, we propose in this paper a knowledge-integrated cross-domain data generation method for unsupervised domain adaptation tasks. Specifically, we extract domain features, lexical and syntactic knowledge from source-domain and target-domain data, and use a masking model with an extended masking strategy and a re-masking strategy to obtain domain-specific data that remove domain-specific features. Finally, we improve the sequence generation model BART and use it to generate high-quality target domain data for the task of aspect and opinion co-extraction from the target domain. Experiments were performed on three conventional English datasets from different domains, and our method generates more accurate and diverse target domain data with the best results compared to previous methods.展开更多
文摘Aiming at the problem that existing models in aspect-level sentiment analysis cannot fully and effectively utilize sentence semantic and syntactic structure information, this paper proposes a graph neural network-based aspect-level sentiment classification model. Self-attention, aspectual word multi-head attention and dependent syntactic relations are fused and the node representations are enhanced with graph convolutional networks to enable the model to fully learn the global semantic and syntactic structural information of sentences. Experimental results show that the model performs well on three public benchmark datasets Rest14, Lap14, and Twitter, improving the accuracy of sentiment classification.
文摘Many datasets in E-commerce have rich information about items and users who purchase or rate them. This information can enable advanced machine learning algorithms to extract and assign user sentiments to various aspects of the items thus leading to more sophisticated and justifiable recommendations. However, most Collaborative Filtering (CF) techniques rely mainly on the overall preferences of users toward items only. And there is lack of conceptual and computational framework that enables an understandable aspect-based AI approach to recommending items to users. In this paper, we propose concepts and computational tools that can sharpen the logic of recommendations and that rely on users’ sentiments along various aspects of items. These concepts include: The sentiment of a user towards a specific aspect of a specific item, the emphasis that a given user places on a specific aspect in general, the popularity and controversy of an aspect among groups of users, clusters of users emphasizing a given aspect, clusters of items that are popular among a group of users and so forth. The framework introduced in this study is developed in terms of user emphasis, aspect popularity, aspect controversy, and users and items similarity. Towards this end, we introduce the Aspect-Based Collaborative Filtering Toolbox (ABCFT), where the tools are all developed based on the three-index sentiment tensor with the indices being the user, item, and aspect. The toolbox computes solutions to the questions alluded to above. We illustrate the methodology using a hotel review dataset having around 6000 users, 400 hotels and 6 aspects.
文摘Introduction: Biermer’s disease is an autoimmune disease characterized by a lack of absorption of vitamin B12 in connection with the production of antibodies (A) destroying the intrinsic factor (IF) which allows the absorption of vitamin B12 (cobalamin). These clinical manifestations are polymorphic and severe in our context. The objective of this work is to identify the epidemiological-clinical, therapeutic and evolutionary characteristics of Biermer’s disease in Guinean population. Materials and methods: This was a retrospective of patient files followed for Biermer’s disease at the internal medicine department of Donka National Hospital from January 2012 to December 2021. Results: Eight patients were included including 5 women and 3 men. The average age of the patients was 48 years old. The diagnostic delay was 3.6 years on average. All our patients had bioclinical anemia (8 cases, i.e. 100%) followed by epigastralgia in 4 cases (50%), neurological damage such as sensitive polyneuropathy in 3 cases (37.5%). Four patients had acquired melanoderma (50%). Hypovitaminosis B12 was found in 4 patients. The myelogram performed in three patients (37.5%) found medullary megaloblastosis. One patient had Hashimoto’s disease associated with Biermer’s disease in endoscopy, (FOGD) found fundica trophy on macroscopy in 4 cases (50%). Treatment consisted of B12 vitamin therapy in all cases with a favorable clinical and biological outcome. Conclusion: Biermer’s disease remains common in Africa and is characterized at a younger age in addition to the severity of clinical and biological manifestations. The care consists of taking vitamin B12 which remains accessible in our context.
文摘To address the difficulty of training high-quality models in some specific domains due to the lack of fine-grained annotation resources, we propose in this paper a knowledge-integrated cross-domain data generation method for unsupervised domain adaptation tasks. Specifically, we extract domain features, lexical and syntactic knowledge from source-domain and target-domain data, and use a masking model with an extended masking strategy and a re-masking strategy to obtain domain-specific data that remove domain-specific features. Finally, we improve the sequence generation model BART and use it to generate high-quality target domain data for the task of aspect and opinion co-extraction from the target domain. Experiments were performed on three conventional English datasets from different domains, and our method generates more accurate and diverse target domain data with the best results compared to previous methods.
基金National Natural Science Foundation of China(No.51906262)Natural Science Foundation of Hunan Province,China(No.2020JJ5735)Youth Excellent Sci-Tech Talents Training Plan under Hunan Provincial Sci-Tech Talents Sponsorship Program,China(No.2023TJ-N10)。
