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Register a new userImproving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories
تحسين التعلم الموحد لتحليل المعنويات المستندة إلى جانب جانب من جانب ذكريات الموضوع
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يتنبأ تحليل المعنويات المستندة إلى جانب الجسيم (ABASA) بقبولية المعنويات نحو مصطلح معين معين في جملة، وهي مهمة مهمة في تطبيقات العالم الحقيقي. لأداء ABSA، يلزم النموذج المدرب أن يكون له فهم جيد للمعلومات السياقية، وخاصة الأنماط الخاصة التي تشير إلى قطبية المعنويات. ومع ذلك، تختلف هذه الأنماط عادة في جمل مختلفة، خاصة عندما تأتي الجمل من مصادر مختلفة (المجالات)، مما يجعل ABSA لا يزال صعبا للغاية. على الرغم من الجمع بين البيانات المسمى عبر مصادر مختلفة (المجالات) هو حل واعد لمعالجة التحدي، في التطبيقات العملية، عادة ما يتم تخزين هذه البيانات المسمى في مواقع مختلفة وقد لا يمكن الوصول إليها لبعضها البعض بسبب الخصوصية أو المخاوف القانونية (مثل البيانات مملوكة لشركات مختلفة). لمعالجة هذه المشكلة واستخدم أفضل استخدام لجميع البيانات المسمى، نقترح نموذج ABSA الجديد مع التعلم الفيدرالي (FL) المعتمد للتغلب على قيود عزل البيانات وإدماج ذاكرة الموضوع (TM) المقترح اتخاذ حالات البيانات من مصادر متنوعة (المجالات) في الاعتبار. خاصة، تهدف TM إلى تحديد مصادر البيانات المختلفة المعزولة بسبب عدم إمكانية الوصول إلى البيانات من خلال توفير معلومات فئة مفيدة للتنبؤات المحلية. توضح النتائج التجريبية على بيئة محاكاة لثلاثة عقد مع ثلاث عقود فعالية نهجنا، حيث تتفوق TM-FL على خطوط أساس مختلفة بما في ذلك بعض أطر FL مصممة جيدا.
Aspect-based sentiment analysis (ABSA) predicts the sentiment polarity towards a particular aspect term in a sentence, which is an important task in real-world applications. To perform ABSA, the trained model is required to have a good understanding of the contextual information, especially the particular patterns that suggest the sentiment polarity. However, these patterns typically vary in different sentences, especially when the sentences come from different sources (domains), which makes ABSA still very challenging. Although combining labeled data across different sources (domains) is a promising solution to address the challenge, in practical applications, these labeled data are usually stored at different locations and might be inaccessible to each other due to privacy or legal concerns (e.g., the data are owned by different companies). To address this issue and make the best use of all labeled data, we propose a novel ABSA model with federated learning (FL) adopted to overcome the data isolation limitations and incorporate topic memory (TM) proposed to take the cases of data from diverse sources (domains) into consideration. Particularly, TM aims to identify different isolated data sources due to data inaccessibility by providing useful categorical information for localized predictions. Experimental results on a simulated environment for FL with three nodes demonstrate the effectiveness of our approach, where TM-FL outperforms different baselines including some well-designed FL frameworks.
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Aspect-based sentiment analysis (ABSA) mainly involves three subtasks: aspect term extraction, opinion term extraction, and aspect-level sentiment classification, which are typically handled in a separate or joint manner. However, previous approaches
do not well exploit the interactive relations among three subtasks and do not pertinently leverage the easily available document-level labeled domain/sentiment knowledge, which restricts their performances. To address these issues, we propose a novel Iterative Multi-Knowledge Transfer Network (IMKTN) for end-to-end ABSA. For one thing, through the interactive correlations between the ABSA subtasks, our IMKTN transfers the task-specific knowledge from any two of the three subtasks to another one at the token level by utilizing a well-designed routing algorithm, that is, any two of the three subtasks will help the third one. For another, our IMKTN pertinently transfers the document-level knowledge, i.e., domain-specific and sentiment-related knowledge, to the aspect-level subtasks to further enhance the corresponding performance. Experimental results on three benchmark datasets demonstrate the effectiveness and superiority of our approach.
It is popular that neural graph-based models are applied in existing aspect-based sentiment analysis (ABSA) studies for utilizing word relations through dependency parses to facilitate the task with better semantic guidance for analyzing context and
aspect words. However, most of these studies only leverage dependency relations without considering their dependency types, and are limited in lacking efficient mechanisms to distinguish the important relations as well as learn from different layers of graph based models. To address such limitations, in this paper, we propose an approach to explicitly utilize dependency types for ABSA with type-aware graph convolutional networks (T-GCN), where attention is used in T-GCN to distinguish different edges (relations) in the graph and attentive layer ensemble is proposed to comprehensively learn from different layers of T-GCN. The validity and effectiveness of our approach are demonstrated in the experimental results, where state-of-the-art performance is achieved on six English benchmark datasets. Further experiments are conducted to analyze the contributions of each component in our approach and illustrate how different layers in T-GCN help ABSA with quantitative and qualitative analysis.
The pivot for the unified Aspect-based Sentiment Analysis (ABSA) is to couple aspect terms with their corresponding opinion terms, which might further derive easier sentiment predictions. In this paper, we investigate the unified ABSA task from the p
erspective of Machine Reading Comprehension (MRC) by observing that the aspect and the opinion terms can serve as the query and answer in MRC interchangeably. We propose a new paradigm named Role Flipped Machine Reading Comprehension (RF-MRC) to resolve. At its heart, the predicted results of either the Aspect Term Extraction (ATE) or the Opinion Terms Extraction (OTE) are regarded as the queries, respectively, and the matched opinion or aspect terms are considered as answers. The queries and answers can be flipped for multi-hop detection. Finally, every matched aspect-opinion pair is predicted by the sentiment classifier. RF-MRC can solve the ABSA task without any additional data annotation or transformation. Experiments on three widely used benchmarks and a challenging dataset demonstrate the superiority of the proposed framework.
When we are interested in a certain domain, we can collect and analyze data from the Internet. The newly collected data is not labeled, so the use of labeled data is hoped to be helpful to the new data. We perform name entity recognition (NER) and as
pect-based sentiment analysis (ABSA) in multi-task learning, and combine parameter generation network and DANN architecture to build the model. In the NER task, the data is labeled with Tie, Break, and the task weight is adjusted according to the loss change rate of each task using Dynamic Weight Average (DWA). This study used two different source domain data sets. The experimental results show that Tie, Break can improve the results of the model; DWA can have better performance in the results; the combination of parameter generation network and gradient reversal layer can be used for every good learning in different domain.
Recently, the majority of sentiment analysis researchers focus on target-based sentiment analysis because it delivers in-depth analysis with more accurate results as compared to traditional sentiment analysis. In this paper, we propose an interactive
learning approach to tackle a target-based sentiment analysis task for the Arabic language. The proposed IA-LSTM model uses an interactive attention-based mechanism to force the model to focus on different parts (targets) of a sentence. We investigate the ability to use targets, right, and left context, and model them separately to learn their own representations via interactive modeling. We evaluated our model on two different datasets: Arabic hotel review and Arabic book review datasets. The results demonstrate the effectiveness of using this interactive modeling technique for the Arabic target-based task. The model obtained accuracy values of 83.10 compared to SOTA models such as AB-LSTM-PC which obtained 82.60 for the same dataset.
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