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1
Predicting the Success of Internet Social Welfare Crowdfunding Based on Text Information
In: Applied Sciences; Volume 12; Issue 3; Pages: 1572 (2022)
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2
How We Failed in Context: A Text-Mining Approach to Understanding Hotel Service Failures
In: Sustainability; Volume 14; Issue 5; Pages: 2675 (2022)
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3
Tracing the Legitimacy of Artificial Intelligence – A Media Analysis, 1980-2020
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4
Dynamics of prescriptivism and lexical borrowings in Contemporary French
Zsombok, Gyula. - 2022
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5
Sentiment Analysis of Arabic Documents
In: Natural Language Processing for Global and Local Business ; https://hal.archives-ouvertes.fr/hal-03124729 ; Fatih Pinarbasi; M. Nurdan Taskiran. Natural Language Processing for Global and Local Business, pp.307-331, 2021, 9781799842408. ⟨10.4018/978-1-7998-4240-8.ch013⟩ ; https://www.igi-global.com/ (2021)
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6
On Multi-domain Sentence Level Sentiment Analysis for Roman Urdu ...
Mehmood, Khawar. - : UNSW Sydney, 2021
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7
Developing Conversational Data and Detection of Conversational Humor in Telugu ...
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8
Detecting Gender Bias using Explainability ...
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9
Characterizing Test Anxiety on Social Media ...
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10
Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification ...
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11
Open Aspect Target Sentiment Classification with Natural Language Prompts ...
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12
SYSML: StYlometry with Structure and Multitask Learning: Implications for Darknet Forum Migrant Analysis ...
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13
Connecting Attributions and QA Model Behavior on Realistic Counterfactuals ...
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14
Solving Aspect Category Sentiment Analysis as a Text Generation Task ...
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15
CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.550/ Abstract: This paper studies continual learning (CL) of a sequence of aspect sentiment classification (ASC) tasks in a particular CL setting called domain incremental learning (DIL). Each task is from a different domain or product. The DIL setting is particularly suited to ASC because in testing the system needs not know the task/domain to which the test data belongs. To our knowledge, this setting has not been studied before for ASC. This paper proposes a novel model called CLASSIC. The key novelty is a contrastive continual learning method that enables both knowledge transfer across tasks and knowledge distillation from old tasks to the new task, which eliminates the need for task ids in testing. Experimental results show the high effectiveness of CLASSIC ...
Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Sentiment Analysis
URL: https://dx.doi.org/10.48448/w2pz-9v10
https://underline.io/lecture/37960-classic-continual-and-contrastive-learning-of-aspect-sentiment-classification-tasks
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16
Improving Multimodal fusion via Mutual Dependency Maximisation ...
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17
Does Commonsense help in detecting Sarcasm? ...
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18
Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories ...
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19
How much coffee was consumed during EMNLP 2019? Fermi Problems: A New Reasoning Challenge for AI ...
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20
Towards Label-Agnostic Emotion Embeddings ...
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