| 1 |
Predicting the Success of Internet Social Welfare Crowdfunding Based on Text Information
|
|
|
|
In: Applied Sciences; Volume 12; Issue 3; Pages: 1572 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 2 |
How We Failed in Context: A Text-Mining Approach to Understanding Hotel Service Failures
|
|
|
|
In: Sustainability; Volume 14; Issue 5; Pages: 2675 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 3 |
Tracing the Legitimacy of Artificial Intelligence – A Media Analysis, 1980-2020
|
|
|
|
BASE
|
|
Show details
|
|
|
| 4 |
Dynamics of prescriptivism and lexical borrowings in Contemporary French
|
|
|
|
BASE
|
|
Show details
|
|
|
| 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)
|
|
BASE
|
|
Show details
|
|
|
| 6 |
On Multi-domain Sentence Level Sentiment Analysis for Roman Urdu ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 7 |
Developing Conversational Data and Detection of Conversational Humor in Telugu ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 10 |
Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 11 |
Open Aspect Target Sentiment Classification with Natural Language Prompts ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 12 |
SYSML: StYlometry with Structure and Multitask Learning: Implications for Darknet Forum Migrant Analysis ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 13 |
Connecting Attributions and QA Model Behavior on Realistic Counterfactuals ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 14 |
Solving Aspect Category Sentiment Analysis as a Text Generation Task ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 15 |
CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 16 |
Improving Multimodal fusion via Mutual Dependency Maximisation ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 18 |
Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 19 |
How much coffee was consumed during EMNLP 2019? Fermi Problems: A New Reasoning Challenge for AI ...
|
|
|
|
Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.582/ Abstract: Many real-world problems require the com- bined application of multiple reasoning abilities—employing suitable abstractions, commonsense knowledge, and creative syn- thesis of problem-solving strategies. To help advance AI systems towards such capabilities, we propose a new reasoning challenge, namely Fermi Problems (FPs), which are questions whose answers can only be approximately estimated because their precise computation is either impractical or impossible. For example, “How much would the sea level rise if all ice in the world melted?” FPs are commonly used in quizzes and interviews to bring out and evaluate the creative reasoning abilities of humans. To do the same for AI systems, we present two datasets: 1) A collection of 1k real-world FPs sourced from quizzes and olympiads; and 2) a bank of 10k synthetic FPs of intermediate complexity to serve as a sandbox for the harder real-world challenge. In addition to question-answer ...
|
|
Keyword:
Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Sentiment Analysis
|
|
URL: https://dx.doi.org/10.48448/ht2e-qg69
https://underline.io/lecture/37469-how-much-coffee
|
|
BASE
|
|
Hide details
|
|
|
|
|
