An Ontology-Based Approach to Enhance Explicit Aspect Extraction in Standard Arabic Reviews [PDF]
International Journal of Computing and Digital Systems, 2022 : Currently, one of the most important and di ffi cult areas of research is Arabic Sentiment analysis. Aspect extraction is the most important task in aspect-based sentiment analysis.
Salima Behdenna +2 more
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Explicit non-Gorenstein R=T via rank bounds II: Computational aspects [PDF]
Research in Number Theory, 2022 This is the second in a pair of papers about residually reducible Galois deformation rings with non-optimal level. In the first paper, we proved a Galois-theoretic criterion for the deformation ring to be as small as possible. This paper focuses on the computations needed to verify this criterion.
Catherine Hsu +2 more
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Zero-shot Aspect-level Sentiment Classification via Explicit Utilization of Aspect-to-Document Sentiment Composition [PDF]
, 2022 As aspect-level sentiment labels are expensive and labor-intensive to acquire, zero-shot aspect-level sentiment classification is proposed to learn classifiers applicable to new domains without using any annotated aspect-level data. In contrast, document-level sentiment data with ratings are more easily accessible.
Pengfei Deng +3 more
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Aspect-Based Sentiment Analysis with Explicit Sentiment Augmentations [PDF]
AAAI Conference on Artificial Intelligence, 2023 Aspect-based sentiment analysis (ABSA), a fine-grained sentiment classification task, has received much attention recently. Many works investigate sentiment information through opinion words, such as "good'' and "bad''.
Jihong Ouyang +5 more
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Predictability Aspects of Global Aqua-planet Simulations with Explicit Convection
Journal of the Meteorological Society of Japan. Ser. II, 2008 High-resolution global simulations over zonally symmetric aqua planets are examined using Fourier analysis in the zonal direction. We highlight the tropics, where the large-scale weather consists of convectively-coupled waves so that explicit convection is an especially topical novelty.Squared differences between pairs of runs grow from initially tiny ...
Brian Mapes +3 more
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Enhancing Attention-Based LSTM With Position Context for Aspect-Level Sentiment Classification
IEEE Access, 2019 Aspect-level sentiment classification is an interesting but challenging research problem, namely, the prediction of the sentiment polarity toward a specific aspect term of an opinionated sentence.
Jiangfeng Zeng, Xiao Ma, Ke Zhou
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Some aspects of explicit birational geometry inspired by complex\n dynamics [PDF]
, 2014 Our aim is to illustrate how one can effectively apply the basic ideas and notions of topological entropy and dynamical degrees, together with recent progress of minimal model theory in higher dimension, for an explicit study of birational or biregular selfmaps of projective or compact K hler manifolds, through concrete examples.
Keiji Oguiso
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English Aspect: L1 Transfer and Explicit Instruction [PDF]
JALT Postconference Publication, 2019 Aspect shows cross-linguistic variation, and the role of the first language in the acquisition of aspect is often discussed in second language literature. However, whether L1 transfer actually occurs in the areas of grammar is controversial. In this paper, I discuss the aspectual characteristics of English and Japanese associated with their aspectual ...
Yuko Koike
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Aspect-Specific Heterogeneous Graph Convolutional Network for Aspect-Based Sentiment Classification
IEEE Access, 2020 Aspect-based sentiment classification aims to identify the sentiment expressed towards an aspect given a context sentence. There are two main problems with existing methods: First, the methods simply take the average of the sentence and aspect word ...
Kuanhong Xu, Hui Zhao, Tianwen Liu
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Explicit and Implicit Aspect Extraction using Whale Optimization Algorithm and Hybrid Approach
International Conference Industrial, Enterprise, and System Engineering, 2019 Huge volume of reviews by customers published on different products websites has become an important source of information for both customers and companies.
Mohammad Tubishat, Norisma Idris
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