ABSA’s earliest efforts depended mainly on machine learning methods that
focus on handcrafted features like lexicons to train sentiment classifiers (Jiang
et al., 2011)(Kiritchenko et al., 2014). These methods are effective but rely
heavily on the efficiency of handcrafted features.
Subsequently, a set of methods based on neural networks consisting of a
word embedding layer followed by a neural architecture were developed for
the ABSA task and pretty results were achieved (Tang et al., 2015a; Zhang
et al., 2016; Poria et al., 2016).
Several attention-based models have been applied to the ABSA task for its
ability to focus on important parts of the sentence related to aspects (Wang
et al., 2016) (Yang et al., 2017). But a single attention network maybe not
enough at capturing key dependency attributes between context and targets
especially when the sentence is long, so multiple attention networks were
proposed to solve this problem (Chen et al., 2017; Ma et al., 2017).
In order to resolve the question-answering problem, the authors of (Weston
et al., 2014) have developed the memory network concept (MemNN), which
was later adopted in several NLP challenges including ABSA(Tang et al.,
2016; Tay et al., 2018, 2017).
Pre-trained language models have recently played a vital role in many NLP
applications, as they can take advantage of the vast volume of unlabeled data
to learn general language representations; Elmo (Peters et al., 2018), GPT
(Radford et al., 2018), and BERT (Devlin et al., 2018) are among the most
well-known examples. The authors of (Li et al., 2019) studied the use of
BERT embeddings with several neural models, such as a linear layer, GRU
model, self-attention network, and conditional random field to deal with the
ABSA task. In (Sun et al., 2019), the authors have demonstrated that treat-
ing ABSA as a sentence pair classification task by building auxiliary sentence
as input to the BERT, significantly improved the results. The strength of
BERT embeddings in ABSA was further investigated by (Hoang et al., 2019;
Bhoi and Joshi, 2018; Xu et al., 2019; Nurifan et al., 2019).
In general, Arabic ABSA research development is slower than English.
(Ruder et al., 2016b) combined aspect embedding with each word embedding
and sent the mixture to CNN model to address aspect polarity and category
identification tasks. Subsequently, (Ruder et al., 2016a) took advantage of
modeling internal information related to the hierarchical review structure in
solving the ABSA task.
Two supervised machine learning-based techniques, RNN and SVM supple-
mented with a set of handcrafted features, were suggested by (Al-Smadi et al.,
4
剩余18页未读，继续阅读
易小侠
- 粉丝: 6545
- 资源: 9万+
我的内容管理
展开
最新资源
- 新型矿用本安直流稳压电源设计：双重保护电路
- 煤矿掘进工作面安全因素研究：结构方程模型
- 利用同位素位移探测原子内部新型力
- 钻锚机钻臂动力学仿真分析与优化
- 钻孔成像技术在巷道松动圈检测与支护设计中的应用
- 极化与非极化ep碰撞中J/ψ的Sivers与cos2φ效应：理论分析与COMPASS验证
- 新疆矿区1200m深孔钻探关键技术与实践
- 建筑行业事故预防：综合动态事故致因理论的应用
- 北斗卫星监测系统在电网塔形实时监控中的应用
- 煤层气羽状水平井数值模拟：交替隐式算法的应用
- 开放字符串T对偶与双空间坐标变换
- 煤矿瓦斯抽采半径测定新方法——瓦斯储量法
- 大倾角大采高工作面设备稳定与安全控制关键技术
- 超标违规背景下的热波动影响分析
- 中国煤矿选煤设计进展与挑战：历史、现状与未来发展
- 反演技术与RBF神经网络在移动机器人控制中的应用
资源上传下载、课程学习等过程中有任何疑问或建议，欢迎提出宝贵意见哦~我们会及时处理！
点击此处反馈
