阿拉伯语情感分析：BERT模型在方面情绪分类中的应用

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"这篇论文是关于使用BERT进行阿拉伯语方面的基于情感的分类，即阿拉伯语的方面情感分析(ABSA)。研究者来自埃及阿斯尤特大学的信息系统部门，他们探讨了预训练语言模型（如BERT）的上下文嵌入在处理阿拉伯语情感极性分类任务中的潜力。论文中提出了一种简单的BERT神经基线模型，该模型在处理任务时超越了当前最先进的方法。" 正文: 基于阿拉伯方面的情感分类是文本分析领域的一个重要方法，它关注的是针对特定目标的某些方面的情感极性。尽管大部分的研究集中在英语的ABSA上，但阿拉伯语的ABSA也逐渐受到关注。然而，过去的阿拉伯语研究主要依赖于深度学习模型，这些模型通常基于上下文独立的词嵌入（如word2vec），每个词的表示与其上下文无关。本论文的核心在于利用预训练的上下文语言模型，特别是BERT，来改进阿拉伯语的情感分析。BERT是一种双向Transformer架构的语言模型，它能够捕捉到词的上下文依赖关系，提供更丰富的词向量表示。通过将句子对作为输入，BERT在处理情感极性分类任务时能更好地理解语境信息。研究者构建了一个基于BERT的简单但有效的神经网络基线模型。这个模型在结构上附加了一个线性分类层，用于确定输入语句中的情感极性。实验结果表明，这种架构在阿拉伯语的方面情感分类任务上超过了现有的最佳性能，证实了BERT的上下文嵌入对于理解和分析阿拉伯语情感的强大能力。此外，该研究还强调了预训练模型在处理小规模、低资源语言数据集时的优势，这对于阿拉伯语等资源相对匮乏的语言尤其重要。这不仅有助于提升情感分析的准确度，也为未来阿拉伯语自然语言处理研究提供了新的方向。这篇论文为阿拉伯语的ABSA提供了一个有力的工具，并证明了BERT在处理具有挑战性的语言任务时的普适性和有效性。通过使用BERT的上下文感知嵌入，研究者成功地提高了情感分类的精确度，为阿拉伯语的文本分析领域开辟了新的可能性。

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.,

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阿拉伯语情感分析：BERT模型在方面情绪分类中的应用

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