Abstract
The rapid development of social media has brought the prosperity of online economy. Recently, product promotion in social networks has become an essential way of online marketing. As one of the most common marketing means, Content Marketing (CM) inserts advertisements into regular articles in a roundabout and covert way. However, the values and characteristics of products are often exaggerated to attract users’ attention. It could cause severe economic losses to users and influence the creditworthiness of the platforms. In this paper, we model the problem of advertisement extraction from CM articles as a sentence classification task. We propose a topic-enhanced deep neural network to encode the semantic information of a sentence for classification. Motivated by the characteristics of CM articles, we develop a segment-aware optimization method that considers the label transitions of sentences in different segments of an article to improve the performance of the classifier. Experimental results based on real-world datasets demonstrate the superiority of the proposed method over state-of-the-art approaches.
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Acknowledgment
The research presented in this paper is supported in part by National Natural Science Foundation of China (No. 61602370, U1736205, 61922067, 61902305), Shenzhen Basic Research Grant (JCYJ20170816100819428), Natural Science Basic Research Plan in Shaanxi Province (2021JM-018).
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Fan, X., Wang, C. (2021). Advertisement Extraction from Content Marketing Articles via Segment-Aware Sentence Classification. In: Wang, L., Feng, Y., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2021. Lecture Notes in Computer Science(), vol 13028. Springer, Cham. https://doi.org/10.1007/978-3-030-88480-2_50
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DOI: https://doi.org/10.1007/978-3-030-88480-2_50
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