Topic-Aware Neural Keyphrase Generation for Social Media Language
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摘要A huge volume of user-generated content is daily produced on social media. To facilitate automatic language understanding, we study keyphrase prediction, distilling salient information from massive posts. While most existing methods extract words from source posts to form keyphrases, we propose a sequence-to-sequence (seq2seq) based neural keyphrase generation framework, enabling absent keyphrases to be created. Moreover, our model, being topic-aware, allows joint modeling of corpus-level latent topic representations, which helps alleviate the data sparsity that widely exhibited in social media language. Experiments on three datasets collected from English and Chinese social media platforms show that our model significantly outperforms both extraction and generation models that do not exploit latent topics. Further discussions show that our model learns meaningful topics, which interprets its superiority in social media keyphrase generation.
出版社接受日期14.05.2019
著者Yue Wang, Jing Li, Hou Pong Chan, Irwin King, Michael R. Lyu, Shuming Shi
會議名稱The 57th Annual Meeting of the Association for Computational Linguistics
會議開始日28.07.2019
會議完結日02.08.2019
會議地點Florence
會議國家/地區意大利
出版年份2019
語言美式英語

上次更新時間 2019-26-09 於 09:42