On Twitter, people often use hashtags to mark the subject of a tweet. Tweets have specific themes or content that are easy for people to manage. With the increase in the number of tweets, how to automatically recommend hashtags for tweets has received wide attention. The previous hashtag recommendation methods were to convert the task into a multi-class classification problem. However, these methods can only recommend hashtags that appeared in historical information, and cannot recommend the new ones. In this work, we extend the self-attention mechanism to turn the hashtag recommendation task into a sequence labeling task. To train and evaluate the proposed method, we used the real tweet data which is collected from Twitter. Experimental results show that the proposed method can be significantly better than the most advanced method. Compared with the state-of-the-art methods, the accuracy of our method has been increased 4%.

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Typhoons are major natural disasters in China. Much typhoon information is contained in a large number of network media resources, such as news reports and volunteered geographic information (VGI) data, and these are the implicit data sources for typhoon research. However, two problems arise when using typhoon information from Chinese news reports. Since the Chinese language lacks natural delimiters, word segmentation error results in trigger mismatches. Additionally, the polysemy of Chinese affects the classification of triggers. Second, there is no authoritative classification system for typhoon events. This paper defines a classification system for typhoon events, and then uses the system in a neural network model, lattice-structured bidirectional long–short-term memory with a conditional random field (BiLSTM-CRF), to detect these events in Chinese online news. A typhoon dataset is created using texts from the China Weather Typhoon Network. Three other datasets are generated from general Chinese web pages. Experiments on these four datasets show that the model can tackle the problems mentioned above and accurately detect typhoon events in Chinese news reports.