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Hashtags of microblogs can provide valuable information for many natural language processing tasks. How to recommend reliable hashtags automatically has attracted considerable attention. However, existing studies assumed that all the training corpus crawled from social networks are labelled correctly, while large sample statistics on real social media shows that there are 8.9% of microblogs with hashtags having wrong labels. The notable influence of noisy data to the classifier is ignored before. Meanwhile, recency also plays an important role in microblog hashtag, but the information is not used in the existing studies. Some temporal hashtags such as World Cup will ignite at a particular time, but at other times, the number of people talking about it will sharply decrease. To address the twofold shortcomings above, the authors propose an long short-term memory-based model, which uses temporal enhanced selective sentence-level attention to reduce the influence of wrong labelled microblogs to the classifier. Experimental results using a dataset of 1.7 million microblogs collected from SINA Weibo microblogs demonstrated that the proposed method could achieve significantly better performance than the state-of-the-art methods.

Cross-modal retrieval has been recently proposed to find an appropriate subspace, where the similarity across different modalities such as image and text can be directly measured. In this study, different from most existing works, the authors propose a novel model for cross-modal retrieval based on a domain-adaptive limited text space (DALTS) rather than a common space or an image space. Experimental results on three widely used datasets, Flickr8K, Flickr30K and Microsoft Common Objects in Context (MSCOCO), show that the proposed method, dubbed DALTS, is able to learn superior text space features which can effectively capture the necessary information for cross-modal retrieval. Meanwhile, DALTS achieves promising improvements in accuracy for cross-modal retrieval compared with the current state-of-the-art methods.

Question classification helps to generate more accurate answers in question answering system. For an efficient question classifier, one of the most important tasks is to fully mine useful features. Aiming at solving the problem of lacking of rich syntax and semantic features in Chinese question classification, an operator called MBWB (multilayer bag-of-words binding) is proposed to extract potential features by binding part-of-speech, word sense, named entity and other basic features to bag-of-words, respectively. Through performing MBWB operator on two kinds of bag-of-words, i.e. A_BOW and T_BOW, the corresponding A_MBWB and T_MBWB features are generated automatically. The MBWB operator can explore potential information contained in basic features, and enrich syntactic and semantic representation of questions. Experimental results on the Chinese question set show that the classification accuracy gets significantly improved when combining two kinds of MBWB features with basic features.