The advent of social media and its prosperity enable users to share their opinions and views. Understanding users' emotional states might provide the potential to create new business opportunities. Automatically identifying users' emotional states from their texts and classifying emotions into finite categories such as joy, anger, disgust, etc., can be considered as a text classification problem. However, it introduces a challenging learning scenario where multiple emotions with different intensities are often found in a single sentence. Moreover, some emotions co-occur more often while other emotions rarely coexist. In this paper, we propose a novel approach based on emotion distribution learning in order to address the aforementioned issues. The key idea is to learn a mapping function from sentences to their emotion distributions describing multiple emotions and their respective intensities. Moreover, the relations of emotions are captured based on the Plutchik's wheel of emotions and are subsequently incorporated into the learning algorithm in order to improve the accuracy of emotion detection. Experimental results show that the proposed approach can effectively deal with the emotion distribution detection problem and perform remarkably better than both the state-of-theart emotion detection method and multi-label learning methods.

This paper presents insights into the structure of turbulence in combined wavecurrent boundary layers, based on experiments performed in flumes of different scale using Particle Image Velocimetry and hydrogen bubble visualisation. Flow conditions covered a range of wave frequencies, wave amplitudes and mean flow conditions. Results show that the spacing between turbulent streaks varies periodically with the passage of each wave, increasing when the flow accelerates and decreasing when the flow decelerates. A new formula has been put forward, relating the streak spacing variation and the wave-induced orbital displacements. The near-wall flow structure suggests a rhythmic pattern in terms of the velocity gradients across the flume. Waves with higher frequencies and larger amplitudes lead to a greater reduction of mean streak spacing, together with a greater increase of the maximum Reynolds shear stress induced by ejections. These results can be useful for better predictions of the hydrodynamics and sediment transport in combined wave-current flows.

Summary Short text similarity plays an important role in natural language processing (NLP). It has been applied in many fields. Due to the lack of sufficient context in the short text, it is difficult to measure the similarity. The use of semantics similarity to calculate textual similarity has attracted the attention of academia and industry and achieved better results. In this survey, we have conducted a comprehensive and systematic analysis of semantic similarity. We first propose three categories of semantic similarity: corpus‐based, knowledge‐based, and deep learning (DL)‐based. We analyze the pros and cons of representative and novel algorithms in each category. Our analysis also includes the applications of these similarity measurement methods in other areas of NLP. We then evaluate state‐of‐the‐art DL methods on four common datasets, which proved that DL‐based can better solve the challenges of the short text similarity, such as sparsity and complexity. Especially, bidirectional encoder representations from transformer model can fully employ scarce information of short texts and semantic information and obtain higher accuracy and F1 value. We finally put forward some future directions.

To extract structured representations of newsworthy events from Twitter, unsupervised models typically assume that tweets involving the same named entities and expressed using similar words are likely to belong to the same event. Hence, they group tweets into clusters based on the cooccurrence patterns of named entities and topical keywords. However, there are two main limitations. First, they require the number of events to be known beforehand, which is not realistic in practical applications. Second, they don't recognise that the same named entity might be referred to by multiple mentions and tweets using different mentions would be wrongly assigned to different events. To overcome these limitations, we propose a nonparametric Bayesian mixture model with word embeddings for event extraction, in which the number of events can be inferred automatically and the issue of lexical variations for the same named entity can be dealt with properly. Our model has been evaluated on three datasets with sizes ranging between 2,499 and over 60 million tweets. Experimental results show that our model outperforms the baseline approach on all datasets by 5-8% in F-measure.

Knowledge modeling is an important step in building knowledge-based applications. Understanding the processes of knowledge modeling and the techniques involved can help developers to grasp the knowledge