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Predicting the impact of academic papers can help scholars quickly identify the high-quality papers in the field. How to develop efficient predictive model for evaluating potential papers has attracted increasing attention in academia. Many studies have shown that early citations contribute to improving the performance of predicting the long-term impact of a paper. Besides early citations, some bibliometric features and altmetric features have also been explored for predicting the impact of academic papers. Furthermore, paper metadata text such as title, abstract and keyword contains valuable information which has effect on its citation count. However, present studies ignore the semantic information contained in the metadata text. In this paper, we propose a novel citation prediction model based on paper metadata text to predict the long-term citation count, and the core of our model is to obtain the semantic information from the metadata text. We use deep learning techniques to encode the metadata text, and then further extract high-level semantic features for learning the citation prediction task. We also integrate early citations for improving the prediction performance of the model. We show that our proposed model outperforms the state-of-the-art models in predicting the long-term citation count of the papers, and metadata semantic features are effective for improving the accuracy of the citation prediction models.

Humor is a special human expression style, an important “lubricant” for daily communication for people; people can convey emotional messages that are not easily expressed through humor. At present, artificial intelligence is one of the popular research domains; “discourse understanding” is also an important research direction, and how to make computers recognize and understand humorous expressions similar to humans has become one of the popular research domains for natural language processing researchers. In this paper, a humor recognition model (MLSN) based on current humor theory and popular deep learning techniques is proposed for the humor recognition task. The model automatically identifies whether a sentence contains humor expression by capturing the inconsistency, phonetic features, and ambiguity of a joke as semantic features. The model was experimented on three publicly available wisecrack datasets and compared with state-of-the-art language models, and the results demonstrate that the proposed model has better humor recognition accuracy and can contribute to the research on discourse understanding.

Semantic feature production norms have several desirable characteristics that have supported models of representation and processing in adults. However, several key challenges have limited the use of semantic feature norms in studies of early language acquisition. First, existing norms provide uneven and inconsistent coverage of early-acquired concepts that are typically produced and assessed in children under the age of three, which is a time of tremendous growth of early vocabulary skills. Second, it is difficult to assess the degree to which young children may be familiar with normed features derived from these adult-generated datasets. Third, it has been difficult to adopt standard methods to generate semantic network models of early noun learning. Here, we introduce Feats—a tool that was designed to make headway on these challenges by providing a database, the Language Learning and Meaning Acquisition (LLaMA) lab Noun Norms that extends a widely used set of feature norms McRae et al. Behavior Research Methods 37 , 547–559, ( 2005 ) to include full coverage of noun concepts on a commonly used early vocabulary assessment. Feats includes several tools to facilitate exploration of features comprising early-acquired nouns, assess the developmental appropriateness of individual features using toddler-accessibility norms, and extract semantic network statistics for individual vocabulary profiles. We provide a tutorial overview of Feats. We additionally validate our approach by presenting an analysis of an overlapping set of concepts collected across prior and new data collection methods. Furthermore, using network graph analyses, we show that the extended set of norms provides novel, reliable results given their enhanced coverage.

Semantic features are components of concepts. In philosophy, there is a predominant focus on those features that are necessary (and jointly sufficient) for the application of a concept. Consequently, the method of cases has been the paradigm tool among philosophers, including experimental philosophers. However, whether a feature is salient is often far more important for cognitive processes like memory, categorization, recognition and even decision-making than whether it is necessary. The primary objective of this paper is to emphasize the significance of researching salient features of concepts. I thereby advocate the use of semantic feature production tasks, which not only enable researchers to determine whether a feature is salient, but also provide a complementary method for studying ordinary language use. I will discuss empirical data on three concepts, conspiracy theory , female/male professor , and life , to illustrate that semantic feature production tasks can help philosophers (a) identify those salient features that play a central role in our reasoning about and with concepts, (b) examine socially relevant stereotypes, and (c) investigate the structure of concepts.

A brocade is a type of heavy cloth with a raised pattern embroidered in gold or silver silk thread. Chinese brocades are famous for their long history, exquisite patterns, and weaving techniques. This study examines 160 Chinese brocade product names, 31 category names, and 24 subcategory names. The names were extracted from A Complete Guide to Chinese Brocade for the purpose of investigating the onomastic patterns of brocades through lexicological and semantic feature analysis. Our results show that the names of brocade categories were usually formed by at most two lexicological units, while the names of brocade products were commonly composed of three or more lexicological units. The lexicological structure of these brocade names was as follows: [pattern name + jin ‘brocade’]. Most of the brocade names investigated in this study had the following composition: [modifier(s) + the core semantic element jin‘brocade’], with the modifiers indicating different characteristics of the brocade. In addition to offering a presentation of these findings, this study also explores the socio-cultural implication of these brocade names from a socio-onomastic perspective. Our results show that the Chinese brocade names examined reflect the Chinese people’s pursuit of a happy, wealthy, and healthy life, along with a harmonious relationship with nature and other people.

Link prediction in co-word network is a quantitative method widely used to predict the research trends and direction of disciplines. It has aroused extensive attention from academia and the industry domain. Most of the methods to date predicting co-word network links are only based on the topology of the co-word network but ignore the characteristics of network nodes. This paper proposes an approach with an attempt to exploit network nodes’ semantic information to improve link prediction in co-word network. Our work involves three major tasks. First, a new semantic feature of network nodes (based on the original technology) was proposed. Second, multiple ground-truth data sets which consist of literature from the Information Science and Library Science, Blockchain and Primary Health Care fields are built. Third, to validate the effectiveness of the new feature and prior ones, extensive prediction experiments are carried out based on the data set we construct. The result shows that the new predictive models with semantic information obtain more than 80% of overall accuracy and more than 0.7 of Area Under Curve, which indicates the effectiveness and stability of the new feature in different feature sets and algorithm sets.

Cross-site scripting (XSS) attacks can be implemented through various attack vectors, and the diversity of these vectors significantly increases the overhead required for detection systems. The existing XSS detection methods face issues such as insufficient feature extraction capabilities for XSS attacks, inadequate multisource feature fusion processes, and high resource consumption levels for their detection models. To address these problems, we propose a novel XSS detection approach based on multisource semantic feature fusion. First, we design a normalized tokenization rule based on the structural features of XSS code and use a word embedding model to generate the original feature vectors of XSS. Second, we propose a local semantic feature extraction network based on depthwise separable convolution (DSC) that extracts XSS text and syntactic features using convolution kernels with different sizes. Then, we use a bidirectional long short-term memory (Bi-LSTM) network to extract the global semantic features of XSS. Finally, we introduce a multihead attention fusion network that employs a saliency score and a dynamic weight adjustment mechanism to identify the key parts of the input sequence and dynamically adjust the weight of each head. This enables the deep fusion of local and global XSS semantic features. Experimental results demonstrate that the proposed approach achieves an F1 score of 99.92%, outperforming the existing detection methods.

Identifying fishing vessel types with artificial intelligence has become a key technology in marine resource management. However, classical feature modeling lacks the ability to express time series features, and the feature extraction is insufficient. Hence, this work focuses on the identification of trawlers, gillnetters, and purse seiners based on semantic feature vectors. First, we extract trajectories from massive and complex historical Vessel Monitoring System data that contain a large amount of dirty data and then extract the semantic features of fishing vessel trajectories. Finally, we input the semantic feature vectors into the LightGBM classification model for classification of fishing vessel types. In this experiment, the F1 measure of our proposed method on the East China Sea fishing vessel dataset reached 96.25, which was 6.82% higher than that of the classical feature-modeling method based on fishing vessel trajectories. Experiments show that this method is accurate and effective for the classification of fishing vessels.

This paper builds a systematic argument against the existence of semantic features, although these would in principle conform with the understanding of features in Chomsky (1995) as instructions to the interfaces, to the Conceptual-Intentional Interface in this case. I first lay out their superfluous character as well as their redundancy in separationist / realisational approaches, and in non-lexicalist models of grammar, more generally. Under the assumption that lexical meaning in natural language is mediated by grammatical structure containing roots, (purely) semantic features would inevitably be restricted to “non-lexical” elements only, i.e. those derivational affixes that encode rich conceptual content. This makes the positing of semantic features methodologically suspect and, ultimately, redundant.Accordingly, the rich content of derivational affixes, which can involve pretty much any nominal concept (as in Acquaviva 2009) from ‘profession’, ’tree’, and ‘place’ to body parts, will be argued not to be encoded in terms of semantic features. On the contrary, this paper makes the case for derivational affixes not belonging to a unitary syntactic category, with some derivational affixes actually being roots interpreted in particular structural contexts, as has been argued already since De Belder (2011). The chapter closes by offering a taxonomy of the elements that grammar manipulates and sketches the division of labour between root structures and formal features.

Sentiment analysis aims to determine the sentiment orientation of a text piece (sentence or document), but many practical applications require more in-depth analysis, which makes finer-grained sentiment classification the ideal solution. Aspect-level Sentiment Classification (ALSC) is a task that identifies the emotional polarity for aspect terms in a sentence. As the mainstream Transformer framework in sentiment classification, BERT-based models apply self-attention mechanism that extracts global semantic information for a given aspect, while a certain proportion of local information is missing in the process. Although recent ALSC models have achieved good performance, they suffer from robustness issues. In addition, uneven distribution of samples greatly hurts model performance. To address these issues, we present the PConvBERT (Prompt-ConvBERT) and PConvRoBERTa (Prompt-ConvRoBERTa) models, in which local context features learned by a Local Semantic Feature Extractor (LSFE) are fused with the BERT/RoBERTa global features. To deal with the robustness problem of many deep learning models, adversarial training is applied to increase model stability. Additionally, Focal Loss is applied to alleviate the impact of unbalanced sample distribution. To fully explore the ability of the pre-training model itself, we also propose natural language prompt approaches that better solve the ALSC problem. We utilize masked vector outputs of templates for sentiment classification. Extensive experiments on public datasets demonstrate the effectiveness of our model.