Due to cultural influences and the long history of language evolution, metaphorical expressions are pervasive in Chinese texts, particularly in ironical comments, poetry, and various literary works. Traditional Chinese text classification methods relying on surface-level features often fail to bridge the gap between literal features and label spaces in such texts. To address this problem, we propose L-MAM, a novel label-constrained contrastive learning approach for Chinese text classification based on large language models. Specifically, we first introduce an ambiguity recognition module (ARM) to quantify phonetic and syntactic ambiguities in Chinese characters, identifying expressions that benefit most from metaphorical associations. By leveraging large language model-driven prompts, metaphorical interpretations are extracted from texts, and classification labels are aligned with semantic definitions for deeper contextual understanding. Then, we design a contextual attention mechanism that dynamically adjusts weights based on character ambiguity, and a metaphorical attention mechanism that aligns metaphorical embeddings with label semantics for refined label-constrained associations. Additionally, we devise a contextual-metaphorical contrastive learning mechanism, which employs a triplet loss to differentiate literal and metaphorical aspects while enhancing interclass separability. Finally, we conduct extensive experiments on three real-world datasets, where the experimental results validate the effectiveness of L-MAM, offering new insights into Chinese text classification involving metaphorical comprehension.

Chinese Spelling Check (CSC) aims to identify and correct spelling errors in Chinese texts, where enhanced semantic understanding of a sentence can significantly improve correction accuracy.Recently, Large Language Models (LLMs) have demonstrated exceptional mastery of world knowledge and semantic understanding, rendering them more robust against spelling errors.However, the application of LLMs in CSC is a double-edged sword, as they tend to unnecessarily alter sentence length and modify rare but correctly used phrases.In this paper, by leveraging the capabilities of LLMs while mitigating their limitations, we propose a novel plug-and-play Alignment-and-Replacement Module (ARM) that enhances the performance of existing CSC models and without the need for retraining or fine-tuning.Experiment results and analysis on three benchmark datasets demonstrate the effectiveness and competitiveness of the proposed module.

Cross-domain hyperspectral image (HSI) classification can improve the model’s classification performance in the target domain by utilizing the rich knowledge from the source domain. However, existing cross-domain HSI classification methods mostly belong to transductive learning, which is difficult to apply to domain generalization tasks where the target domain is unseen during model learning. Inspired by human causal reasoning and knowledge induction mechanisms, this article develops an inductive learning-based framework for hyperspectral domain generalization: Causal meta-generalization. By simulating domain generalization scenarios, the framework helps the model induct domain-invariant causal meta-knowledge, thereby ensuring its strong generalization ability to unseen target domains. Specifically, we first propose a bottleneck variational auto-encoder (B-VAE) based on a forward–reverse information bottleneck, decoupling the domain distribution and class distribution of HSIs. By perturbing the domain distribution to generate virtual domains, we simulate potential domain distribution changes in the real world, providing a data basis for the induction of causal meta-knowledge. Second, in the process of simulating domain generalization scenarios, we establish a dual-layer optimization mechanism (DLOM) based on invariant-generalization risk minimization. In the inner layer optimization, by minimizing the model’s invariant causal effect loss (ICEL) in the virtual source domains, we guide the model to learn domain-invariant causal meta-knowledge. In the outer optimization, by minimizing the model’s generalization risk in the unseen virtual target domain, we enhance the applicability of causal meta-knowledge in domain generalization tasks. This proposed method has potential applications in remote sensing signal-processing tasks, such as the recognition of crop pests and diseases and the identification of minerals. The code can be accessed athttps://github.com/wzr78998/CMG.

Reading psychology believes text comprehension to involve a complex psychological construction process, with the reader mind being a dynamic associative system that stores an abundance of schemata. For Chinese text, in particular, the unique ideographic writing system allows its lansign to trigger semantic association and schema recalling without the need of phonetics. In contrast to previous research efforts on text classification problems, in this paper we present an interdisciplinary modeling approach that draws inspirations from the cognitive principles of ideography, schema theory and deep learning to study Chinese text classification. Specifically, we first propose a Radical-guided Associative Model (RAM) for preliminary cognitive imitation, which comprises two coupled spaces, namely the Literal Space and Associative Space. Then, taking consideration of the schemata acquired from the mind of a reader which plays a important role in influencing text-dependent information revision, we extend RAM with a systematic Schema-aware Radical-guided Associative Model (SRAM) that embeds label semantics as essential text-independent human knowledge for real-world abstraction. In SRAM, the Schema Space is introduced and a Schema Attention module is proposed with a novel loss paradigm that includes the linkage and interaction between text-dependent prior concepts and text-independent label schemata. Extensive experiments on three real-world datasets demonstrate the effectiveness and rationality of our proposed method.

As a vivid and linguistic symbol, Emojis have become a prevailing medium interspersed in text-based communication (e.g., social media and chit-chat) to express emotions, attitudes, and situations. Generally speaking, a social-oriented chatbot that can generate appropriate Emoji-embedded responses would be much more competitive, making communications more fun, engaging, and human-like. However, the current Emoji-related research is still in its infancy, leading to an awkward situation of data deficiency. How to develop an Emoji-embedded dialogue system while addressing the lack of data will be interesting and meaningful for the application of future AI. To bridge this gap, we propose a multi-task learning method for persona-aware Emoji-embedded dialogue generation in this article. Specifically, as the benchmark of model training and evaluation, which includes 1.2 million Emoji-embedded tweets and 1.1 million post-response pairs, we first construct a dataset named EmojiTweet to handle the data deficiency problem. Then, a Seq2Seq-based model with multi-task learning is designed to simultaneously learn response generation and Emoji embedding from the constructed non-Emoji dialogue and Emoji-embedded monologue data. Afterward, we incorporate persona factors into our model by adopting persona fusion and personalized bias methods to deliver personalized dialogues with more accurately selected Emojis. Finally, we conduct extensive experiments, where the experimental results and evaluations demonstrate that our model has three key benefits: improved dialogue quality, higher user engagement, and not relying on large-scale Emoji-embedded dialogue data representing specific personas. EmojiTweet will be published publicly via https://mea-lab-421.github.io/EmojiTweet/ .

Text classification is a fundamental and classical problem in natural language processing. Existing methods in this area attach more attention to structure modeling of texts, while largely ignoring the cognitive principles of human reading. Actually, as an important aspect of exploring the characteristics of language comprehension, neuroscience research in recent years has demonstrated the human instinct for abstract thinking, where semantic processing and summarizing play essential roles. To this end, we propose a novel text classification method with Semantic Dependency and Association (SDA). To be specific, SDA comprises two coupled spaces, namely Dependency Space and Association Space, to imitate the real process when readers are inferring text semantics. Firstly, in Dependency Space, we devise a graph modeling structure to thoroughly capture the dependency relations between words. Then in Association Space, we associate the words with prior concepts and adopt attention mechanisms to match words with the most related concepts based on their dependency relations. Later, we synthesize the above two aspects to enhance the semantic representation of words. Afterwards, we design a Semantic Summarizing module to integrate the semantic information learned above. Finally, extensive experiments are conducted on four public English and Chinese text classification datasets, where experimental results not only demonstrate the effectiveness of our model but also reveal the superiority of the perspective from the human cognition for text comprehension.

Emotion Cause Extraction (ECE) aims to reveal the cause clauses behind a given emotion expressed in a text, which has become an emerging topic in broad research communities, such as affective computing and natural language processing. Despite the fact that current methods about the ECE task have made great progress in text semantic understanding from lexicon- and sentence-level, they always ignore the certain causal narratives of emotion text. Significantly, these causal narratives are presented in the form of semantic structure and highly helpful for structure-level emotion cause understanding. Nevertheless, causal narrative is just an abstract narratological concept and its involving semantics is quite different from the common sequential information. Thus, how to properly model and utilize such particular narrative information to boost the ECE performance still remains an unresolved challenge. To this end, in this paper, we propose a novel Causal Narrative Comprehension Model (CNCM) for emotion cause extraction, which learns and leverages causal narrative information smartly to address the above problem. Specifically, we develop a Narrative-aware Causal Association (NCA) unit, which mines the narrative cue about emotional results and uses the semantic correlation between causes and results to model causal narratives of documents. Besides, we design a Result-aware Emotion Attention (REA) unit to make full use of the known result of causal narrative for multiple understanding about emotional causal associations. Through the ingenious combination and collaborative utilization of these two units, we could better identify the emotion cause in the text with causal narrative comprehension. Extensive experiments on the public English and Chinese benchmark datasets of ECE task have validated the effectiveness of CNCM with significant margin by comparing with the state-of-the-art baselines, which demonstrates the potential of narrative information in long text understanding.

Cognitive psychology research shows that humans have the instinct for abstract thinking, where association plays an essential role in language comprehension. Especially for Chinese, its ideographic writing system allows radicals to trigger semantic association without the need of phonetics. In fact, subconsciously using the associative information guided by radicals is a key for readers to ensure the robustness of semantic understanding. Fortunately, many basic and extended concepts related to radicals are systematically included in Chinese language dictionaries, which leaves a handy but unexplored way for improving Chinese text representation and classification. To this end, we draw inspirations from cognitive principles between ideography and human associative behavior to propose a novel Radical-guided Associative Model (RAM) for Chinese text classification. RAM comprises two coupled spaces, namely Literal Space and Associative Space, which imitates the real process in people's mind when understanding a Chinese text. To be specific, we first devise a serialized modeling structure in Literal Space to thoroughly capture the sequential information of Chinese text. Then, based on the authoritative information provided by Chinese language dictionaries, we design an association module and put forward a strategy called Radical-Word Association to use ideographic radicals as the medium to associate prior concept words in Associative Space. Afterwards, we design an attention module to imitate people's matching and decision between Literal Space and Associative Space, which can balance the importance of each associative words under specific contexts. Finally, extensive experiments on two real-world datasets prove the effectiveness and rationality of RAM, with good cognitive insights for future language modeling.

Recent years have witnessed the increasing interests in research of crowdfunding mechanism. In this area, dynamics tracking is a significant issue but is still under exploration. Existing studies either fit the fluctuations of time-series or employ regularization terms to constrain learned tendencies. However, few of them take into account the inherent decision-making process between investors and crowdfunding dynamics. To address the problem, in this paper, we propose a Trajectory-based Continuous Control for Crowdfunding (TC3) algorithm to predict the funding progress in crowdfunding. Specifically, actor-critic frameworks are employed to model the relationship between investors and campaigns, where all of the investors are viewed as an agent that could interact with the environment derived from the real dynamics of campaigns. Then, to further explore the in-depth implications of patterns (i.e., typical characters) in funding series, we propose to subdivide them into fast-growing and slow-growing ones. Moreover, for the purpose of switching from different kinds of patterns, the actor component of TC3 is extended with a structure of options, which comes to the TC3-Options. Finally, extensive experiments on the Indiegogo dataset not only demonstrate the effectiveness of our methods, but also validate our assumption that the entire pattern learned by TC3-Options is indeed the U-shaped one.

Recent years have witnessed a booming increase of patent applications, which provides an open chance for revealing the inner law of innovation, but in the meantime, puts forward higher requirements on patent mining techniques. Considering that patent mining highly relies on patent document analysis, this paper makes a focused study on constructing a technology portrait for each patent, i.e., to recognize technical phrases concerned in it, which can summarize and represent patents from a technology angle. To this end, we first give a clear and detailed description about technical phrases in patents based on various prior works and analyses. Then, combining characteristics of technical phrases and multi-level structures of patent documents, we develop an Unsupervised Multi-level Technical Phrase Extraction (UMTPE) model. Particularly, a novel evaluation metric called Information Retrieval Efficiency (IRE) is designed to evaluate the extracted phrases from a new perspective, which greatly supplements traditional metrics like Precision and Recall. Finally, extensive experiments on real-world patent data show the effectiveness of our UMTPE model.

Visual Emotion Recognition has attracted more and more research attention in recent years. Existing approaches mainly depend on facial expression or analyze the whole image between positive and negative. Actually, people can recognize multiple emotions from one image based on global and 10-cal information. In this paper, we propose a Context-Aware Generation-Based Net (CAGBN), a novel architecture that makes full use of global and local information of the image by considering both the whole image and details of the target person. Inspired by psychological studies that when viewing a person in his situation, we tend to give judgments gradually rather than assign all labels at the same time, CAGBN transforms the multi-label classification problem into a sequence generation task for better recognition. Extensive experimental results on the emotion recognition dataset demonstrate the superiority and rationality of CAGBN.

Recent years, Chinese text classification has attracted more and more research attention. However, most existing techniques which specifically aim at English materials may lose effectiveness on this task due to the huge difference between Chinese and English. Actually, as a special kind of hieroglyphics, Chinese characters and radicals are semantically useful but still unexplored in the task of text classification. To that end, in this paper, we first analyze the motives of using multiple granularity features to represent a Chinese text by inspecting the characteristics of radicals, characters and words. For better representing the Chinese text and then implementing Chinese text classification, we propose a novel Radicalaware Attention-based Four-Granularity (RAFG) model to take full advantages of Chinese characters, words, characterlevel radicals, word-level radicals simultaneously. Specifically, RAFG applies a serialized BLSTM structure which is context-aware and able to capture the long-range information to model the character sharing property of Chinese and sequence characteristics in texts. Further, we design an attention mechanism to enhance the effects of radicals thus model the radical sharing property when integrating granularities. Finally, we conduct extensive experiments, where the experimental results not only show the superiority of our model, but also validate the effectiveness of radicals in the task of Chinese text classification.