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Declarative sentences are known to be compatible with a wide range of different speech acts. The paper develops, for German, a proposal in which declarative sentences that express assertions, declarations (including explicit performatives), exclamations, wishes, commands, observations (or miratives), reminders and questions have slightly distinct syntactic representations and, consequently, semantic interpretations. The syntactic representation allows for epistemic and evidential modifications of assertions and for markers of assertive strength by speechact-related adverbials and by embedding verbal predicates that are usually analyzed as parenthetical. It also contains new proposals for evidential modal verbs and for the reportative subjunctive in German. Speech acts are interpreted as common ground updates that typically do not only involve an informative update but also a performative update that changes the worldtime indices of the common ground. This does not only hold for declarations but also for assertions and exclamations, where the former are analyzed as creating a commitment to the truth of a proposition, and the latter as committing to an emotional attitude. In contrast to proposals that relegate the illocutionary interpretation of declarative clauses largely to pragmatics, the paper develops a perspective in which it is predominantly a matter of syntactic structure and compositional interpretation. • Syntax and semantics of different interpretations of declarative sentences. • Assertions, declarations, exclamations, wishes, observations, reminders, questions. • Speech acts as updates of common ground. • Modifications of assertions by epistemic, evidential and commitment strength markers. [ABSTRACT FROM AUTHOR]

The current paper aims to discuss the impact of pre-posing a subordinate clause (as in connective p, q) on the overall meaning of a conjunction. This issue has traditionally been explored from a functional perspective and, more specifically, in relation to 'topic', 'framework' or other related notions. The current work reveals that, among the inadequacies that a topic-based account presents, it essentially falls short of a context-sensitive and unified perspective. In response to these inadequacies, a relevance-theoretic approach is proposed on procedural grounds. More specifically, it is argued that a sentence-initial subordinate clause, or, more accurately, the entire context associated with it, serves as evidence for the delivery of procedure r, namely the inference that the main-clause-proposition that may follow will be relevant to the foregoing pre-posed context. From a communicative point of view, the procedural impact of sentence-initial subordinate clauses is justified as a useful rhetorical instrument that serves to manipulate the hearer's epistemic assessment of the main-clause-proposition as genuinely relevant to the context associated with the (pre-posed) subordinate-clause proposition. • Exploration of the impact of a sentence-initial subordinate clause on a conjunction. • Discussion of the problems connected to traditional approaches of a functional origin. • Proposal of a procedural approach within a relevance-theoretic conception. • Discussion of the role of epistemic vigilance in employing sentence-initial clauses. [ABSTRACT FROM AUTHOR]

Bilmes's work in the last decade of his career was primarily concerned with the approach he developed and called Occasioned Semantics. Bilmes based OS on Sacks' (1995) membership categorisation work together with components of taxonomical analysis derived from ethno-semantics. While the approach was primarily aimed at the field of Semantics, Bilmes also argued that OS offered a way to develop upon Sacks' work by situating categorial inferencing within 'occasioned fields of meaning' within which categorial definitions and descriptions evolve through a taxonomic branching texture. In this paper we explore the potential analytic intersection between aspects of Bilmes' OS and Sacks' category analysis together with later developments in Membership Categorisation Analysis (MCA). In particular we revisit the 'consistency rule' together with 'omni-relevance', which provides a way of understanding shifts in fields of meaning as hierarchical and multi-layered. The combined approach allows to bring into view multi-layered sequential and categorial flow with taxonomic branching that takes place within an ongoing, unfolding and contingent interactional context of 'who-we-are-and-what-we-are-doing'. • Explore the analytic intersections of Occasioned Semantics and MCA. • Consider how combined OS and MCA can add to the analytic insights of each other. • Explore how emergent structures of meaning are co-constructed by participants. • Shed further light onto the routine layering of members' situated categorial practices. • Revisit the 'consistency rule' together with 'omni-relevance'. [ABSTRACT FROM AUTHOR]

The hype of Large Language Models manifests in disruptions, expectations or concerns in scientific communities that have focused for a long time on design-oriented research. The current experiences with Large Language Models and associated products (e.g. ChatGPT) lead to diverse positions regarding the foreseeable evolution of such products from the point of view of scholars who have been working with designed abstractions for most of their careers - typically relying on deterministic design decisions to ensure systems and automation reliability. Such expectations are collected in this paper in relation to a flavor of systems engineering that relies on explicit knowledge structures, introduced here as "semantics-driven systems engineering". The paper was motivated by the panel discussion that took place at CAiSE 2023 in Zaragoza, Spain, during the workshop on Knowledge Graphs for Semantics-driven Systems Engineering (KG4SDSE). The workshop brought together Conceptual Modeling researchers with an interest in specific applications of Knowledge Graphs and the semantic enrichment benefits they can bring to systems engineering. The panel context and consensus are summarized at the end of the paper, preceded by a proposed research agenda considering the expressed positions. [ABSTRACT FROM AUTHOR]

Traffic trajectories of various vehicles, bicycles and pedestrians can help understand the traffic dynamics in a fine-grained manner like traffic flow, traffic congestion and ride-hailing demand. The comprehensive usage of traffic trajectory data has not been fully investigated due to the prevalent privacy concerns and commercial limitations. The traffic trajectory simulation task has emerged to generate high-fidelity trajectories in demand for downstream tasks to fill the gap between the scarce trajectory data and the widespread applications. Previous state-of-the-art methods build the spatiotemporal dependencies of trajectories with Graph Neural Networks (GNNs) under generative adversarial training, yielding better yet unstable trajectory quality. We observe that the unsatisfied synthetic trajectories are caused by the insufficient spatiotemporal modeling of road networks and trajectory semantics. In this paper, we propose a novel S patio TE mporal GA ted (STEGA) framework equipped with semantic-aware graph learning for traffic trajectory simulation to enable the explicit modeling of spatiotemporal dependencies throughout the learning pipeline. On the one hand, STEGA employs a graph encoder with the semantics of road networks for the spatial points of a trajectory, together with a time encoder for the time points. On the other hand, STEGA devises two spatiotemporal gates with the semantic graphs for the predictions of the future trajectory. Boosted by the semantic-aware graph learning, the proposed STEGA outperforms the state-of-the-art counterparts consistently at both macro- and micro-level metrics on two datasets. Elaborate ablation studies and downstream tasks of the synthetic trajectories further demonstrate the superiority of STEGA. Our code is available at https://github.com/Star607/STEGA. • Semantic-aware graph learning is utilized throughout the entire pipeline. • Two spatiotemporal gates are devised with semantic graphs. • The proposed STEGA integrates GNN and powerful generative Transformer. • The proposed model outperforms all state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Aspect-level sentiment analysis is a more fine-grained task that aims to determine the sentiment polarity of specific aspects. Recent studies have employed graph attention networks and graph convolutional networks to model dependency trees, effectively establishing explicit associations between aspects and opinions, yielding promising performance. However, these methods have limitations in capturing complex linguistic features and the intricate dependencies between aspects and their contexts, resulting in suboptimal performance. In this paper, we propose a dual graph neural network that incorporates syntax and semantics, called IDGNN. Specifically, we utilize the relational graph attention network (RGAT) to encode the syntactic dependency tree and obtain syntactic information, while incorporating dependency labels to enhance aspect representation. Additionally, the semantic graph convolutional network (SemGCN) is employed to encode the self-attention matrix and capture semantic information, with the inclusion of orthogonal regularization to enhance semantic association. Furthermore, we introduce two fusion strategies based on gate mechanisms: the syntax fusion module (SYF) and the semantic fusion module (SEF). SYF combines contextual and syntactic representations to obtain global syntactic features, while SEF fuses semantic information with global syntactic features to obtain the final feature representation. Experimental results demonstrate that our proposed model achieves state-of-the-art performance on several benchmark datasets. • A dual graph neural network combining syntax and semantics for aspect-level sentiment analysis is proposed by us. • Introducing dependency labels in relational graph attention networks for enhancing syntactic dependency information. • Regularization is added to the semantic convolution module to increase the capacity of semantic information. • Syntactic aggregation and semantic aggregation are used to aggregate the output syntactic and semantic information, respectively. [ABSTRACT FROM AUTHOR]

Hotel reviews play a vital role in tourism recommender system. They should be analyzed effectively to enhance the accuracy of recommendations which can be generated either from crisp ratings on a fixed scale or real sentiments of reviews. But crisp ratings cannot represent the actual feelings of reviewers. Existing tourism recommender systems mostly recommend hotels on the basis of vague and sparse ratings resulting in inaccurate recommendations or preferences for online users. This paper presents a semantic approach to analyze the online reviews being crawled from tripadvisor.in. It discovers the underlying fuzzy semantics of reviews with respect to the multiple criteria of hotels rather than using the crisp ratings. The crawled reviews are preprocessed via data cleaning such as stopword and punctuation removal, tokenization, lemmatization, pos tagging to understand the semantics efficiently. Nouns representing frequent features of hotels are extracted from pre-processed reviews which are further used to identify opinion phrases. Fuzzy weights are derived from normalized frequency of frequent nouns and combined with sentiment score of all the synonyms of adjectives in the identified opinion phrases. This results in fuzzy semantics which form an ideal representation of reviews for a multi-criteria tourism recommender system. The proposed work is implemented in python by crawling the recent reviews of Jaipur hotels from TripAdvisor and analyzing their semantics. The resultant fuzzy semantics form a manually tagged dataset of reviews tagged with sentiments of identified aspects, respectively. Experimental results show improved sentiment score while considering all the synonyms of adjectives. The results are further used to fine-tune BERT models to form encodings for a query-based recommender system. The proposed approach can help tourism and hospitality service providers to take advantage of such sentiment analysis to examine the negative comments or unpleasant experiences of tourists and making appropriate improvements. Moreover, it will help online users to get better recommendations while planning their trips. [ABSTRACT FROM AUTHOR]

Domain relation extraction (DRE) aims to understand domain semantics of unstructured text to mine structural knowledge. However, most existing DRE models may fail to fully represent the domain entity that is usually absent in the corpus of pre-trained language models. In addition, almost all these models directly use contextual semantics for relation classification that ignore purifying the task relevant features. To address these challenging problems, we propose a novel and effective M ulti- M odal S emantics F usion (MMSF) model, which can automatically capture domain semantics to discover the important semantics contained in images, thus enhancing the representations of the domain entity by exploiting the multi-modal semantics. Afterwards, we introduce heterogeneous graph neural networks to learn contextual semantics. To obtain the task relevant features for DRE, we design a new purified feature architecture by exploring the mutual information between contextual semantics and the labels of domain relation. With the enhanced representations of the domain entity and purified the task relevant features, the satisfactory results of relation classification can be guaranteed. Extensive experiments on two real domain relation extraction datasets demonstrate the superiority of our proposed model over several state-of-the-art models. The source code and experiment details of this paper can be obtained from https://github.com/SWT-AITeam/MMSF. • Proposing a novel MMSF model for domain relation extraction. • The MMSF can enrich the representations of the domain entity. • We introduce the IB theory to extract task relevant features. • Our MMSF achieves the state-of-the-art. [ABSTRACT FROM AUTHOR]

Approximation Fixpoint Theory (AFT) and Justification Theory (JT) are two frameworks to unify logical formalisms. AFT studies semantics in terms of fixpoints of lattice operators, and JT in terms of so-called justifications, which are explanations of why certain facts do or do not hold in a model. While the approaches differ, the frameworks were designed with similar goals in mind, namely to study the different semantics that arise in (mainly) non-monotonic logics. The first contribution of our current paper is to provide a formal link between the two frameworks. To be precise, we show that every justification frame induces an approximator and that this mapping from JT to AFT preserves all major semantics. The second contribution exploits this correspondence to extend JT with a novel class of semantics, namely ultimate semantics : we formally show that ultimate semantics can be obtained in JT by a syntactic transformation on the justification frame, essentially performing a sort of resolution on the rules. [ABSTRACT FROM AUTHOR]

The growing ubiquity of location/activity sensing technologies has created unprecedented opportunities for research on human spatiotemporal interaction behavior in mobile environments. However, existing studies of human mobility need to sufficiently account for the association of indoor scenes with the semantics of human behavior. This paper introduces TSTM-in, a trajectory model that combines trajectory data and indoor scenes using topological semantic modeling, semantic trajectory reconstruction, and trajectory queries. The model effectively manages indoor semantic trajectory data and extracts topological behavioral semantics by incorporating important points across a trajectory to reflect the semantics of key points connected to indoor corridors and regions. These topological semantics facilitate the creation of a flexible intersection-based indoor semantic trajectory reconstruction. Reconstructed semantic trajectories represent human mobility by integrating semantic data sets along the time axis. A case study with real-world trajectory queries from travelers demonstrates the model's effectiveness. TSTM-in realizes the association of indoor scenes with human behavior semantics, supporting the construction of mobile object management applications for indoor scenes and providing scientific and reasonable spatiotemporal semantic information description for location service-based intelligent cities. • Introduced TSTM-in model for indoor semantic trajectory data, bridging qualitative and quantitative movement features. • Integrated topological invariants into indoor trajectory analysis, enhancing diverse indoor motion behaviors. • Demonstrated TSTM-in's efficacy and potential for indoor mobility data encoding applications through real-world study cases. [ABSTRACT FROM AUTHOR]

Dependency trees reflect rich structural information, which can effectively guide models to understand text semantics and are widely used for relation extraction. However, existing dependency-based models suffer from noise in dependency trees, which distorts the modeling of contextual information, particularly when introducing dependency types, further exacerbating the propagation and accumulation of errors. In addition, rule-based pruning strategies used to eliminate noise in dependency trees may lose crucial information for capturing relational patterns, whereas unconstrained learning-based pruning strategies may introduce more noisy edges. These two aspects of dependency trees may cause models to produce semantic confusion, which is detrimental to relation extraction. In this study, we propose a Contextual Dependency-Aware Graph Convolutional Network (C-DAGCN) that works on complete dependency trees and perceives the importance of word dependencies from multiple angles without considering the noise in dependency trees. This study includes three parts. First, the sequence model captures semantic information for the input sentence and its words. We considered the appropriate outputs as word and sentence-level representations based on model characteristics. Second, the structure model enhances the description of the sentence structure and its guidance. We consider that position knowledge implies associations between words; therefore, we propose the Current Word Global Relative Position Knowledge (CW-GRPK) to globally enhance text structure modeling and reduce the models' overreliance on single noisy structure guidance. A dependency-aware module weighs heuristic syntactic knowledge and non-heuristic CW-GRPK to capture fine-grained word-level interactions that effectively distinguish words related to relational semantics. Subsequently, a dependency-guided module, based on the results of the dependency-aware layer, encodes a knowledge-enhanced word graph without losing global linguistic patterns. Third, we constructed high-level linguistic representations based on the results of the sequence and structure model in C-DAGCN as a relational representation and mapped it to the relational decision space for relational prediction. The experimental results demonstrate that our method outperforms nine strong baseline models on two benchmark datasets. Compared with the best baseline Attentive Graph Convolutional Networks (A-GCN) model (Tian et al., 2021), our proposed C-DAGCN model with 90.15 macro-average F1 and 79.52 micro-average F1 achieves 0.28 macro-average F1 and 0.47 micro-average F1 improvement on the SemEval2010-Task8 and ACE2005 datasets, respectively. 1 1 Our code is available at https://github.com/dymyyc/RE%5fC-DAGCN. The released code when run produces results which are the same as those reported in this paper. • A kind of position knowledge globally enhances text structure modeling. • A dependency-aware module captures fine-grained word-level interactions. • Our model constructs a high-level linguistic representation for relation extraction. • Our model outperforms nine strong baselines on the two benchmark datasets. [ABSTRACT FROM AUTHOR]

Electric Health Records (EHR) have been widely adopted by many hospitals to improve clinical decision making and re-admission prediction. Each patient admission usually contains both multiple medical codes as well as clinical notes. Accurate learning of the representations (also called embeddings) of medical concepts from EHR is a key strategy to improve prediction performance in healthcare. Existing works employ medical ontologies to improve the quality of representations but focus solely on the relationships amongst the medical codes, ignoring textual data such medical code descriptions, clinical notes, and patient demographics. In this paper, we propose a new model called Semantic-based Attention model using Textual data for Medical Concept Embedding (SATexMCE). SATexMCE consists of three parts: medical codes embedding, admission embedding, and prediction model. First, we generate representations of medical codes by using both the textual description of medical codes and also the relationships among medical codes. Then, we generate the admission representation based on the representation of medical codes in the admission, the clinical notes and the demographic information associated with the admission via several attention mechanisms. The admission embeddings are used to construct a Recurrent neural network model which is used to predict patients' readmission and a disease in the next admission based on patient admission data. Experimental results show that the proposed SATexMCE model improves not only the performance of readmission prediction but also the quality of medical concept representations. Attention mechanism helps us measure the importance of different medical codes and understand the meaning of the words in clinical notes for predictions. • A attention model to learn medical code representations using code descriptions. • A method to learn medical code and admission representations using clinical notes. • A RNN-based classification model for readmission prediction and CKD prediction. • Analyses of the proposed medical code representations for the prediction tasks. [ABSTRACT FROM AUTHOR]

Sentence representation is one of the most fundamental research topics in natural language processing (NLP), as its quality directly affects various downstream task performances. Recent studies for sentence representations have established state-of-the-art (SOTA) performance on semantic representation tasks. However, embeddings by those approaches share unsatisfying transferability when applied to various specific applications. Seldom work studies the transferability of semantic sentence embeddings. In this paper, we first explore the transferability characteristic of the sentence embeddings, and present BlendCSE, a new sentence embedding model targeting rich semantics and transferability. BlendCSE blends three recent advanced NLP learning methodologies, namely, continue learning on masked language modeling (MLM), contrastive learning (CL) with data augmentations (DA), and semantic supervised learning. The main objectives of BlendCSE are to capture token/word level information, diversified linguistic properties, and sentence semantics, respectively. Empirical studies demonstrate that BlendCSE captures semantics comparably well on STS tasks, yet surpasses existing methods on various transfer tasks, yielding even stronger transferability on document-level applications. Ablation studies verified that the three learning objectives synergy well to capture semantics and transferability effectively. • Sentence representation for rich semantics and strong transferability. • Sentence representation via contrastive learning compatible with data augmentation. • Blends supervised, self-supervised learning and masked language modeling. • Achieves state-of-the-art for downstream tasks and document applications. [ABSTRACT FROM AUTHOR]

The community question-answering system has gradually replaced the search engine as the primary way for people to acquire and share knowledge. Users' interactive behavior establishes complex relationships among multiple entities in the question-answering community. With the accumulation of question-and-answer data, retrieving relevant answers to a new question is time-consuming. Some researchers have conducted related research on answer recommendation, mainly dependent on word-based question–answer literal matching, ignoring text semantics and connected entity relationships. In this paper, we propose a Community Answer Recommendation Model Based on Heterogeneous Semantic Fusion, called CARHSF, which combines text semantic and multi-aspect features to enhance answer recommendations. Firstly, we employ the PCFG to build parse trees of text and extract hierarchical phrases through the iterative traversal method. Meanwhile, we utilize phrase embedding to efficiently quantify semantic relatedness. Secondly, we construct a heterogeneous information network to fuse text semantics and entity-relationship features. Finally, we represent entity relations through the heterogeneous graph neural network and supply the recommended answers based on entity correlations. Comparative experiments on three real datasets demonstrate that CARHSF has advantages in answer recommendation tasks. [ABSTRACT FROM AUTHOR]

In recent years, Aspect-based Sentiment Analysis (ABSA) has been a crucial yet challenging task in recognizing textual emotions from text. ABSA has numerous application across various fields, such as social media, commodity review, and movie comment, making it an attractive area of research. Many researchers are working to develop more powerful sentiment analysis models. Currently, most existing ABSA models use the generic pre-trained language models (PLMs) based fine-tuning paradigm, which only utilizes the encoder parameters while discarding the decoder parameters of PLMs. However, this approach fails to leverage the prior knowledge revealed in PLMs effectively. To address these issues, we investigate the potential of the initial pre-training scheme of PLMs to conduct ABSA and thus propose a novel approach in this paper, namely Target Word Transferred ABSA (WordTransABSA). In WordTransABSA, we propose "Word Transferred LM", a novel sequence-level optimization strategy that transferred target words in sentence into pivot tokens to stimulate better PLM semantic understanding capability. Given a sentence with aspect terms as input, WordTransABSA generates contextually appropriate semantics and predicts the affective tokens on the corresponding positions of the aspect terms. The final sentiment polarity of each aspect term is determined through several sentiment identification strategies that we selected. WordTransABSA takes full advantage of the versatile linguistic knowledge of Pre-trained Language Model, resulting in competitive accuracy compared with recent baselines. The WordTransABSA demonstrates its superiority and effectiveness through extensive experiments in both data-sufficient (full-data supervised learning) and data-insufficient (few-shot learning) scenarios. We have made our code publicly available on GitHub: https://github.com/albert-jin/WordTransABSA. • We present WordTransABSA, introducing a "Word Transferred LM" strategy for ABSA. • Various strategies are explored for searching sentiment-related pivot tokens. • Data-sufficient/scarce experiments validate the effectiveness of WordTransABSA. • Stimulating PLM pre-training paradigm is proved to be a better solution for ABSA. [ABSTRACT FROM AUTHOR]

In the field of video captioning, recent works usually focus on multi-modal video content understanding, in which transcripts are extracted from speech and are often adopted as an informational supplement. However, most existing works only consider transcripts as a supplementary modality, neglecting their potential in capturing high-level semantics, such as multi-modal topics . In fact, transcripts, as a textual attribute derived from the video, reflect the same high-level topics as the video content. Nonetheless, how to resolve the heterogeneity of multi-modal topics is still under-investigated and worth exploring. In this paper, we introduce a contrastive topic-enhanced network to consistently model heterogeneous topics, that is, inject an alignment module in advance, to learn a comprehensive latent topic space and guide caption generation. Specifically, our method includes a local semantic alignment module and a global topic fusion module. In the local semantic alignment module, a fine-grained semantic alignment at the clip-sentence granularity reduces the semantic gap between modalities. Extensive experiments have verified the effectiveness of our solution. [ABSTRACT FROM AUTHOR]

• Proposing an iterative and incremental knowledge management model for problem solving. • Developing a new search intent predicting method. • Designing a knowledge learning strategy addressing knowledge visualization techniques. • Achieving an interactive healthcare knowledge management system. Healthcare is a knowledge intensive process, which requires large amount of information from various healthcare knowledge providers. However, this growing information makes challengeable for users in finding useful healthcare information to support their healthcare decision making. Knowledge management and knowledge management systems are drawing increasing attention in healthcare community. This paper proposes an online healthcare knowledge management framework and system prototype, featured with integrating knowledge learning and exploratory search for solving problems efficiently. Inspired by the knowledge to action framework in healthcare and the iterative process in software engineering, an iterative and incremental knowledge management model is designed to organize the activities during the knowledge learning and searching procedure. Supported by the knowledge-enhanced infrastructure of online healthcare information designed, domain knowledge learning and task knowledge learning environment can work collaboratively and interactively to improve the result of learning and exploratory search. Knowledge base also provides helpful information in an interactive way for solving problems. Given exploiting probabilistic information retrieval model and concept semantics described in Description Logics, a search intent discovery method and search intent guided learning and searching mechanism are designed within this environment for achieving the learning and search goal. The results of the experiments have proved the efficacy of the methods and process model used in the leaning-focused healthcare knowledge management system. [ABSTRACT FROM AUTHOR]

Sarcasm embodies a linguistic phenomenon that highlights a significant incongruity between the literal meanings of words and intended attitudes. With the proliferation of image–text content on social media, the task of multi-modal sarcasm detection (MSD) has gained considerable attention recently. Tremendous progress have been made in developing better MSD models, primarily relying on a straightforward extract-then-fuse paradigm. However, such a setting encounters two potential challenges. First, the utilization of separately pre-trained unimodal models for extracting visual and textual features frequently lacks the fundamental alignment capabilities required for effective multimodal data integration. Second, the detrimental modality gaps between vision and language make it challenging to comprehensively integrate multi-modal information solely via diverse cross-modal fusion techniques. Consequently, this poses a prominent challenge in further capturing cross-modal incongruity and improving the effectiveness of MSD. In this paper, we propose a Mu lti-modal Mu tual Learning (MuMu) network to tackle these issues. Specifically, we initialize the MuMu network with image and text encoders from the large-scale Contrastive Language-Image Pretraining model to enhance the underlying image–text correspondence. Moreover, to improve the capability of capturing cross-modal inconsistency during the fusion process, we design an align-fuse-collaborate mechanism to align disparate modalities before fusion and enhance the collaborative modeling ability between the two modalities with mutual learning after fusion. The proposed MuMu achieves new state-of-the-art results on a public dataset, demonstrating a substantial improvement of approximately 3% to 9% in terms of accuracy, micro-F1, and macro-F1 scores. • Proposing a multi-modal mutual learning network for multi-modal sarcasm detection. • Designing a novel paradigm of align-fuse-collaborate for improving cross-modal fusion. • Achieving the state-of-the-art results on a public dataset with a notable improvement. [ABSTRACT FROM AUTHOR]

Knowledge representation in the Materials Science and Engineering (MSE) domain is a vast and multi-faceted challenge: Overlap, ambiguity, and inconsistency in terminology are common. Invariant (consistent) and variant (context-specific) knowledge are difficult to align cross-domain. Generic top-level semantic terminology often is too abstract, while MSE domain terminology often is too specific. In this paper, an approach how to maintain a comprehensive MSE-centric terminology composing a mid-level ontology–the Platform MaterialDigital Core Ontology (PMDco)–via MSE community-based curation procedures is presented. The illustrated findings show how the PMDco bridges semantic gaps between high-level, MSE-specific, and other science domain semantics. Additionally, it demonstrates how the PMDco lowers development and integration thresholds. Moreover, the research highlights how to fuel it with real-world data sources ranging from manually conducted experiments and simulations with continuously automated industrial applications. • The PMD Core Ontology (PMDco) bridges semantic gaps in Materials Science and Engineering, enabling data interoperability. • The PMDco supports the FAIR principles and enables materials and process data conversion into machine-processable format. • Community-driven curation ensures PMDco's usability and evolution, with applications in domain-specific ontology development. • The PMDco serves as a valuable resource, fostering scientific discovery, innovation, and sustainable research practices. [ABSTRACT FROM AUTHOR]

Accurate assignment of meaning to a word based on its context, known as Word Sense Disambiguation (WSD), remains challenging across languages. Extensive research aims to develop automated methods for determining word senses in different contexts. However, the literature lacks the presence of datasets generated for the Arabic language WSD. This paper presents a dataset comprising a hundred polysemous Arabic words. Each word in the dataset encompasses 3–8 distinct senses, with ten example sentences per sense. Some statistical operations are conducted to gain insights into the dataset, enlightening its characteristics and properties. Subsequently, a novel WSD approach is proposed to utilize similarity measures and find the overlap between contextual information and dictionary definitions. The proposed method uses the power of BERT, a pre-trained language model, to enable effective Arabic word disambiguation. In training, new features are integrated to improve the model's ability to differentiate between various senses of words. The proposed BERT models are combined to compose an ensemble model architecture to improve the classification performances. The performance of the WSD system outperforms state-of-the-art systems, achieving an approximate F1-score of 96 %. Statistical analyses are performed to evaluate the overall performance of the WSD approach by providing additional information on model predictions. A case study was implemented to test the effectiveness of WSD in sentiment analysis, a downstream task. [ABSTRACT FROM AUTHOR]