Word reuse and combination support efficient communication of emerging concepts.

A key function of the lexicon is to express novel concepts as they emerge over time through a process known as lexicalization. The most common lexicalization strategies are the reuse and combination of existing words, but they have typically been studied separately in the areas of word meaning extension and word formation. Here, we offer an information-theoretic account of how both strategies are constrained by a fundamental tradeoff between competing communicative pressures: Word reuse tends to preserve the average length of word forms at the cost of less precision, while word combination tends to produce more informative words at the expense of greater word length. We test our proposal against a large dataset of reuse items and compounds that appeared in English, French, and Finnish over the past century. We find that these historically emerging items achieve higher levels of communicative efficiency than hypothetical ways of constructing the lexicon, and both literal reuse items and compounds tend to be more efficient than their nonliteral counterparts. These results suggest that reuse and combination are both consistent with a unified account of lexicalization grounded in the theory of efficient communication.

Research on the sentiment recognition and application of allusive words based on text semantic enhancement.

In the era of digital intelligence empowerment, the data-driven approach to the mining and organization of humanistic knowledge has ushered in new development opportunities. However, current research on allusions, an important type of humanities data, mainly focuses on the adoption of a traditional paradigm of humanities research. Conversely, little attention is paid to the application of auto-computing techniques to allusive resources. In light of this research gap, this work proposes a model of allusive word sentiment recognition and application based on text semantic enhancement. First, explanatory texts of 36,080 allusive words are introduced for text semantic enhancement. Subsequently, the performances of different deep learning-based approaches are compared, including three baselines and two optimized models. The best model, ERNIE-RCNN, which exhibits a 6.35% improvement in accuracy, is chosen for the sentiment prediction of allusive words based on text semantic enhancement. Next, according to the binary relationships between allusive words and their source text, explanatory text, and sentiments, the overall and time-based distribution regularities of allusive word sentiments are explored. In addition, the sentiments of the source text are inferred according to the allusive word sentiments. Finally, the LDA model is utilized for the topic extraction of allusive words, and the sentiments and topics are fused to construct an allusive word-sentiment theme relationship database, which provides two modes for the semantic association and organization of allusive resources. The empirical results show that the proposed model can achieve the discovery and association of allusion-related humanities knowledge.

Semantic-guided attention and adaptive gating for document-level relation extraction.

In natural language processing, document-level relation extraction is a complex task that aims to predict the relationships among entities by capturing contextual interactions from an unstructured document. Existing graph- and transformer-based models capture long-range relational facts across sentences. However, they still cannot fully exploit the semantic information from multiple interactive sentences, resulting in the exclusion of influential sentences for related entities. To address this problem, a novel Semantic-guided Attention and Adaptively Gated (SAAG) model is developed for document-level relation extraction. First, a semantic-guided attention module is designed to guide sentence representation by assigning different attention scores to different words. The multihead attention mechanism is then used to capture the attention of different subspaces further to generate a document context representation. Finally, the SAAG model exploits the semantic information by leveraging a gating mechanism that can dynamically distinguish between local and global contexts. The experimental results demonstrate that the SAAG model outperforms previous models on two public datasets.

Language Matters: The Semantics and Politics of "Assisted Dying".

This essay examines the impact of linguistic choices on the perception and regulation of assisted dying, particularly in Canada. It argues that euphemistic terms like "medical assistance in dying" and its acronym, "MAID," serve to normalize the practice, potentially obscuring its moral gravity. This contrasts with what is seen in Belgium and the Netherlands, where terms like "euthanasia" are used, as well as in France and the United Kingdom, where terminology remains divisive and contested. By tracing the evolution of these terms and what they reveal about different cultural and legal approaches, this essay sheds light on the politics of language in end-of-life discourses. It suggests that the shift toward euphemistic language reflects a broader discomfort with death that can shape public attitudes and legal frameworks. It calls for a more transparent, philosophically grounded approach to terminology and suggests that continued debate about semantics is necessary to capture the complexities and ethical significance of assisted dying.

Language at a glance: How our brains grasp linguistic structure from parallel visual input.

Human brains grasp the gists of visual scenes from a single glance, but to what extent is this possible for language? While we typically think of language in terms of sequential speech, our everyday experience involves numerous rapidly flashing written notifications, which we understand instantly. What do our brains detect in the first few hundred milliseconds after seeing such a stimulus? We flashed short sentences during magnetoencephalography measurement, revealing sentence-sensitive neural activity in left temporal cortex within 130 milliseconds. These signals emerged for subject-verb-object sentences regardless of grammatical or semantic well-formedness, suggesting that at-a-glance language comprehension begins by detecting basic phrase structure, independent of meaning or other grammatical details. Our findings unveil one aspect of how our brains process information rapidly in today's visually saturated world.

Leveraging weak complementary labels enhances semantic segmentation of hepatocellular carcinoma and intrahepatic cholangiocarcinoma.

In this paper we present a deep learning segmentation approach to classify and quantify the two most prevalent primary liver cancers - hepatocellular carcinoma and intrahepatic cholangiocarcinoma - from hematoxylin and eosin (H&E) stained whole slide images. While semantic segmentation of medical images typically requires costly pixel-level annotations by domain experts, there often exists additional information which is routinely obtained in clinical diagnostics but rarely utilized for model training. We propose to leverage such weak information from patient diagnoses by deriving complementary labels that indicate to which class a sample cannot belong to. To integrate these labels, we formulate a complementary loss for segmentation. Motivated by the medical application, we demonstrate for general segmentation tasks that including additional patches with solely weak complementary labels during model training can significantly improve the predictive performance and robustness of a model. On the task of diagnostic differentiation between hepatocellular carcinoma and intrahepatic cholangiocarcinoma, we achieve a balanced accuracy of 0.91 (CI 95%: 0.86-0.95) at case level for 165 hold-out patients. Furthermore, we also show that leveraging complementary labels improves the robustness of segmentation and increases performance at case level.

LLM-Enhanced multimodal detection of fake news.

Fake news detection is growing in importance as a key topic in the information age. However, most current methods rely on pre-trained small language models (SLMs), which face significant limitations in processing news content that requires specialized knowledge, thereby constraining the efficiency of fake news detection. To address these limitations, we propose the FND-LLM Framework, which effectively combines SLMs and LLMs to enhance their complementary strengths and explore the capabilities of LLMs in multimodal fake news detection. The FND-LLM framework integrates the textual feature branch, the visual semantic branch, the visual tampering branch, the co-attention network, the cross-modal feature branch and the large language model branch. The textual feature branch and visual semantic branch are responsible for extracting the textual and visual information of the news content, respectively, while the co-attention network is used to refine the interrelationship between the textual and visual information. The visual tampering branch is responsible for extracting news image tampering features. The cross-modal feature branch enhances inter-modal complementarity through the CLIP model, while the large language model branch utilizes the inference capability of LLMs to provide auxiliary explanation for the detection process. Our experimental results indicate that the FND-LLM framework outperforms existing models, achieving improvements of 0.7%, 6.8% and 1.3% improvements in overall accuracy on Weibo, Gossipcop, and Politifact, respectively.

Predicting Invasiveness in Lepidic Pattern Adenocarcinoma of Lung: Analysis of Visual Semantic and Radiomic Features.

OBJECTIVES: To differentiate invasive lepidic predominant adenocarcinoma (iLPA) from adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) of lung utilizing visual semantic and computer-aided detection (CAD)-based texture features on subjects initially diagnosed as AIS or MIA with CT-guided biopsy. MATERIALS AND METHODS: From 2011 to 2017, all patients with CT-guided biopsy results of AIS or MIA who subsequently underwent resection were identified. CT scan before the biopsy was used to assess visual semantic and CAD texture features, totaling 23 semantic and 95 CAD-based quantitative texture variables. The least absolute shrinkage and selection operator (LASSO) method or forward selection was used to select the most predictive feature and combination of semantic and texture features for detection of invasive lung adenocarcinoma. RESULTS: Among the 33 core needle-biopsied patients with AIS/MIA pathology, 24 (72.7%) had invasive LPA and 9 (27.3%) had AIS/MIA on resection. On CT, visual semantic features included 21 (63.6%) part-solid, 5 (15.2%) pure ground glass, and 7 (21.2%) solid nodules. LASSO selected seven variables for the model, but all were not statistically significant. "Volume" was found to be statistically significant when assessing the correlation between independent variables using the backward selection technique. The LASSO selected "tumor_Perc95", "nodule surround", "small cyst-like spaces", and "volume" when assessing the correlation between independent variables. CONCLUSIONS: Lung biopsy results showing noninvasive LPA underestimate invasiveness. Although statistically non-significant, some semantic features showed potential for predicting invasiveness, with septal stretching absent in all noninvasive cases, and solid consistency present in a significant portion of invasive cases.

The Association Between Plasma Amyloid-ß 42/40 and Percentage of Semantic Intrusion Errors in Mild Cognitive Impairment.

Background: Semantic intrusion errors (SIEs) are both sensitive and specific to PET amyloid-ß (Aß) burden in older adults with amnestic mild cognitive impairment (aMCI). Objective: Plasma Aß biomarkers including the Aß42/40 ratio using mass spectrometry are expected to become increasingly valuable in clinical settings. Plasma biomarkers are more clinically informative if linked to cognitive deficits that are salient to Alzheimer's disease (AD). Methods: This study included 119 older adults enrolled in the 1Florida Alzheimer's Disease Research Center (ADRC), 45 aMCI participants scored below the established Aß42/40 ratio cut-off of 0.160 using the Quest AD-Detect™ assay indicating Aß positivity (Aß+), while 50 aMCI participants scored above this cut-off indicating Aß negative status (Aß-). Additionally, 24 cognitively unimpaired (CU) persons scored above the cut-off of 0.160 (Aß-). Results: The aMCI plasma Aß+ group evidenced the greatest percentage of SIEs, followed by the aMCI Aß-. The CU Aß- group exhibited the lowest percentage of SIEs. After adjustment for global cognitive impairment, aMCI plasma Aß+ continued to demonstrate greater SIEs on tests tapping the failure to recover from proactive semantic interference (frPSI) as compared to the aMCI Aß-group. Using pre-established cut-offs for frPSI impairment, 8.3% of CU Aß- participants evidenced deficits, compared to 37.8% of aMCI Aß-, and 74.0% of aMCI Aß+. Conclusions: SIEs reflecting frPSI were associated with aMCI Aß+ status based on the Aß42/40 ratio. Results suggest the importance of SIEs as salient cognitive markers that map onto underlying AD pathology in the blood.

Contributions of Speaking, Listening, and Semantic Depth to Word Learning in Typical 3- and 4-Year-Olds.

PURPOSE: This study examined learning via perception, learning via production, and semantic depth as contributors to word learning in preschool-aged children. There is broad evidence that semantic depth is an important contributor to word learning, especially when semantic cues are repeated and spaced out over time. Perceptual learning and production learning each support word learning sometimes, but not in all cases. The purpose of this study was to examine all three learning mechanisms within a single experimental paradigm. METHOD: Thirty-six typically developing preschool children completed the experiment. They were familiarized with 16 novel words that were contextualized as alien names. These aliens came in four sets, each set comprising one base alien and three modified aliens marked by suffixes. Children completed four familiarizations: two in which they simply listened to the alien names (perceptual learning) and two where they repeated the alien names (production learning). Those conditions were crossed with a semantic depth manipulation (aliens with and without verbal semantic cues). Following each familiarization, referent identification and confrontation naming tasks were completed to assess learning. RESULTS: Children were able to identify more alien referents following familiarizations with semantic depth. There were no significant effects of either perceptual learning or production learning. CONCLUSIONS: This study confirms and expands on the benefits of semantic depth, but the results are unclear about the relative importance of perception and production to word learning. Nevertheless, the study suggests benefits to simultaneously studying multiple factors related to word learning.

Enhancing bridge damage detection with Mamba-Enhanced HRNet for semantic segmentation.

With the acceleration of urbanization, bridges, as crucial infrastructure, their structural health and stability are paramount to public safety. This paper proposes Mamba-Enhanced HRNet for bridge damage detection. Mamba-Enhanced HRNet integrates the advantages of HRNet's multi-resolution parallel design and VMamba's visual state space model. By replacing the residual convolutional blocks in HRNet with a combination of VSS blocks and convolution, this model enhances the network's capability to capture global contextual information while maintaining computational efficiency. This work builds an extensive dataset with multiple damage kinds and uses Mean Intersection over Union (Mean IoU) as the assessment metric to assess the performance of Mamba-Enhanced HRNet. Experimental results demonstrate that Mamba-Enhanced HRNet achieves significant performance improvements in bridge damage semantic segmentation tasks, with Mean IoU scores of 0.963, outperforming several other semantic segmentation models.

Biomedical relation extraction method based on ensemble learning and attention mechanism.

BACKGROUND: Relation extraction (RE) plays a crucial role in biomedical research as it is essential for uncovering complex semantic relationships between entities in textual data. Given the significance of RE in biomedical informatics and the increasing volume of literature, there is an urgent need for advanced computational models capable of accurately and efficiently extracting these relationships on a large scale. RESULTS: This paper proposes a novel approach, SARE, combining ensemble learning Stacking and attention mechanisms to enhance the performance of biomedical relation extraction. By leveraging multiple pre-trained models, SARE demonstrates improved adaptability and robustness across diverse domains. The attention mechanisms enable the model to capture and utilize key information in the text more accurately. SARE achieved performance improvements of 4.8, 8.7, and 0.8 percentage points on the PPI, DDI, and ChemProt datasets, respectively, compared to the original BERT variant and the domain-specific PubMedBERT model. CONCLUSIONS: SARE offers a promising solution for improving the accuracy and efficiency of relation extraction tasks in biomedical research, facilitating advancements in biomedical informatics. The results suggest that combining ensemble learning with attention mechanisms is effective for extracting complex relationships from biomedical texts. Our code and data are publicly available at: https://github.com/GS233/Biomedical .

Semantic fMRI neurofeedback of emotions: from basic principles to clinical applications.

During fMRI neurofeedback participants learn to self-regulate activity in relevant brain areas and networks based on ongoing feedback extracted from measured responses in those regions. This closed-loop approach has been successfully applied to reduce symptoms in mood disorders such as depression by showing participants a thermometer-like display indicating the strength of activity in emotion-related brain areas. The hitherto employed conventional neurofeedback is, however, 'blind' with respect to emotional content, i.e. patients instructed to engage in a specific positive emotion could drive the neurofeedback signal by engaging in a different (positive or negative) emotion. In this future perspective, we present a new form of neurofeedback that displays semantic information of emotions to the participant. Semantic information is extracted online using real-time representational similarity analysis of emotion-specific activity patterns. The extracted semantic information can be provided to participants in a two-dimensional semantic map depicting the current mental state as a point reflecting its distance to pre-measured emotional mental states (e.g. 'happy', 'content', 'sad', 'angry'). This new approach provides transparent feedback during self-regulation training, and it has the potential to enable more specific training effects for future therapeutic applications such as clinical interventions in mood disorders.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

Midlife dynamics of white matter architecture in lexical production.

We aimed to examine the white matter changes associated with lexical production difficulties, beginning in midlife with increased naming latencies. To delay lexical production decline, middle-aged adults may rely on domain-general and language-specific compensatory mechanisms proposed by the LARA model (Lexical Access and Retrieval in Aging). However, the white matter changes supporting these mechanisms remains largely unknown. Using data from the CAMCAN cohort, we employed an unsupervised and data-driven methodology to examine the relationships between diffusion-weighted imaging and lexical production. Our findings indicate that midlife is marked by alterations in brain structure within distributed dorsal, ventral, and anterior cortico-subcortical networks, marking the onset of lexical production decline around ages 53-54. Middle-aged adults may initially adopt a "semantic strategy" to compensate for lexical production challenges, but this strategy seems compromised later (ages 55-60) as semantic control declines. These insights underscore the interplay between domain-general and language-specific processes in the trajectory of lexical production performance in healthy aging and hint at potential biomarkers for language-related neurodegenerative pathologies.

BAMRE: Joint extraction model of Chinese medical entities and relations based on Biaffine transformation with relation attention.

Electronic Health Records (EHRs) contain various valuable medical entities and their relationships. Although the extraction of biomedical relationships has achieved good results in the mining of electronic health records and the construction of biomedical knowledge bases, there are still some problems. There may be implied complex associations between entities and relationships in overlapping triplets, and ignoring these interactions may lead to a decrease in the accuracy of entity extraction. To address this issue, a joint extraction model for medical entity relations based on a relation attention mechanism is proposed. The relation extraction module identifies candidate relationships within a sentence. The attention mechanism based on these relationships assigns weights to contextual words in the sentence that are associated with different relationships. Additionally, it extracts the subject and object entities. Under a specific relationship, entity vector representations are utilized to construct a global entity matching matrix based on Biaffine transformations. This matrix is designed to enhance the semantic dependencies and relational representations between entities, enabling triplet extraction. This allows the two subtasks of named entity recognition and relation extraction to be interrelated, fully utilizing contextual information within the sentence, and effectively addresses the issue of overlapping triplets. Experimental observations from the CMeIE Chinese medical relation extraction dataset and the Baidu2019 Chinese dataset confirm that our approach yields the superior F1 score across all cutting-edge baselines. Moreover, it offers substantial performance improvements in intricate situations involving diverse overlapping patterns, multitudes of triplets, and cross-sentence triplets.

Emerging Roots: Investigating Early Access to Meaning in Maltese Auditory Word Recognition.

In Semitic languages, the consonantal root is central to morphology, linking form and meaning. While psycholinguistic studies highlight its importance in language processing, the role of meaning in early lexical access and its representation remain unclear. This study investigates when meaning becomes accessible during the processing of Maltese verb forms, using a computational model based on the Discriminative Lexicon framework. Our model effectively comprehends and produces Maltese verbs, while also predicting response times in a masked auditory priming experiment. Results show that meaning is accessible early in lexical access and becomes more prominent after the target word is fully processed. This suggests that semantic information plays a critical role from the initial stages of lexical access, refining our understanding of real-time language comprehension. Our findings contribute to theories of lexical access and offer valuable insights for designing priming studies in psycholinguistics. Additionally, this study demonstrates the potential of computational models in investigating the relationship between form and meaning in language processing.

Complex Words as Shortest Paths in the Network of Lexical Knowledge.

Lexical models diverge on the question of how to represent complex words. Under the morpheme-based approach, each morpheme is treated as a separate unit, while under the word-based approach, morphological structure is derived from complex words. In this paper, we propose a new computational model of morphology that is based on graph theory and is intended to elaborate the word-based network approach. Specifically, we use a key concept of network science, the notion of shortest path, to investigate how complex words are learned, stored, and processed. The notion of shortest path refers to the task of finding the shortest or most optimal path connecting two non-adjacent nodes in a network. Building on this notion, the current study shows (i) that new complex words can be segmented into morphemes through the shortest path analysis; (ii) that attested English words tend to represent the shortest paths in the morphological network; and (iii) that novel (unattested) words receive higher acceptability ratings in experiments when they are formed along established optimal paths. The model's performance is tested in two experiments with human participants as well as against the behavioral data from the English Lexicon Project. We interpret our empirical results from the perspective of a usage-based model of grammar and argue that network science provides a powerful tool for analyzing language structure.

Probing the link between vision and language in material perception using psychophysics and unsupervised learning.

We can visually discriminate and recognize a wide range of materials. Meanwhile, we use language to describe what we see and communicate relevant information about the materials. Here, we investigate the relationship between visual judgment and language expression to understand how visual features relate to semantic representations in human cognition. We use deep generative models to generate images of realistic materials. Interpolating between the generative models enables us to systematically create material appearances in both well-defined and ambiguous categories. Using these stimuli, we compared the representations of materials from two behavioral tasks: visual material similarity judgments and free-form verbal descriptions. Our findings reveal a moderate but significant correlation between vision and language on a categorical level. However, analyzing the representations with an unsupervised alignment method, we discover structural differences that arise at the image-to-image level, especially among ambiguous materials morphed between known categories. Moreover, visual judgments exhibit more individual differences compared to verbal descriptions. Our results show that while verbal descriptions capture material qualities on the coarse level, they may not fully convey the visual nuances of material appearances. Analyzing the image representation of materials obtained from various pre-trained deep neural networks, we find that similarity structures in human visual judgments align more closely with those of the vision-language models than purely vision-based models. Our work illustrates the need to consider the vision-language relationship in building a comprehensive model for material perception. Moreover, we propose a novel framework for evaluating the alignment and misalignment between representations from different modalities, leveraging information from human behaviors and computational models.

Semantic inhibition impairment in college students with depressive states as evidenced by EEG and pupillometry during the hayling task.

In this study we tested whether depression is associated with impaired semantic inhibition, resulting in symptoms of rumination and anhedonia. For this purpose and using the Beck Depression Inventory II (BDI-II) college students with depressive states (DEP) and matched controls (CTL) performed a Hayling's task, while EEG and pupillometry measures were recorded. Participants were asked to complete sentential contexts with either a highly associated word (initiation) or a non-related word (inhibition), in response to randomly presented trial-by-trial cues. The DEP group, compared to the CTL group, showed lower performance, and reduced frontal negativity (N450) in inhibition trials. Source analyses revealed greater activation for inhibition trials than for initiation trials in bilateral orbitofrontal cortex for the CTL group, but the difference was reduced and more left lateralized for the DEP group. In addition, the DEP group showed more pupil size reactivity to inhibition trials than the CTL group, indicating higher cognitive effort during semantic inhibition. Finally, self-reported rumination and anhedonia correlated with N450 in inhibition trials, and rumination correlated with pupil dilation. Overall, this research contributes to understanding the neural underpinnings of impaired semantic inhibition in individuals with depression, with potential clinical applications.

Exploring online public survey lifestyle datasets with statistical analysis, machine learning and semantic ontology.

Lifestyle diseases significantly contribute to the global health burden, with lifestyle factors playing a crucial role in the development of depression. The COVID-19 pandemic has intensified many determinants of depression. This study aimed to identify lifestyle and demographic factors associated with depression symptoms among Indians during the pandemic, focusing on a sample from Kolkata, India. An online public survey was conducted, gathering data from 1,834 participants (with 1,767 retained post-cleaning) over three months via social media and email. The survey consisted of 44 questions and was distributed anonymously to ensure privacy. Data were analyzed using statistical methods and machine learning, with principal component analysis (PCA) and analysis of variance (ANOVA) employed for feature selection. K-means clustering divided the pre-processed dataset into five clusters, and a support vector machine (SVM) with a linear kernel achieved 96% accuracy in a multi-class classification problem. The Local Interpretable Model-agnostic Explanations (LIME) algorithm provided local explanations for the SVM model predictions. Additionally, an OWL (web ontology language) ontology facilitated the semantic representation and reasoning of the survey data. The study highlighted a pipeline for collecting, analyzing, and representing data from online public surveys during the pandemic. The identified factors were correlated with depressive symptoms, illustrating the significant influence of lifestyle and demographic variables on mental health. The online survey method proved advantageous for data collection, visualization, and cost-effectiveness while maintaining anonymity and reducing bias. Challenges included reaching the target population, addressing language barriers, ensuring digital literacy, and mitigating dishonest responses and sampling errors. In conclusion, lifestyle and demographic factors significantly impact depression during the COVID-19 pandemic. The study's methodology offers valuable insights into addressing mental health challenges through scalable online surveys, aiding in the understanding and mitigation of depression risk factors.