We introduce a novel dataset of affective, semantic, and descriptive norms for all facial emojis at the point of data collection. We gathered and examined subjective ratings of emojis from 138 German speakers along five essential dimensions: valence, arousal, familiarity, clarity, and visual complexity. Additionally, we provide absolute frequency counts of emoji use, drawn from an extensive Twitter corpus, as well as a much smaller WhatsApp database. Our results replicate the well-established quadratic relationship between arousal and valence of lexical items, also known for words. We also report associations among the variables: for example, the subjective familiarity of an emoji is strongly correlated with its usage frequency, and positively associated with its emotional valence and clarity of meaning. We establish the meanings associated with face emojis, by asking participants for up to three descriptions for each emoji. Using this linguistic data, we computed vector embeddings for each emoji, enabling an exploration of their distribution within the semantic space. Our description-based emoji vector embeddings not only capture typical meaning components of emojis, such as their valence, but also surpass simple definitions and direct emoji2vec models in reflecting the semantic relationship between emojis and words. Our dataset stands out due to its robust reliability and validity. This new semantic norm for face emojis impacts the future design of highly controlled experiments focused on the cognitive processing of emojis, their lexical representation, and their linguistic properties.

With technological innovations, enterprises in the real world are managing every iota of data as it can be mined to derive business intelligence (BI). However, when data comes from multiple sources, it may result in duplicate records. As data is given paramount importance, it is also significant to eliminate duplicate entities towards data integration, performance and resource optimization. To realize reliable systems for record deduplication, late, deep learning could offer exciting provisions with a learning-based approach. Deep ER is one of the deep learning-based methods used recently for dealing with the elimination of duplicates in structured data. Using it as a reference model, in this paper, we propose a framework known as Enhanced Deep Learning-based Record Deduplication (EDL-RD) for improving performance further. Towards this end, we exploited a variant of Long Short Term Memory (LSTM) along with various attribute compositions, similarity metrics, and numerical and null value resolution. We proposed an algorithm known as Efficient Learning based Record Deduplication (ELbRD). The algorithm extends the reference model with the aforementioned enhancements. An empirical study has revealed that the proposed framework with extensions outperforms existing methods.

Smooth interaction with a disaster-affected community can create and strengthen its social capital, leading to greater effectiveness in the provision of successful post-disaster recovery aid. To understand the relationship between the types of interaction, the strength of social capital generated, and the provision of successful post-disaster recovery aid, intricate ethnographic qualitative research is required, but it is likely to remain illustrative because it is based, at least to some degree, on the researcher\'s intuition. This paper thus offers an innovative research method employing a quantitative artificial intelligence (AI)-based language model, which allows researchers to re-examine data, thereby validating the findings of the qualitative research, and to glean additional insights that might otherwise have been missed. This paper argues that well-connected personnel and religiously-based communal activities help to enhance social capital by bonding within a community and linking to outside agencies and that mixed methods, based on the AI-based language model, effectively strengthen text-based qualitative research.
災害の影響を受けたコミュニティとの円滑な交流は、コミュニティの社会資本を構築および強化することができ、災害後の復興支援をより効果的に提供することにつながる。相互作用の種類、生成される社会資本の強さ、および成功した災害復興支援の提供の関係を理解するには、複雑な民族誌的かつ質的研究が必要であるが、それは少なくともある程度は研究者の直感に基づいているため、例示にとどまる可能性が高い。このような研究に次元を加えて強化するために、この論文では、定量的な AI ベースの言語モデルを使用した革新的研究手法を提示する。本モデルを使用することで、質的研究のデータを再調査して結果を検証し、見逃されていた可能性のあるその他の洞察の収集が可能となる。本論文では、人間関係の良好な人材と宗教に基づいた共同体活動が、コミュニティ内での絆や外部機関とのつながりによって社会資本の強化に役立つと論じている。また、AI ベースの言語モデルに基づく混合手法により、テキストベースの質的研究を効果的に強化できると論じている。.
与受灾社区的顺畅互动可创造和加强社区的社会资本，从而更有效地提供灾后恢复援助。为了理解互动类型所产生的社会资本强度以及成功的灾后恢复援助之间的关系，需要进行复杂的民族志定性研究，但它可能仍具有说明性，因为它至少在某种程度上基于研究人员的直觉。为了增加维度并加强此类研究，本文提出了一种采用基于定量人工智能的语言模型的创新研究方法。该模型允许研究人员重新审查数据，从而验证定性研究的结果，并收集定性研究可能错过的额外见解。本文认为，人脉广泛的人员和以基于宗教的社区活动有助于通过社区内的联系和与外部机构的联系来增强社会资本。它还认为，基于人工智能语言模型的混合方法能有效地加强基于文本的定性研究。.

As words can have multiple meanings that depend on sentence context, genes can have various functions that depend on the surrounding biological system. This pleiotropic nature of gene function is limited by ontologies, which annotate gene functions without considering biological contexts. We contend that the gene function problem in genetics may be informed by recent technological leaps in natural language processing, in which representations of word semantics can be automatically learned from diverse language contexts. In contrast to efforts to model semantics as \"is-a\" relationships in the 1990s, modern distributional semantics represents words as vectors in a learned semantic space and fuels current advances in transformer-based models such as large language models and generative pre-trained transformers. A similar shift in thinking of gene functions as distributions over cellular contexts may enable a similar breakthrough in data-driven learning from large biological datasets to inform gene function.

This study investigates the intersection of race, gender, and criminality in the language surrounding mental health and illness. Applying computational methods of word embeddings to full text data from major American newspapers between 2000 and 2023, I show that the landscape of mental health is broadly racialized as black, challenging the notion of mental illness as a predominantly white phenomenon. Cultural ideas about mental illness are gendered such that women are medicalized and men are criminalized, yet certain terms blur the boundary between illness and criminality. I highlight how stereotypes embedded in mental health language perpetuate stigma around men\'s mental health and justify social control with notable implications for black men. I conclude with recommendations for the mental health movement by advocating for more inclusive discussions around men\'s mental health and revised person-centric language.

Today, many social groups face negative stereotypes. Is such negativity a stable feature of society and, if so, what mechanisms maintain stability both within and across group targets? Answering these theoretically and practically important questions requires data on dozens of group stereotypes examined simultaneously over historical and societal scales, which is only possible through recent advances in Natural Language Processing. Across two studies, we use word embeddings from millions of English-language books over 100 years (1900-2000) and extract stereotypes for 58 stigmatized groups. Study 1 examines aggregate, societal-level trends in stereotype negativity by averaging across these groups. Results reveal striking persistence in aggregate negativity (no meaningful slope), suggesting that society maintains a stable level of negative stereotypes. Study 2 introduces and tests a new framework identifying potential mechanisms upholding stereotype negativity over time. We find evidence of two key sources of this aggregate persistence: within-group \"reproducibility\" (e.g., stereotype negativity can be maintained by using different traits with the same underlying meaning) and across-group \"replacement\" (e.g., negativity from one group is transferred to other related groups). These findings provide novel historical evidence of mechanisms upholding stigmatization in society and raise new questions regarding the possibility of future stigma change.

BACKGROUND: Traditional literature based discovery is based on connecting knowledge pairs extracted from separate publications via a common mid point to derive previously unseen knowledge pairs. To avoid the over generation often associated with this approach, we explore an alternative method based on word evolution. Word evolution examines the changing contexts of a word to identify changes in its meaning or associations. We investigate the possibility of using changing word contexts to detect drugs suitable for repurposing.
RESULTS: Word embeddings, which represent a word\'s context, are constructed from chronologically ordered publications in MEDLINE at bi-monthly intervals, yielding a time series of word embeddings for each word. Focusing on clinical drugs only, any drugs repurposed in the final time segment of the time series are annotated as positive examples. The decision regarding the drug\'s repurposing is based either on the Unified Medical Language System (UMLS), or semantic triples extracted using SemRep from MEDLINE.
CONCLUSIONS: The annotated data allows deep learning classification, with a 5-fold cross validation, to be performed and multiple architectures to be explored. Performance of 65% using UMLS labels, and 81% using SemRep labels is attained, indicating the technique\'s suitability for the detection of candidate drugs for repurposing. The investigation also shows that different architectures are linked to the quantities of training data available and therefore that different models should be trained for every annotation approach.

In principle, the fundamental concepts person, woman, and man should apply equally to people of different genders and races/ethnicities. In reality, these concepts might prioritize certain groups over others. Based on interdisciplinary theories of androcentrism, we hypothesized that (a) person is more associated with men than women (person = man) and (b) woman is more associated with women than man is with men (i.e., women are more gendered: gender = woman). We applied natural language processing tools (specifically, word embeddings) to the linguistic output of millions of individuals (specifically, the Common Crawl corpus). We found the hypothesized person = man / gender = woman bias. This bias was stronger about Hispanic and White (vs. Asian) women and men. We also uncovered parallel biases favoring White individuals in the concepts person, woman, and man. Western society prioritizes men and White individuals as people and \"others\" women as people with gender, with implications for equity across policy- and decision-making contexts.

Social group-based identities intersect. The meaning of \"woman\" is modulated by adding social class as in \"rich woman\" or \"poor woman.\" How does such intersectionality operate at-scale in everyday language? Which intersections dominate (are most frequent)? What qualities (positivity, competence, warmth) are ascribed to each intersection? In this study, we make it possible to address such questions by developing a stepwise procedure, Flexible Intersectional Stereotype Extraction (FISE), applied to word embeddings (GloVe; BERT) trained on billions of words of English Internet text, revealing insights into intersectional stereotypes. First, applying FISE to occupation stereotypes across intersections of gender, race, and class showed alignment with ground-truth data on occupation demographics, providing initial validation. Second, applying FISE to trait adjectives showed strong androcentrism (Men) and ethnocentrism (White) in dominating everyday English language (e.g. White + Men are associated with 59% of traits; Black + Women with 5%). Associated traits also revealed intersectional differences: advantaged intersectional groups, especially intersections involving Rich, had more common, positive, warm, competent, and dominant trait associates. Together, the empirical insights from FISE illustrate its utility for transparently and efficiently quantifying intersectional stereotypes in existing large text corpora, with potential to expand intersectionality research across unprecedented time and place. This project further sets up the infrastructure necessary to pursue new research on the emergent properties of intersectional identities.

Anticancer peptides (ACPs) are a group of peptides that exhibit antineoplastic properties. The utilization of ACPs in cancer prevention can present a viable substitute for conventional cancer therapeutics, as they possess a higher degree of selectivity and safety. Recent scientific advancements generate an interest in peptide-based therapies which offer the advantage of efficiently treating intended cells without negatively impacting normal cells. However, as the number of peptide sequences continues to increase rapidly, developing a reliable and precise prediction model becomes a challenging task. In this work, our motivation is to advance an efficient model for categorizing anticancer peptides employing the consolidation of word embedding and deep learning models. First, Word2Vec, GloVe, FastText, One-Hot-Encoding approaches are evaluated as embedding techniques for the purpose of extracting peptide sequences. Then, the output of embedding models are fed into deep learning approaches CNN, LSTM, BiLSTM. To demonstrate the contribution of proposed framework, extensive experiments are carried on widely-used datasets in the literature, ACPs250 and independent. Experiment results show the usage of proposed model enhances classification accuracy when compared to the state-of-the-art studies. The proposed combination, FastText+BiLSTM, exhibits 92.50% of accuracy for ACPs250 dataset, and 96.15% of accuracy for the Independent dataset, thence determining new state-of-the-art.