This article sheds light on the significant yet nuanced roles of shame and guilt in influencing moral behaviour, a phenomenon that became particularly prominent during the COVID-19 pandemic with the community’s heightened desire to be seen as moral. These emotions are central to human interactions, and the question of how they are conveyed linguistically is a vast and important one. Our study contributes to this area by analysing the discourses around shame and guilt in English and Japanese online forums, focusing on the terms shame, guilt, haji (‘shame’) and zaiakukan (‘guilt’). We utilise a mix of corpus-based methods and natural language processing tools, including word embeddings, to examine the contexts of these emotion terms and identify semantically similar expressions. Our findings indicate both overlaps and distinct differences in the semantic landscapes of shame and guilt within and across the two languages, highlighting nuanced ways in which these emotions are expressed and distinguished. This investigation provides insights into the complex dynamics between emotion words and the internal states they denote, suggesting avenues for further research in this linguistically rich area.

Product data sharing is fundamental for collaborative product design and development. Although the STandard for Exchange of Product model data (STEP) enables this by providing a unified data definition and description, it lacks the ability to provide a more semantically enriched product data model. Many researchers suggest converting STEP models to ontology models and propose rules for mapping EXPRESS, the descriptive language of STEP, to Web Ontology Language (OWL). In most research, this mapping is a manual process which is time-consuming and prone to misunderstandings. To support this conversion, this research proposes an automatic method based on natural language processing techniques (NLP). The similarities of language elements in the reference manuals of EXPRESS and OWL have been analyzed in terms of three aspects: heading semantics, text semantics, and heading hierarchy. The paper focusses on translating between language elements, but the same approach has also been applied to the definition of the data models. Two forms of the semantic analysis with NLP are proposed: a Combination of Random Walks (RW) and Global Vectors for Word Representation (GloVe) for heading semantic similarity; and a Decoding-enhanced BERT with disentangled attention (DeBERTa) ensemble model for text semantic similarity. The evaluation shows the feasibility of the proposed method. The results not only cover most language elements mapped by current research, but also identify the mappings of the elements that have not been included. It also indicates the potential to identify the OWL segments for the EXPRESS declarations.

Modelling semantic similarity plays a fundamental role in lexical semantic applications. A natural way of calculating semantic similarity is to access handcrafted semantic networks, but similarity prediction can also be anticipated in a distributional vector space. Similarity calculation continues to be a challenging task, even with the latest breakthroughs in deep neural language models. We first examined popular methodologies in measuring taxonomic similarity, including edge-counting that solely employs semantic relations in a taxonomy, as well as the complex methods that estimate concept specificity. We further extrapolated three weighting factors in modelling taxonomic similarity. To study the distinct mechanisms between taxonomic and distributional similarity measures, we ran head-to-head comparisons of each measure with human similarity judgements from the perspectives of word frequency, polysemy degree and similarity intensity. Our findings suggest that without fine-tuning the uniform distance, taxonomic similarity measures can depend on the shortest path length as a prime factor to predict semantic similarity; in contrast to distributional semantics, edge-counting is free from sense distribution bias in use and can measure word similarity both literally and metaphorically; the synergy of retrofitting neural embeddings with concept relations in similarity prediction may indicate a new trend to leverage knowledge bases on transfer learning. It appears that a large gap still exists on computing semantic similarity among different ranges of word frequency, polysemous degree and similarity intensity.

Due to their success in routine tasks such as voice recognition, image classification, and text processing, extensive attention has been aroused on how to use artificial intelligence (AI)-based automation tools in the judicial-trial process to improve efficiency. Meanwhile, judicial trial is a complex task that requires accurate insight and subtle analysis of the cases, law, and common knowledge. Applying the results provided by AI-based automation tools directly to the judicial-trial process is controversial due to their irregular logic and low accuracy. Based on this observation, this article investigates the logic underlined in judicial trials and the technical characteristics of AI, and proposes an AI-based semantic assist approach for judicial trials that is logical and transparent to the judges.

Technical writing in professional environments, such as user manual authoring, requires the use of uniform language. Nonuniform language refers to sentences in a technical document that are intended to have the same meaning within a similar context, but use different words or writing style. Addressing this nonuniformity problem requires the performance of two tasks. The first task, which we named nonuniform language detection (NLD), is detecting such sentences. We propose an NLD method that utilizes different similarity algorithms at lexical, syntactic, semantic and pragmatic levels. Different features are extracted and integrated by applying a machine learning classification method. The second task, which we named nonuniform language correction (NLC), is deciding which sentence among the detected ones is more appropriate for that context. To address this problem, we propose an NLC method that combines contraction removal, near-synonym choice, and text readability comparison. We tested our methods using smartphone user manuals. We finally compared our methods against state-of-the-art methods in paraphrase detection (for NLD) and against expert annotators (for both NLD and NLC). The experiments demonstrate that the proposed methods achieve performance that matches expert annotators.

Over the last two decades, determining the similarity between words as well as between their meanings, that is, word senses, has been proven to be of vital importance in the field of Natural Language Processing. This paper provides the reader with an introduction to the tasks of computing word and sense similarity. These consist in computing the degree of semantic likeness between words and senses, respectively. First, we distinguish between two major approaches: the knowledge-based approaches and the distributional approaches. Second, we detail the representations and measures employed for computing similarity. We then illustrate the evaluation settings available in the literature and, finally, discuss suggestions for future research.

The question of whether different linguistic patterns differentially influence semantic and conceptual representations is of central interest in cognitive science. In this paper, we investigate whether the regular encoding of shape within a nominal classification system leads to an increased salience of shape in speakers' semantic representations by comparing English, (Amazonian) Spanish, and Bora, a shape-based classifier language spoken in the Amazonian regions of Columbia and Peru. Crucially, in displaying obligatory use, pervasiveness in grammar, high discourse frequency, and phonological variability of forms corresponding to particular shape features, the Bora classifier system differs in important ways from those in previous studies investigating effects of nominal classification, thereby allowing better control of factors that may have influenced previous findings. In addition, the inclusion of Spanish monolinguals living in the Bora village allowed control for the possibility that differences found between English and Bora speakers may be attributed to their very different living environments. We found that shape is more salient in the semantic representation of objects for speakers of Bora, which systematically encodes shape, than for speakers of English and Spanish, which do not. Our results are consistent with assumptions that semantic representations are shaped and modulated by our specific linguistic experiences.

Many words have more than one translation across languages. Such translation-ambiguous words are translated more slowly and less accurately than their unambiguous counterparts. We examine the extent to which word context and translation dominance influence the processing of translation-ambiguous words. We further examine how these factors influence translation ambiguity stemming from two sources, specifically translation ambiguity derived from semantic ambiguity and from near-synonymy. Bilingual participants were presented with English–German word pairs that were preceded by a related or unrelated prime and were asked to decide if the word pairs were translations. Translation-unambiguous pairs were recognized more quickly and accurately than translation-ambiguous pairs. Related pairs and dominant translations were responded to more quickly than unrelated pairs and subordinate translations, respectively. We discuss the results in relation to models of bilingual memory and propose a new model that makes specific predictions about translation ambiguity, the Revised Hierarchical Model of Translation Ambiguity.