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Abstract This study investigates the use of metaphors and the prospect of gain/loss conveyed in the coverage of the pandemic in a leading conventional news outlet in Macau. We discovered that war metaphors have predominantly been used in reports in the Macau Daily News, and have identified three sets of lexical expressions used in these metaphors. The main themes conveyed by the war metaphors include describing the gravity of the situation, communicating anti-pandemic measures, and urging the public to participate in anti-pandemic efforts. In addition, the persuasive messages contained in the appeals for joint action are examined in terms of gain/loss frames and collectivistic versus individualistic appeals. We argue for an analytical framework that encompasses both conceptual metaphors and gain/loss framing in subsequent studies on persuasive messaging.

Abstract Nouns in human languages mostly profile concrete and abstract entities. But how much eventive information can be found in nouns? Will such eventive information found in sensory nouns have anything to do with the cognitive representation of the basic human senses? Importantly, is there any ontological and/or cognitive motivation that can account for this noun–verb dichotomy via body-and-world interactions? This study presents the first comprehensive investigation of sensory nouns in Mandarin Chinese, examining their qualia structures formalised in the Generative Lexicon Theory, as well as the time-dependent (endurant–perdurant) properties encoded in their sensory modalities. This study fills the gap in sensorial studies by highlighting the pivotal position of nouns in sensory experiences and provides insights into the interactions between perception, cognition, and language. Further, it establishes, for the first time, the cognitive motivation of the categorial noun–verb bifurcation without presupposing any a priori knowledge of grammatical categories.

Abstract The present paper explores the synchronic variations and diachronic changes in political discourses in Hong Kong (HK) and in Mainland of People’s Republic of China (PRC). The relationship between lengths of linguistic constructs and their immediate constituents (including sentences and clauses, and clauses and words) are fitted using the function y = ax b based on the Menzerath–Altmann (MA) law to capture the characteristics of language as self-organizing complex systems. We found that the two fitted parameters a and b, as distinctive characteristics of complex systems, can distinguish two regional variants of political speeches from HK and PRC over different periods in time. We also found that the same parameters can capture language changes between different periods of political speeches from the PRC. More specifically, we found that regional variations and historical changes show different degrees of salience at different constituency levels. In addition, we found compounding effects between historical change and regional variations. That is, the two regional variants of political speeches are closer to each other at the earliest diachronic period as compared with the latter two periods, as represented by the fitted parameters of the relationship between sentence and clause lengths. Our results provide strong support for the hypothesis for the MA Law capturing the characteristics of language as a complex self-organizing system, as the two fitted parameters account for the interaction of diachronic language change and synchronic variation.

Abstract Background The COVID-19 pandemic has increasingly accelerated the publication pace of scientific literature. How to efficiently curate and index this large amount of biomedical literature under the current crisis is of great importance. Previous literature indexing is mainly performed by human experts using Medical Subject Headings (MeSH), which is labor-intensive and time-consuming. Therefore, to alleviate the expensive time consumption and monetary cost, there is an urgent need for automatic semantic indexing technologies for the emerging COVID-19 domain. Results In this research, to investigate the semantic indexing problem for COVID-19, we first construct the new COVID-19 Semantic Indexing dataset, which consists of more than 80 thousand biomedical articles. We then propose a novel semantic indexing framework based on the multi-probe attention neural network (MPANN) to address the COVID-19 semantic indexing problem. Specifically, we employ a k-nearest neighbour based MeSH masking approach to generate candidate topic terms for each input article. We encode and feed the selected candidate terms as well as other contextual information as probes into the downstream attention-based neural network. Each semantic probe carries specific aspects of biomedical knowledge and provides informatively discriminative features for the input article. After extracting the semantic features at both term-level and document-level through the attention-based neural network, MPANN adopts a linear multi-view classifier to conduct the final topic prediction for COVID-19 semantic indexing. Conclusion The experimental results suggest that MPANN promises to represent the semantic features of biomedical texts and is effective in predicting semantic topics for COVID-19 related biomedical articles.

Word embeddings are vectorial semantic representations built with either counting or predicting techniques aimed at capturing shades of meaning from word co-occurrences. Since their introduction, these representations have been criticized for lacking interpretable dimensions. This property of word embeddings limits our understanding of the semantic features they actually encode. Moreover, it contributes to the “black box” nature of the tasks in which they are used, since the reasons for word embedding performance often remain opaque to humans. In this contribution, we explore the semantic properties encoded in word embeddings by mapping them onto interpretable vectors, consisting of explicit and neurobiologically motivated semantic features (Binder et al. 2016). Our exploration takes into account different types of embeddings, including factorized count vectors and predict models (Skip-Gram, GloVe, etc.), as well as the most recent contextualized representations (i.e., ELMo and BERT).In our analysis, we first evaluate the quality of the mapping in a retrieval task, then we shed light on the semantic features that are better encoded in each embedding type. A large number of probing tasks is finally set to assess how the original and the mapped embeddings perform in discriminating semantic categories. For each probing task, we identify the most relevant semantic features and we show that there is a correlation between the embedding performance and how they encode those features. This study sets itself as a step forward in understanding which aspects of meaning are captured by vector spaces, by proposing a new and simple method to carve human-interpretable semantic representations from distributional vectors.

Abstract This paper proposes a textual analytics approach to the discovery of trends and variations in social development. Specifically, we have designed a linguistic index that measures the marked usage of gendered modifiers in the Chinese language; this predicts the degree of occupational gender segregation by identifying the unbalanced distribution of males and females across occupations. The effectiveness of the linguistic index in modelling occupational gender segregation was confirmed through survey responses from 244 participants, covering 63 occupations listed in the Holland Occupational Codes. The index was then applied to explore the trends and variations of gender equality in occupation, drawing on an extensive digital collection of materials published by the largest newspaper group in China for both longitudinal (from 1946 to 2018) and synchronic (from 31 provincial-level administrative divisions) data. This quantitative study shows that (1) the use of gendered language has weakened over time, indicating a decline in occupational gender stereotyping; (2) conservative genres have shown higher degrees of gendered language use; (3) culturally conservative, demographically stable, or geographically remote regions have higher degrees of gendered language use. These findings are discussed with consideration of historical, cultural, social, psychological, and geographical factors. While the existing literature on gendered language has been an important and useful tool for reading a text in the context of digital humanities, an innovative textual analytics approach, as shown in this paper, can prove to be a crucial indicator of historical trends and variations in social development.

Abstract Interactions among the environment, humans and language underlie many of the most pressing challenges we face today. This study investigates the use of different verbs to encode various weather events in Sinitic languages, a language family spoken over a wide range of climates and with 3000 years of continuous textual documentation. We propose to synergise the many concepts of kinesis that grew from Aristotle’s original ideas to account for the correlation between meteorological events and their linguistic encoding. It is observed that the two most salient key factors of weather events, i.e., mass of weather substances and speed of weather processes, are the two contributing components of kinetic energy. Leveraging the linguistic theory that kinesis underpins conceptualisation of verb classes, this paper successfully accounts for the selection of verbs for different meteorological events in all Sinitic languages in terms of both language variations and changes. Specifically, weather events with bigger weather substances and faster weather processes tend to select action verbs with high transitivity. The kinesis driven accounts also predict the typological variations between verbal and nominal constructions for weather expressions. The correlation between kinesis and the selection of verbs is further corroborated by an experiment on the perception of native Sinitic language speakers, as well as analyses of regional variations of verb selections that do not follow general typological patterns. It is found that such typological exceptions generally correspond to variations in meteorological patterns. By explicating the pivotal role of kinesis in bridging weather events and the linguistic encoding of weather, this study underlines the role of cognition as the conceptualisation of physical and sensory inputs to sharable knowledge encoded by language.

Leech’s corpus-based comparison of English modal verbs from 1961 to 1992 showed the steep decline of all modal verbs together, which he ascribed to continuing changes towards a more equal and less authority-driven society. This study inspired many diachronic and synchronic studies, mostly on English modal verbs and largely assuming the correlation between the use of modal verbs and power relations. Yet, there are continuing debates on sampling design and the choices of corpora. In addition, this hypothesis has not been attested in any other language with comparable corpus size or examined with longitudinal studies. This study tracks the use of Chinese modal verbs from 1901 to 2009, covering the historical events of the New Culture Movement, the establishment of the PRC, the implementation of simplified characters and the completion and finalization of simplification of the Chinese writing system. We found that the usage of modal verbs did rise and fall during the last century, and for more complex reasons. We also demonstrated that our longitudinal end-to-end approach produces convincing analysis on English modal verbs that reconciles conflicting results in the literature adopting Leech’s point-to-point approach.

This article investigates the evolution of social distancing terms in Chinese and English in two geographically close yet culturally distinct metropolitan cities: Hong Kong and Guangzhou. This study of bilingual public health campaign posters during the COVID-19 pandemic focuses on how the evolution of neologisms and linguistic strategies in public health campaigns adapts to different societal contexts. A baseline meaning of the re-purposed linguistic expressions was established according to the BNC corpus for English and the Chinese Gigaword Corpus for Chinese. To establish the link between linguistic expressions and public health events, we converted them to eventive structures using the Module-Attribute Representation of Verbs and added interpersonal meaning interpretations based on Systemic Functional Linguistics. The two cities are found to have taken divergent approaches. Guangzhou prefers “contact prevention” with behavior-inhibiting imperatives and high value modality. Conversely, the original use of “contact prevention” in Hong Kong was gradually replaced by the neologism social distancing in English, triggering competing loan translations in Chinese. In Hong Kong, behavior-encouraging expressions are predominantly used with positive polarity and varying modality and mood devices, which fluctuate to track the epidemic curve of COVID-19. We conclude that lexical evolution interacts with social realities. Different speech acts, prohibition in Guangzhou but advice and warning in Hong Kong, are constructed with a careful bilingual reconfiguration of eventive information, mood, modality, and polarity to tactfully address the social dynamics in the two cities.

Metaphors are compact packages of information with rich cultural background information. As one of the most powerful linguistic forms with non-literal meaning, metaphor detection in natural language processing can be both challenging and rewarding. We propose an innovative method for metaphor detection and classification leveraging culturally grounded eventive information. This culturally grounded information is organized based on ontological structure, which in turn facilitates further semantic processing of the result of our classification. As a culturally bound ontological system, the Chinese writing system has basic concepts organized according to semantic radicals, which are symbols containing rich eventive information that represent categorical concepts. This paper illustrates the basic design principles of applying ontological structures in metaphor detection by taking into account radicals representing body parts, instruments, materials, and movements. Our approach to leverage the eventive information of the Chinese writing system in metaphor detection is based on the fact that such information is available as an integral part of the writing system of any text. We hypothesize that eventive information can be accessed through the “embodied” source domain information represented by the radicals without syntactic processing or annotation. In terms of the theory of metaphor, we further hypothesize that eventive types in the embodied source domain maps to, and hence can help to predict, eventive meaning in the target domain of metaphor. Our studies show that the event information encoded in lexical items can facilitate classification of metaphoric events and identification of metaphors in Chinese texts effectively. We achieved improvements in Chinese metaphor detection over state-of-the-art approaches in our first classification experiment, and our proposed approach is shown to be generalizable in a second experiment involving new sets of characters with the same radicals.

Distributional Semantic Models (DSMs) established themselves as a standard for the representation of word and sentence meaning. However, DSMs provide quantitative measurement of how strongly two linguistic expressions are related, without being able to automatically classify different semantic relations. Hence the notion of semantic similarity is underspecified in DSMs. We introduce Evalution-MAN in this paper as an effort to address this underspecification problem. Following the EVALution 1.0 dataset for English, we present a dataset for evaluating DSMs on the task of the identification of semantic relations in Mandarin Chinese. Moreover, we test different types of word vectors on the automatic learning of these semantic relations, and we evaluate them both in a unsupervised and in a supervised setting, finding that distributional models tend, in general, to assign higher similarity scores to synonyms and that deep learning classifiers are the best performing ones in the identification of semantic relations.

Why are fog, dew, and frost said to “fall” in some languages when they don’t in the physical world? We explore this seeming infelicity to study the nature of linguistic conceptualization. We focus on variations and changes of the morphosemantic behaviors of weather words in Mandarin and other Sinitic languages with an interdisciplinary approach to establish links between linguistic expressions and scientific facts. We propose that this use of directionality is the result of conventionalization of Chinese people’s inference from shared daily experience, and is well motivated in terms of a linguistic ontology that reflects a scientific account of natural phenomena. We further demonstrate that the semantically relevant orthography shared by Chinese speakers can be directly mapped to Hantology, a formal linguistic ontology based on Suggested Upper Merged Ontology (SUMO). In this mapping, the radical 雨 yǔ “rain,” derived from the ideograph of “rain” to represent atmospheric water, provides crucial clues to the use of directional verbs and the parts of speech of weather words. Our findings also lend support to language-based reconstruction of traditional ecological knowledge (TEK) and lay foundation for TEK research in the Sinosphere.

This paper adopts models from epidemiology to account for the development and decline of neologisms based on internet usage. The research design focuses on the issue of whether a host-driven epidemic model is well-suited to explain human behavior regarding neologisms. We extracted the search frequency data from Google Trends that covers the ninety most influential Chinese neologisms from 2008-2016 and found that the majority of them possess a similar rapidly rising-decaying pattern. The epidemic model is utilized to fit the evolution of these internet-based neologisms. The epidemic model not only has good fitting performance to model the pattern of rapid growth, but also is able to predict the peak point in the neologism's life cycle. This result underlines the role of human agents in the life cycle of neologisms and supports the macro-theory that the evolution of human languages mirrors the biological evolution of human beings.

This article investigates the interplay of lexical competition and socio-historical events through a close examination of the use of gambling and gaming based on large-scale synchronic and diachronic corpora. We first set the background for comparison through a synchronic study of the collocational patterns and grammatical relations of the two words using Sketch Engine. We show that gambling tends to be associated with negatively perceived activities and strong disapproval, whereas gaming tends to collocate with recreational activities, business, and technology. Using Google Books Ngram Viewer, we focus on the drastic diachronic changes in use of the two words, from competition to co-development. Based on corpora trends, we correlate the rise and fall of the two words and the change in their competition relation to particular socio-historical events: gold rushes, sports betting, the popularity of video games, and the gaming industry boom. The classical competition model of near synonyms remained valid until recent socio-economic events introduced additional and unique meanings for both words. The article thus shows that linguistic variations as collective human behavior changes can be leveraged to evidence other collective human behavior changes.

Finding methods to represent multiple types of nodes in heterogeneous networks is both challenging and rewarding, as there is much less work in this area compared with that of homogeneous networks. In this paper, we propose a novel approach to learn node embedding for heterogeneous networks through a joint learning framework of both network links and text associated with nodes. A novel attention mechanism is also used to make good use of text extended through links to obtain much larger network context. Link embedding is first learned through a random-walk-based method to process multiple types of links. Text embedding is separately learned at both sentence level and document level to capture salient semantic information more comprehensively. Then, both types of embeddings are jointly fed into a hierarchical neural network model to learn node representation through mutual enhancement. The attention mechanism follows linked edges to obtain context of adjacent nodes to extend context for node representation. The evaluation on a link prediction task in a heterogeneous network data set shows that our method outperforms the current state-of-the-art method by 2.5%-5.0% in AUC values with p-value less than 10-9, indicating very significant improvement.

Modality exclusivity norms have been developed in different languages for research on the relationship between perceptual and conceptual systems. This paper sets up the first modality exclusivity norms for Chinese, a Sino-Tibetan language with semantics as its orthographically relevant level. The norms are collected through two studies based on Chinese sensory words. The experimental designs take into consideration the morpho-lexical and orthographic structures of Chinese. Study 1 provides a set of norms for Mandarin Chinese single-morpheme words in mean ratings of the extent to which a word is experienced through the five sense modalities. The degrees of modality exclusivity are also provided. The collected norms are further analyzed to examine how sub-lexical orthographic representations of sense modalities in Chinese characters affect speakers' interpretation of the sensory words. In particular, we found higher modality exclusivity rating for the sense modality explicitly represented by a semantic radical component, as well as higher auditory dominant modality rating for characters with transparent phonetic symbol components. Study 2 presents the mean ratings and modality exclusivity of coordinate disyllabic compounds involving multiple sense modalities. These studies open new perspectives in the study of modality exclusivity. First, links between modality exclusivity and writing systems have been established which has strengthened previous accounts of the influence of orthography in the processing of visual information in reading. Second, a new set of modality exclusivity norms of compounds is proposed to show the competition of influence on modality exclusivity from different linguistic factors and potentially allow such norms to be linked to studies on synesthesia and semantic transparency.

The purpose of this study was to construct, measure, and identify a schematic representation of phonological processing in the tonal language Mandarin Chinese through the combination of network science and psycholinguistic tasks. Two phonological association tasks were performed with native Mandarin speakers to identify an optimal phonological annotation system. The first task served to compare two existing syllable inventories and to construct a novel system where either performed poorly. The second task validated the novel syllable inventory. In both tasks, participants were found to manipulate lexical items at each possible syllable location, but preferring to maintain whole syllables while manipulating lexical tone in their search through the mental lexicon. The optimal syllable inventory was then used as the basis of a Mandarin phonological network. Phonological edit distance was used to construct sixteen versions of the same network, which we titled phonological segmentation neighborhoods (PSNs). The sixteen PSNs were representative of every proposal to date of syllable segmentation. Syllable segmentation and whether or not lexical tone was treated as a unit both affected the PSNs’ topologies. Finally, reaction times from the second task were analyzed through a model selection procedure with the goal of identifying which of the sixteen PSNs best accounted for the mental target during the task. The identification of the tonal complex-vowel segmented PSN (C_V_C_T) was indicative of the stimuli characteristics and the choices participants made while searching through the mental lexicon. The analysis revealed that participants were inhibited by greater clustering coefficient (interconnectedness of words according to phonological similarity) and facilitated by lexical frequency. This study illustrates how network science methods add to those of psycholinguistics to give insight into language processing that was not previously attainable.

This paper analyzes the concept of opposition and describes a fully unsupervised method for its automatic discrimination from near-synonymy in Distributional Semantic Models (DSMs). The discriminating method is based on the hypothesis that, even though both near-synonyms and opposites are mostly distributionally similar, opposites are different from each other in at least one dimension of meaning, which can be assumed to be salient. Such hypothesis has been implemented in APAnt, a distributional measure that evaluates the extent of the intersection among the most relevant contexts of two words (where relevance is measured as mutual dependency), and its saliency (i.e. their average rank in the mutual dependency sorted list of contexts). The measure – previously introduced in some pilot studies – is presented here with two variants. Evaluation shows that it outperforms three baselines in an antonym retrieval task: the vector cosine, a baseline implementing the co-occurrence hypothesis, and a random rank. This paper describes the algorithm in details and analyzes its current limitations, suggesting that extensions may be developed for discriminating antonyms not only from near-synonyms but also from other semantic relations. During the evaluation, we have noticed that APAnt also has a particular preference for hypernyms.

This study explores the issues involving pragmatic inferences with prosodic cues. Although there is a well-established literature from multiple languages demonstrating how different pragmatic inferences can be applied to the same syntactic structure, few studies discuss whether prosody can determine types of alternative sets based on the same syntactic structure. In Mandarin Chinese, the same sentence containing a numeral-classifier phrase as a negative polarity item can be employed for two types of scalar inferences based on either the numeral or the noun. The sentence wo yi zhi mayi dou mei kan dao (“I didn’t even see one ant”) can induce two different scalar inferences: Quantity-contrast (‘I did not see one ant, much less two ants, three ants, and so on’ by drawing a contrast against the minimal quantity of one), and Type-contrast (‘I did not see an ant, much less a dog, a cat, a human being, and so on’ by drawing a contrast against the minimally surprising type, that of ants). Taking advantage of similar sentences with the syntactic structure and lexical items, our study examines whether prosodic conditions can guide people to choose pragmatic inferences from a set of options based on the same syntactic structure. The experiments of this study are designed to answer whether prosody interacts with contextual information in this grammatical structure. The results suggest that Mandarin speakers can use sentence prosody to determine which inference is intended, at least in experimental contexts that directly probe explicit awareness of prosody. Prosody does play a role in inducing scalar inferences, but contextual information can override the effects of prosody. Each prosodic pattern can evoke a specific set of scalar inferences, but quantity-contrast inferences are favored over type-contrast inferences. Our experiments show that prosodic prominence can serve as a linguistic cue to pragmatic inferences.