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This study seeks to clarify the nature of linguistic synesthesia using a lexical-conceptual account. Based on a lexical analysis of Mandarin synesthetic usages, we find that (1) linguistic synesthesia maps the metaphorical meaning between two domains; and (2) linguistic synesthetic mappings and conceptual metaphoric mappings have similar behaviors when sense modalities are treated as conceptual domains that contain a set of mappings constrained by Mapping Principles. This lexical-conceptual account is designed to capture the fact that linguistic synesthesia involves mapping between lexicalized concepts of sensory properties, instead of the real-time sensory input that is processed in neurological synesthesia. The incorporation of a lexical semantic view with the framework of Conceptual Metaphor Theory not only offers a coherent and comprehensive account for the nature of linguistic synesthesia, but also handles aspects of linguistic synesthesia previously only accounted for by non-metaphorical accounts. These design features make this proposal the most comprehensive account to fit the current data. Furthermore, by showing linguistic synesthesia as a type of metaphor, our study strengthens the role of conceptual metaphors as the link between the perceived world and our conceptualization of that world.

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.

Written for beginning learners of the language, this concise introduction to Chinese grammar assumes only a basic knowledge of Chinese, and no knowledge of grammatical terminology and practices. Comparing Chinese grammar patterns and rules with those of English, and illustrated with a wealth of real-life examples, it allows learners to understand the similarities and differences between the two languages. Using engaging and accessible language, it examines the Chinese sound system, writing system, word formation rules, parts of speech, and simple and complex sentences, as well as explaining special constructions that are typically challenging to second language learners. Each chapter begins with clear learning goals and ends with a useful summary highlighting the chapter's main points. To call attention to specific issues, sidebars are interspersed throughout the text, and exercises within the book and online answer keys help students to reinforce learned material and assist with self-study.

On the basis of Mey’s Pragmatic Act Theory, this paper investigates the cross-cultural and cross-language variations in the pragmemes to call for social distancing in public health campaigns to combat COVID-19. We compare the officially released posters calling for social distancing in English and Chinese in two neighboring cities with distinctive socio-cultural contexts – Guangzhou and Hong Kong. Our main findings are: (1) Guangzhou takes one pragmeme to suit a short illocution-perlocution distance in calling for social distancing – “admonition,” and Hong Kong takes two pragmemes to meet a larger illocution-perlocution distance – “recommendation” and “reminder”; (2) Cross-cultural differences between the two cities are manifested in the individuated pragmatic acts of the pragmemes in both propositional contents and metapragmatic co-construction of personal references, polarity, modality, and mood; and (3) In both cities, cross-language differences can be observed in the propositional and metapragmatic dimensions of pragmatic acts, with the English posters bearing a weaker sense of addressee obligation than the Chinese. Adding the new angle of illocution-perlocution distance, our rethinking of the illocution versus perlocution dichotomy in pragmemes leads to an elaboration of the classical perlocution formula proposed by Austin in 1962.

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 = axb 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.

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.

Attention models are proposed in sentiment analysis and other classification tasks because some words are more important than others to train the attention models. However, most existing methods either use local context based information, affective lexicons, or user preference information. In this work, we propose a novel attention model trained by cognition grounded eye-tracking data. First,a reading prediction model is built using eye-tracking data as dependent data and other features in the context as independent data. The predicted reading time is then used to build a cognition grounded attention layer for neural sentiment analysis. Our model can capture attentions in context both in terms of words at sentence level as well as sentences at document level. Other attention mechanisms can also be incorporated together to capture other aspects of attentions, such as local attention, and affective lexicons. Results of our work include two parts. The first part compares our proposed cognition ground attention model with other state-of-the-art sentiment analysis models. The second part compares our model with an attention model based on other lexicon based sentiment resources. Evaluations show that sentiment analysis using cognition grounded attention model outperforms the state-of-the-art sentiment analysis methods significantly. Comparisons to affective lexicons also indicate that using cognition grounded eye-tracking data has advantages over other sentiment resources by considering both word information and context information. This work brings insight to how cognition grounded data can be integrated into natural language processing (NLP) tasks.

Durative events by default are atelic. However, temporal targets are typically required for durative verbs with a rushing manner, such as ‘We are catching the 3:30 flight’ and ‘The farmer rushed to harvest before the storm’. Why and how does manner introduce delimiting temporal concepts to durative verbs? This puzzle is addressed by our current study of two near-synonymous Mandarin durative verbs describing events carried out in a rushing manner: 赶 gǎn and 抢 qiǎng. Our event-based account will examine both their compositional meanings and their constructional patterns. We will show that 赶 gǎn and 抢 qiǎng not only coerce eventive readings from their nominal objects, but also require certain delineating temporal targets. The verb 赶 gǎn requires an understood deadline, while the verb 抢 qiǎng requires the presupposition of the limited availability of the object. As neither temporal targets mark the time of the actual activities, these are exceptional cases of aktionsart. We will show that the different ways to delineate event meanings of the constructions [gǎn/qiǎng+ noun] can be predicted from the lexical meaning of the two verbs and can in turn predict the event types represented by the object with the MARVS theory. Based on this lexical semantic representation, we further show that the Generative Lexicon theory predicts the coercions of the rushing meaning from the original activity verb senses, and that the Construction Grammar theory accounts for their sharing of the same syntactic configuration.

Machine learning methods, especially deep learning models, have achieved impressive performance in various natural language processing tasks including sentiment analysis. However, deep lea...

In this paper we present Uniform Meaning Representation (UMR), a meaning representation designed to annotate the semantic content of a text. UMR is primarily based on Abstract Meaning Representation (AMR), an annotation framework initially designed for English, but also draws from other meaning representations. UMR extends AMR to other languages, particularly morphologically complex, low-resource languages. UMR also adds features to AMR that are critical to semantic interpretation and enhances AMR by proposing a companion document-level representation that captures linguistic phenomena such as coreference as well as temporal and modal dependencies that potentially go beyond sentence boundaries.

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.

The Coronavirus Disease 2019 (COVID-19) pandemic has shifted the focus of research worldwide, and more than 10 000 new articles per month have concentrated on COVID-19–related topics. Considering this rapidly growing literature, the efficient and precise extraction of the main topics of COVID-19–relevant articles is of great importance. The manual curation of this information for biomedical literature is labor-intensive and time-consuming, and as such the procedure is insufficient and difficult to maintain. In response to these complications, the BioCreative VII community has proposed a challenging task, LitCovid Track, calling for a global effort to automatically extract semantic topics for COVID-19 literature. This article describes our work on the BioCreative VII LitCovid Track. We proposed the LitCovid Ensemble Learning (LCEL) method for the tasks and integrated multiple biomedical pretrained models to address the COVID-19 multi-label classification problem. Specifically, seven different transformer-based pretrained models were ensembled for the initialization and fine-tuning processes independently. To enhance the representation abilities of the deep neural models, diverse additional biomedical knowledge was utilized to facilitate the fruitfulness of the semantic expressions. Simple yet effective data augmentation was also leveraged to address the learning deficiency during the training phase. In addition, given the imbalanced label distribution of the challenging task, a novel asymmetric loss function was applied to the LCEL model, which explicitly adjusted the negative–positive importance by assigning different exponential decay factors and helped the model focus on the positive samples. After the training phase, an ensemble bagging strategy was adopted to merge the outputs from each model for final predictions. The experimental results show the effectiveness of our proposed approach, as LCEL obtains the state-of-the-art performance on the LitCovid dataset.Database URL: https://github.com/JHnlp/LCEL

Informal short texts on the web are rich in emotions as they often reflect unfiltered immediate reactions to breaking news events. The emotion density, however, stands in contrast to its poverty of linguistic contexts and features for emotion classification. This paper tackles that challenge by proposing orthographic features based on orthographic code mixing and code-switching for both non-ML and ML approaches. Our results show that orthographic features routinely outperform grammatical features for emotion classification for short texts in all approaches as expected. Orthographic features were also shown to make more significant contributions, especially in terms of precision and in formal texts when state of the art deep learning algorithms are applied. This result confirms the effectiveness of the orthographic change feature to the task of emotion classification. These results are argued to be applicable to all languages because of the common code-shifting in languages with non-Latin orthographies, and the use of non-letter symbols in all languages.

This behavioural profiling (BP) study examines the use of the near-synonyms rang (讓), shi (使) and ling (令), three ways to express cause-effect relationships in Chinese. Instead of using an out-of-the-box BP design, we present a modified approach to profiling that includes a range of frame semantic features that aim to capture variation of slot fillers of this construction. The study investigates the intricate semantic variation of rang, shi and ling through a comprehensive analysis of 38 contextual features (ID tags) that characterize the collocational, lexical semantic and frame semantic environment of the near-synonyms. Our dataset consists of around 100.000 data points based on the annotation of 1002 sentences of Mandarin Chinese of three varieties. The BPs of each near-synonym are compared using multidimensional scaling and hierarchical cluster analysis. The results show that rang, shi and ling are each characterized by a combination of distinctive features and how different feature types contribute to setting the near-synonyms apart based on their usage patterns. Methodologically, this study illustrates how behavioural profiling can be modified to include frame semantic features in accordance with the method’s emphasis on producing empirically verifiable results and how these features can aid a comparative analysis of near-synonyms.

The authors identify a plural marker -ɕiɛ in Chengdu Chinese that can apply to both count and mass nouns, encoding plurality as well as definiteness. This post-nominal -ɕiɛ differs from the pre-nominal ɕiɛ, as the latter is purely plural but not inherently definite. Our analysis shows that the pre-nominal ɕiɛ functions as a quantifier, which occupies the [Spec, NumP] position, whereas the suffixal -ɕiɛ, by virtue of being a plural marker, is base-generated in the Num0 position and move to D0 to encode both plurality and definiteness. Moreover, the two ɕiɛs can co-occur in one and the same nominal phrase, exhibiting the double definiteness effect. The syntactic analysis of ɕiɛ in Chengdu Chinese, coupled with the study of di in Cantonese, has theoretical impacts on nominal phrase structures, in particular, on “plurality” and “definiteness”. First, plural markers in classifier languages contrast with those in number languages, as the former, but not the latter, defies numerical modification. Second, definiteness can be expressed by a non-D element, which may check its [+def] feature either by undergoing an upward movement to D (or [Spec, DP]) or by agreeing with the [+def] feature of a demonstrative. Third, the DP-NP distinction is strongly supported by our account of Chengdu Chinese.

This paper explores the effectiveness of Chinese function words in register studies based on text mining and attempts to determine their register specific functions based on quantitative methods. Following the hypothesis that function words can be used as features to differentiate different registers, our text clustering experiment demonstrates that different registers in Chinese can in fact be classified in terms of distribution of function words. Our correspondence analysis shows that the correlations between function words and different registers vary, hence some function words have register-specific functions. In addition, the correlation coefficients between function words and various registers can determine the register-specific function of function words. Our quantitative model of register specific function of function words makes an important contribution to our understanding of the correlation between function words and registers.

This article presents a classification and clustering based study to account for the differences among five Chinese light verbs (congshi, gao, jiayi, jinxing, and zuo) as well as their variations in Mainland China Mandarin (ML) and Taiwan Mandarin (TW). Based on 13 linguistic features, both competition and co-development of these light verbs are studied in terms of their distinct and shared collocates. The proposed method discovers significant new grammatical differences in addition to confirming previously reported ones. Most significant discoveries include selectional restrictions differentiating deverbal nominals and event nouns, and degrees of transitivity of VO compounds. We also find that most variations between Mainland China Mandarin and Taiwan Mandarin are in fact differences in tendencies or preferences in contexts of usage of shared grammatical rules.

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 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.

Genre characterizes a document differently from a subject that has been the focus of most document retrieval and classification applications. This work hypothesizes a close interaction between synt...

This paper reports an innovative Chinese register study based on regression analysis for sentence length distribution and text clustering. Although end of sentence is not conventionally marked in Chinese, we resolve this issue by assuming that segments between periods, question marks, and exclamation marks are sentences, which can be further divided into simple sentences and compound sentences. We also assume that segments between punctuation marks that express pauses in utterances form sentences (i.e., clauses). Using regression analysis, we find that the frequency distribution of sentence and clause lengths in Chinese can be fitted by the formula F = aLbcL, where L is sentence/clause length. Texts from different registers give rise to different fitted values of the parameters, and hence can serve to differentiate these registers. Finally, we use these parameters to represent and cluster texts from different registers. The successful text clustering results further prove that the parameters of the fitted results are reliable linguistic characteristics for different registers. In terms of linguistic theories, our study shows that it is just as effective to model sentence length in Chinese using sociological words (i.e., characters) as it is using linguistic words.

International audience; 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.

In this article, we propose an innovative and robust approach to stylometric analysis without annotation and leveraging lexical and sub-lexical information. In particular, we propose to leverage the phonological information of tones and rimes in Mandarin Chinese automatically extracted from unannotated texts. The texts from different authors were represented by tones, tone motifs, and word length motifs as well as rimes and rime motifs. Support vector machines and random forests were used to establish the text classification model for authorship attribution. From the results of the experiments, we conclude that the combination of bigrams of rimes, word-final rimes, and segment-final rimes can discriminate the texts from different authors effectively when using random forests to establish the classification model. This robust approach can in principle be applied to other languages with established phonological inventory of onset and rimes.

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.