Search

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 FROM AUTHOR]

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 FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Sentiment analysis is an important task in corpus linguistics and natural language processing. Based on statistical and machine-learning algorithms, texts' subjective evaluations and emotional states can be detected, extracted, and classified. Sentiment analysis results are significant to the development of many different industries in the financial, political, medical, and entertainment domains. They are beneficial to make relevant stakeholders have a good command of public attitudes to products, policies, medical treatment, and services in order to make appropriate adjustments. Lei and Liu provide a comprehensive and concise introduction to sentiment analysis to assist students and academics in understanding the theoretical aspects and the application of sentiment analysis. [ABSTRACT FROM AUTHOR]

In this article we present the Database of Word-Level Statistics for Mandarin Chinese (DoWLS-MAN). The database addresses the lack of agreement in phonological syllable segmentation specific to Mandarin by offering phonological features for each lexical item according to 16 schematic representations of the syllable (8 with tone and 8 without tone). Those lexical statistics that differ per phonological word and nonword due to changes in syllable segmentation are of the variant category and include subtitle lexical frequency, phonological neighborhood density measures, homophone density, and network science measures. The invariant characteristics consist of each items' lexical tone, phonological transcription, and syllable structure among others. The goal of DoWLS-MAN is to provide researchers both the ability to choose stimuli that are derived from a segmentation schema that supports an existing model of Mandarin speech processing, and the ability to choose stimuli that allow for the testing of hypotheses on phonological segmentation according to multiple schemas. In an exploratory analysis we illustrate how multiple schematic representations of the phonological mental lexicon can aid in hypothesis generation, specifically in terms of phonological processing when reading Chinese orthography. Users of the database can search among over 92,000 words, over 1600 out-of-vocabulary Chinese characters, and 4300 phonological nonwords according to either Chinese orthography, pinyin, or ASCII phonetic script. Users can also generate a list of phonological words and nonwords according to user-defined ranges and categories of lexical characteristics. DoWLS-MAN is available to the public for search or download at https://dowls.site. [ABSTRACT FROM AUTHOR]

This work addresses some questions about language processing: what does it mean that natural language sentences are semantically complex? What semantic features can determine different degrees of difficulty for human comprehenders? Our goal is to introduce a framework for argument semantic complexity, in which the processing difficulty depends on the typicality of the arguments in the sentence, that is, their degree of compatibility with the selectional constraints of the predicate. We postulate that complexity depends on the difficulty of building a semantic representation of the event or the situation conveyed by a sentence. This representation can be either retrieved directly from the semantic memory or built dynamically by solving the constraints included in the stored representations. To support this postulation, we built a Distributional Semantic Model to compute a compositional cost function for the sentence unification process. Our evaluation on psycholinguistic datasets reveals that the model is able to account for semantic phenomena such as the context-sensitive update of argument expectations and the processing of logical metonymies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Annotating linguistic data has become a major field of interest, both for supplying the necessary data for machine learning approaches to NLP applications, and as a research issue in its own right. This comprises issues of technical formats, tools, and methodologies of annotation. We provide a brief overview of these notions and then introduce the papers assembled in this special issue. [ABSTRACT FROM AUTHOR]

We investigated network principles underlying mental search through a novel phonological verbal fluency task. Post exclusion, 95 native-language Mandarin speakers produced as many items that differed by a single lexical tone as possible within one minute. Their verbal productions were assessed according to several novel graded fluency measures, and network science measures that accounted for the structure, cohesion and interconnectedness of lexical items. A multivariate regression analysis of our participants' language backgrounds included their mono- or multi-lingual status, English proficiency, and fluency in other Chinese languages/dialects. Higher English proficiency predicted lower error rates and greater interconnectedness, while higher fluency in other Chinese languages/dialects revealed lower successive similarity and lower network coherence. This inverse relationship between English and other Chinese languages/dialects provides evidence of the restructuring of the phonological mental lexicon. [ABSTRACT FROM AUTHOR]

Despite being spoken by a large population of speakers worldwide, Cantonese is under-resourced in terms of the data scale and diversity compared to other major languages. This limitation has excluded it from the current “pre-training and fine-tuning” paradigm that is dominated by Transformer architectures. In this paper, we provide a comprehensive review on the existing resources and methodologies for Cantonese Natural Language Processing, covering the recent progress in language understanding, text generation and development of language models. We finally discuss two aspects of the Cantonese language that could make it potentially challenging even for state-of-the-art architectures: colloquialism and multilinguality [ABSTRACT FROM AUTHOR]