Chunyu Kit

This study investigates how wrap-up effects—sentence-final words incur heavier processing loads than sentence-internal words—manifest in natural reading of unspaced, logographic Chinese scripts. We leveraged a large-scale naturalistic reading corpus (subjects: 98; word tokens: more than 1 million by 300 individual sentences and seven passages), employed multifactorial analyses by (generalized) linear mixed-effects models, and compared four eye-movement differences (i.e., gaze duration, total reading time, skipping probability and regression-in probability) between sentence-internal and sentence-final words. The results demonstrated robust reversal of traditional wrap-up effects: sentence-final words required significantly less processing effort than sentence-internal words, such as shorter durations, higher skipping rates and lower regression-in probabilities. Further, this reversal was modulated by reading scenarios (sentence vs. passage), boundary salience (period- vs. comma-bounded), and wrap-up positions (pre-critical, critical, and spill-over). Notably, sentence-final words were processed more rapidly when associated with characteristics such as fewer stroke counts, shorter length, higher frequency, function-word status, or progress further into page at the late processing stage. Challenging classic models that attribute inflated time at clause/sentence boundaries to semantic integration, we postulate punctuation’s dual role in unspaced language processing: visual cues for word segmentation/recognition and semantic cues for integration jointly optimize language comprehension.

Contrary to the widespread notion that linguistic signs are arbitrary, researchers have consistently demonstrated the existence of sound symbolism in language, providing evidence for non-arbitrariness in sound-meaning associations. However, much evidence of this kind is based on a limited subset of vocabulary and falls short of systematically demonstrating the pervasive nature of sound symbolism and, especially, its central, rather than marginal, role in language. Furthermore, a historical perspective is lacking to determine whether sound symbolism is merely a feature of archaic languages or has remained a significant element throughout the evolution of languages. This research pioneers a diachronic analysis of sound symbolism in Chinese using historical rhyme books to trace its presence on the vocabulary scale. Employing natural language processing techniques along with statistical methods, it investigates whether phonologically related Chinese characters, as documented in rhyme books, also demonstrate semantic congruence, which would suggest that the phonological aspects of characters are inherently meaningful and hence indicate a systematic, rather than random or purely arbitrary relationship between sounds and meanings. Statistically significant results from our analysis of all four analyzed rhyme books confirm the robustness of sound symbolism over a large span of the Chinese language continuum, and a granular analysis of a representative one of them further reveals that sound symbolism is manifest across various levels of phonological organization, including initials, finals, etc. This study initiates an innovative combination of traditional materials with novel techniques to enrich and expand existing knowledge about sound symbolism, providing both methodological advancement and empirical insights.

The enduring comparison between Li Bai (李白, 701–762) and Du Fu (杜甫, 712–770), two towering poets in Chinese literary history, has traditionally centred on semantic and thematic aspects, often overlooking the phonetic dimension or narrowly focusing on rhythm and rhyme while neglecting other sound features. This study applies sound symbolism principles to explore the emotional and perceptual effects of sounds in their poetry, focusing on a quantitative phonetic comparison of their works. Utilising character sound information extracted from the rhyme book Guangyun (廣韻 extended rhymes), their poems were transformed into sound vectors for authorship attribution by machine learning models. Several of these models achieved average F1 scores above 80%, effectively demonstrating the capability of sounds to distinguish between the two poets. Through statistical analyses, their preferred phonetic features were located and the sound-sentiment relationships in their poetry were identified, revealing that Li Bai favoured the more positive sound features while Du Fu showed an inclination towards the more negative ones. Moreover, the distinct sound preferences sculpted Li’s poetry with a vibrant, melodious, and unbound quality, while lending a subtle, sombre, and constrained undertone to Du’s verses. Focusing on the phonetic aspect, this study takes a novel digital humanities approach to the traditional Li-Du comparison topic, offering fresh insights into their differences and underscoring the impact of sounds on the aesthetic experience of poetry.

Recent years have witnessed a mushrooming of reading corpora that have been built by means of eye tracking. This article showcases theHong Kong Corpus of Chinese Sentence and Passage Reading(HKC for brevity), featured by a natural reading of logographic scripts and unspaced words. It releases 28 eye-movement measures of 98 native speakers reading simplified Chinese in two scenarios: 300 one-line single sentences and 7 multiline passages of 5,250 and 4,967 word tokens, respectively. To verify its validity and reusability, we carried out (generalised) linear mixed-effects modelling on the capacity of visual complexity, word frequency, and reading scenario to predict eye-movement measures. The outcomes manifest significant impacts of these typical (sub)lexical factors on eye movements, replicating previous findings and giving novel ones. The HKC provides a valuable resource for exploring eye movement control; the study contrasts the different scenarios of single-sentence and passage reading in hopes of shedding new light on both the universal nature of reading and the unique characteristics of Chinese reading.

The scientific literature contains valuable information that can be used for future applications, but manual analysis presents challenges due to its size and disciplinary boundaries. The prevailing solution involves natural language processing (NLP) techniques such as information retrieval. Nonetheless, existing automated systems primarily provide either statistically based shallow information or deep information without traceability, thereby falling short of delivering high-quality and reliable insights. To address this, we propose an innovative approach of leveraging sentiment information embedded within the literature to track the opinions toward materials. In this study, we integrated material knowledge into text representation and constructed opinion data sets to hierarchically train deep learning models, named as Scientific Sentiment Network (SSNet). SSNet can effectively extract knowledge from the energy material literature and accurately categorize expert opinions into challenges and opportunities (94% and 92% accuracy, respectively). By incorporating sentiment features determined by SSNet, we can predict the ranking of emerging thermoelectric materials with a 70% correlation to experimental outcomes. Furthermore, our model achieves a commendable 68% accuracy in predicting suitable nanomaterials for atomic layer deposition (ALD) over time. These promising results offer a practical framework to extract and synthesize knowledge from the scientific literature, thereby accelerating research in the field of nanomaterials.

This chapter introduces the key issues and basic principles of computer(-aided) translation evaluation, covering its historical evolution, context-dependent multi-dimensional nature, and existing methodologies. The major evaluation approaches, including both manual and automatic, are presented with discussion of their strengths and weaknesses.

This chapter divides information retrieval and text mining into three stages and describes the main operations of information retrieval and text mining.

The material science literature contains up-to-date and comprehensive scientific knowledge of materials. However, their content is unstructured and diverse, resulting in a significant gap in providing sufficient information for material design and synthesis. To this end, we used natural language processing (NLP) and computer vision (CV) techniques based on convolutional neural networks (CNN) to discover valuable experimental-based information about nanomaterials and synthesis methods in energy-material-related publications. Our first system, TextMaster, extracts opinions from texts and classifies them into challenges and opportunities, achieving 94% and 92% accuracy, respectively. Our second system, GraphMaster, realizes data extraction of tables and figures from publications with 98.3\% classification accuracy and 4.3% data extraction mean square error. Our results show that these systems could assess the suitability of materials for a certain application by evaluation of synthesis insights and case analysis with detailed references. This work offers a fresh perspective on mining knowledge from scientific literature, providing a wide swatch to accelerate nanomaterial research through CNN.

Abstract This article presents a corpus-based distributional analysis of the usage patterns of a cluster of words and compounds containing the morpheme qià (恰) ‘just, exactly’, by the aid of an extended concordancer to retrieve representative collocations from their adjacent contexts in Chinese Gigaword. Upon a survey of the historical evolution of the qià cluster with exemplar data and an overview of existing proposals to account for their usages in terms of expectational match, our distributional analysis is conducted to identify the salient collocational or contextual features that lead to a number of interesting findings. Substantial evidences are provided for clarifying the non-word status of qià rú (恰如) and qià sì (恰似) and their similarities, the exchangeability of qiàhǎo (恰好) and qiàqiǎo (恰巧), distinct collocational preferences of the adverbs qià (恰), qiàqià (恰恰) and the others with different subsets of verbs, the prosodic requirement of an even number of syllables for a qià-adverb and its main verb, and the contrastive popularity of qiàqià (恰恰) vs qiàdàng (恰当) to reveal different usage tendencies between speakers in Taiwan and the Mainland. All these novel findings and insights about the subtle (dis)similarities in the usage and meanings of the qià (恰) cluster suggest that distributional analysis of contextual collocations using large-scale language data remains a powerful tool that can complement other analytical approaches for the advancement of lexical semantic research.