Lexical Properties Research Articles

Review of Language Structures and NLP Techniques for Chinese, Japanese, and English

The increasing demand for cross-lingual communication in a globally interconnected world has spurred significant advancements in Natural Language Processing (NLP), a branch of artificial intelligence aimed at enabling computers to comprehend, generate, and process human language. Over the past few years, NLP has transitioned from traditional rule-based and statistical approaches to deep learning-based methods, with pre-trained models like BERT and GPT demonstrating remarkable performance across a variety of tasks, including text classification and machine translation. However, the linguistic diversity of the world's languages presents ongoing challenges, as many NLP models are primarily developed for high-resource languages like English, leaving other languages underserved. This paper examines the linguistic features of three representative languagesChinese, Japanese, and Englishand explores NLP methodologies tailored to their distinct grammatical, lexical, and structural properties. Through comparative linguistic analysis, this study discusses the challenges of multilingual NLP and the potential for cross-lingual applications. The findings underscore the importance of adapting NLP models to different language characteristics to enhance the effectiveness and inclusivity of cross-lingual processing.

Word formation in Bangladeshi English

Background: This paper aims to investigate the lexical properties of Bangladeshi English (BdE). Bangladesh does not have an official second language. Nevertheless, English has been present in Bangladeshi society for approximately four hundred years. The polity of Bangladesh is also silent about its position in the three-circle model of World English’s and does not have any proper plan for the English language. Very few studies have been done on the English of Bangladesh from World English’s perspective. Methodology: Data is collected from five different sources; a survey, the Corpus of Global Web-Based English (GloWbE), the News on the Web (NOW), the Oxford English Dictionary, and one corpus compiled with 92 speeches of the former Prime Minister of Bangladesh. This corpus contains 101,490 words. The freeware corpus analysis toolkit Antconc is used to process the data of this corpus. The mixed method is followed in this work. Data is collected quantitatively and analyzed qualitatively. Findings: The result of the study reveals that Bangladeshi English has rich lexical properties. These lexis are formed in different morphological processes. A good fraction of these lexis are noun, but adjective, verb, and adverb are also present. Unique Contribution to Theory, Practice and Policy: These findings can be used as a starting point for further research.

MMLmiRLocNet: miRNA Subcellular Localization Prediction based on Multi-view Multi-label Learning for Drug Design.

Identifying subcellular localization of microRNAs (miRNAs) is essential for comprehensive understanding of cellular function and has significant implications for drug design. In the past, several computational methods for miRNA subcellular localization is being used for uncovering multiple facets of RNA function to facilitate the biological applications. Unfortunately, most existing classification methods rely on a single sequencebased view, making the effective fusion of data from multiple heterogeneous networks a primary challenge. Inspired by multi-view multi-label learning strategy, we propose a computational method, named MMLmiRLocNet, for predicting the subcellular localizations of miRNAs. The MMLmiRLocNet predictor extracts multi-perspective sequence representations by analyzing lexical, syntactic, and semantic aspects of biological sequences. Specifically, it integrates lexical attributes derived from k-mer physicochemical profiles, syntactic characteristics obtained via word2vec embeddings, and semantic representations generated by pre-trained feature embeddings. Finally, module for extracting multi-view consensus-level features and specific-level features was constructed to capture consensus and specific features from various perspectives. The full connection networks are utilized as the output module to predict the miRNA subcellular localization. Experimental results suggest that MMLmiRLocNet outperforms existing methods in terms of F1, subACC, and Accuracy, and achieves best performance with the help of multi-view consensus features and specific features extract network.

The Norwegian language in Argentina: A first look at heritage Norwegian in a new context

This paper presents preliminary observations of Norwegian as a heritage language (HL) in a contact situation that has largely gone unnoticed up until now: Argentina, where Spanish is the main contact language. We discuss some lexical, morphological, and syntactic properties of Argentine Norwegian, with Norwegian as a HL in North America as a comparative backdrop, and we point out directions for future research.

Word learning tasks as a window into the triggering problem for presuppositions

In this paper, we show that native speakers spontaneously divide the complex meaning of a new word into a presuppositional component and an assertive component. These results argue for the existence of a productive triggering algorithm for presuppositions, one that is not based on alternative lexical items nor on contextual salience. On a methodological level, the proposed learning paradigm can be used to test further theories concerned with the interaction of lexical properties and conceptual biases.

Comparative Study of Chinese Translations of “Gitanjali” based on Natural Language Processing

This paper employs the HanLP natural language processing model to conduct a quantitative text analysis of four Chinese translations of “Gitanjali”. The analysis examines text features such as character and sentence count, lexical properties, lexical density, and high-frequency words. The results indicate that, except for Wu Yan’s translation, the text features of the other translations are relatively similar in terms of data distribution. Wu Yan’s translation has higher character and sentence count, and usage of lexical properties than the other translations, but it has the lowest lexical density, indicating a lower information density. The study also reveals that the choice of lexical properties and high-frequency words in each translation is influenced by the translator’s personal language preferences. Although the types of high-frequency words are relatively fixed, their frequencies vary significantly. Based on these findings, the study further uncovers the intrinsic logic and structural relationships among different text features, offering significant insights for the study of specific literary and translation texts, as well as broader translation and linguistic research. This paper demonstrates the effectiveness of HanLP in literary and translation research, highlighting the potential of natural language processing models in large-scale text analysis.

Expanding the neighbourhood watch: Orthographic neighbours in isiXhosa reading and spelling

Background: Lexical properties such as orthographic neighbours have been shown to have an influence on reading and writing; however, this phenomenon is yet to be explored in the Southern Bantu languages.Objectives: We investigate the role of orthographic neighbourhood density and neighbourhood frequency in reading and spelling in Grade 3 isiXhosa home-language learners. The aim is to establish whether orthographic neighbours facilitate or hinder reading and spelling. The dual-route model of orthographic processing is used to interpret our findings.Method: Data were collected from 97 Grade 3 isiXhosa home-language learners. Lexical decision, word reading, and spelling tasks were administered. Tasks included both real and pseudowords varying in orthographic neighbourhood density and neighbourhood frequency, while controlling for word length and word frequency.Results: Findings show a significant inhibitory effect of orthographic neighbourhood frequency and word length for spelling accuracy. Longer words with higher frequency neighbours were more likely to be spelt incorrectly. No effect was recorded for the lexical decision and word reading tasks.Conclusion: Our findings suggest that spelling in isiXhosa relies partially on lexical processing strategies, which allows for the inhibitory effects of orthographic neighbours to be observed. While in reading, sublexical processing is more prominent, therefore mitigating the effect of orthographic neighbours.Contribution: Empirical evidence for the effects of orthographic neighbours, specifically in a Southern Bantu language, provides a clearer picture of the underlying cognitive-linguistic processes involved in reading and writing. This evidence may contribute to the development of targeted pedagogical practices to address spelling errors.

This construction needs understood: An experimental study of the Alternative Embedded Passive (AEP)

The Alternative Embedded Passive (AEP) occurs in the Midland region in variation with the infinitival passive. AEPs may occur with the verbs need, want, and like: The baby needs/wants/likes fed. Murray & Simon (2002) observe a hierarchy of acceptability and usage across the three AEP verbs, with need as the most acceptable and like as the least. Building on an idea in Edelstein (2014), this paper presents evidence that the hierarchy results from lexical semantic properties of need, want, and like. Experiment results reveal that the hierarchy is alive in the judgments of young Pennsylvania speakers, and also emerges partially in the judgments of speakers from New England unfamiliar with the construction. The hierarchy only fully emerges with Pennsylvania speakers when the subject of the sentence is sentient (e.g., the baby). With non-sentient subjects (e.g., the computer), want and like are similar and less acceptable than need. We attribute this to a property of the verb need, which can be either a deontic modal (which does not take a thematic subject) or a thematic subject-taking verb. We discuss how our results support the hypothesis that speakers adapt to novel dialectal constructions by forming analogies to constructions already in their grammars.

Coh-Metrix 시스템을 통한 고등학교 영어 모의시험 독해지문의 학년 내 연계성 분석

This corpus-driven research aims to examine the intra-grade continuity of reading passages within high school English mock exams, specifically the national academic achievement tests for seniors in 2023. These tests were conducted quarterly by four regional educational offices: Seoul in March, Incheon in April, Gyeonggi Province in July, and Seoul in October. A corpus of 100 reading passages, 25 from each test, was analyzed via a range of Coh-Metrix measures, including word features, lexical diversity, connectives, readability, referential cohesion, syntactic complexity, semantic cohesion, and so on. These Coh-metrix results demonstrated that there was no compelling evidence to support strong intra-grade continuity, highlighting the necessity for meticulous calibration of text difficulty, which should incrementally escalate with successive test administrations. The analysis of basic metrics, lexical frequency and diversity, syntactic complexity, and readability revealed no consistent trends across test administrations. Additionally, lexical attributes such as concreteness, familiarity, imagery, and age of acquisition did not show significant continuity. Moreover, the analysis of syntactic complexity also showed no consistent pattern. The current inconsistency in text difficulty suggests that students may not be receiving the progressive challenge needed to develop their reading comprehension skills effectively. Drawing upon these findings, theoretical and pedagogical implications are elucidated.

KannadaLex: A lexical database with psycholinguistic information

Databases containing lexical properties are of primary importance to psycholinguistic research and speech-language therapy. Several lexical databases for different languages have been developed in the recent past, but Kannada, a language spoken by 50.8 million people, has no comprehensive lexical database yet. To address this, KannadaLex , a Kannada lexical database is built as a language resource that contains orthographic, phonological, and syllabic information about words that are sourced from newspaper articles from the last decade. Along with these vital statistics like the phonological neighbourhood, syllable complexity summed syllable and bigram syllable frequencies, and lemma and inflectional family information are stored. The database is validated by correlating frequency, a well-established psycholinguistic feature, with other numerical features. The developed lexical database contains 170K words from varied disciplines, complete with psycholinguistic features. This KannadaLex is a comprehensive resource for psycholinguists, speech therapists, and linguistic researchers for analyzing Kannada and other similar languages. Psycholinguists require lexical data for choosing stimuli to conduct experiments that study the factors that enable humans to acquire, use, comprehend, and produce language. Speech and language therapists query these databases for developing the most efficient stimuli for evaluating, diagnosing, and treating communication disorders, and rehabilitation of speech after brain injuries.

Effects of Lexical Properties in L2 Chinese Compound Processing: A Multivariate Approach.

Previous studies on L2 (i.e., second language) Chinese compound processing have focused on the relative efficiency of two routes: holistic processing versus combinatorial processing. However, it is still unclear whether Chinese compounds are processed with multilevel representations among L2 learners due to the hierarchical structure of the characters. Therefore, taking a multivariate approach, the present study evaluated the relative influence and importance of different grain sizes of lexical information in an L2 Chinese two-character compound decision task. Results of supervised component generalized linear regression models with random forests analysis revealed that the orthographic, phonological and semantic information all contributed to L2 compound processing, but the L2 learners used more orthographic processing strategies and fewer phonological processing strategies compared to the native speakers. Specifically, the orthographic information was activated at the whole-word, the character and the radical levels in orthographic processing, and the phonological information at the whole-word, the syllable, and the phoneme levels all exerted contributions in phonological processing. Furthermore, the semantic information of the whole words and the constituents was accessed in semantic processing. These findings together suggest that the L2 learners are able to use cues at all levels simultaneously to process Chinese compound words, supporting a multi-route model with a hierarchical morphological structure in such processing.

In the Beginning Was a to-Phrase

Abstract In this paper we reply to the objections raised against a connection between Double Object Constructions (DOC) and to-constructions and present new arguments showing the empirical and theoretical advantages of derivational analyses over non-derivational ones. We argue that to-constructions and DOCs share a common substructure—where the theme is higher than the goal—that construction-based analyses fail to capture, both crosslinguistically and English-internally. We also argue that variation on the lexical properties of verbs and adpositions is the right tool to account for the alternation.

Integrating Cognitive Factors and Eye Movement Data in Reading Predictive Models for Children with Dyslexia and ADHD-I.

This study reports on several specific neurocognitive processes and eye-tracking predictors of reading outcomes for a sample of children with Developmental Dyslexia (DD) and At-tention-Deficit/Hyperactivity Disorder - inattentive subtype (ADHD-I) compared to typical readers. Participants included 19 typical readers, 21 children diagnosed with ADHD-I and 19 children with DD. All participants were attending 4th grade and had a mean age of 9.08 years. The psycholinguistic profile of each group was assessed using a battery of neuropsy-chological and linguistic tests. Participants were submitted to a silent reading task with lex-ical manipulation of the text. Multinomial logistic regression was conducted to evaluate the predictive capability of developing dyslexia or ADHD-I based on the following measures: (a) a linguistic model that included measures of phonological awareness, rapid naming, and reading fluency and accuracy; (b) a cognitive neuropsychological model that included measures of memory, attention, visual processes, and cognitive or intellectual functioning, and (c) an additive model of lexical word properties with manipulation of word-frequency and word-length effects through eye-tracking. The additive model in conjunction with the neuropsychological model classification improved the prediction of who develops dyslexia or ADHD-I having as baseline normal readers. Several of the neuropsychological and eye-tracking variables have power to predict the degree of reading outcomes in children with learning disabilities.

The utility of (political) dogwhistles – a life cycle perspective

Abstract The term dogwhistle refers to an expression conveying a message to a subset of an audience which is not perceived by the rest of the group, in addition to a primary meaning directed at the group at large. We follow up on previous work in linguistics and political communication on defining dogwhistles, taking into account how they likely function in real-life political contexts. We consider the utility of dogwhistles in terms of their sensitivity and their specificity, which allows us to consider dogwhistles in terms of an idealized “life cycle”, whose phases we describe in terms of a multi-dimensional utility tradeoff, described in terms of dogwhistle users, the benefit they expect to receive from dogwhistling, and the deniability of controversial dogwhistle meanings. We propose an approach for the longitudinal study of dogwhistles, and describe the first stages of an experiment to characterize dogwhistles in terms of their lexical properties.

The Cerebellum Is Sensitive to the Lexical Properties of Words During Spoken Language Comprehension.

Over the past few decades, research into the function of the cerebellum has expanded far beyond the motor domain. A growing number of studies are probing the role of specific cerebellar subregions, such as Crus I and Crus II, in higher-order cognitive functions including receptive language processing. In the current fMRI study, we show evidence for the cerebellum's sensitivity to variation in two well-studied psycholinguistic properties of words-lexical frequency and phonological neighborhood density-during passive, continuous listening of a podcast. To determine whether, and how, activity in the cerebellum correlates with these lexical properties, we modeled each word separately using an amplitude-modulated regressor, time-locked to the onset of each word. At the group level, significant effects of both lexical properties landed in expected cerebellar subregions: Crus I and Crus II. The BOLD signal correlated with variation in each lexical property, consistent with both language-specific and domain-general mechanisms. Activation patterns at the individual level also showed that effects of phonological neighborhood and lexical frequency landed in Crus I and Crus II as the most probable sites, though there was activation seen in other lobules (especially for frequency). Although the exact cerebellar mechanisms used during speech and language processing are not yet evident, these findings highlight the cerebellum's role in word-level processing during continuous listening.

Individual word representations dissociate from linguistic context along a cortical unimodal to heteromodal gradient.

Language comprehension involves multiple hierarchical processing stages across time, space, and levels of representation. When processing a word, the sensory input is transformed into increasingly abstract representations that need to be integrated with the linguistic context. Thus, language comprehension involves both input-driven as well as context-dependent processes. While neuroimaging research has traditionally focused on mapping individual brain regions to the distinct underlying processes, recent studies indicate that whole-brain distributed patterns of cortical activation might be highly relevant for cognitive functions, including language. One such pattern, based on resting-state connectivity, is the 'principal cortical gradient', which dissociates sensory from heteromodal brain regions. The present study investigated the extent to which this gradient provides an organizational principle underlying language function, using a multimodal neuroimaging dataset of functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) recordings from 102 participants during sentence reading. We found that the brain response to individual representations of a word (word length, orthographic distance, and word frequency), which reflect visual; orthographic; and lexical properties, gradually increases towards the sensory end of the gradient. Although these properties showed opposite effect directions in fMRI and MEG, their association with the sensory end of the gradient was consistent across both neuroimaging modalities. In contrast, MEG revealed that properties reflecting a word's relation to its linguistic context (semantic similarity and position within the sentence) involve the heteromodal end of the gradient to a stronger extent. This dissociation between individual word and contextual properties was stable across earlier and later time windows during word presentation, indicating interactive processing of word representations and linguistic context at opposing ends of the principal gradient. To conclude, our findings indicate that the principal gradient underlies the organization of a range of linguistic representations while supporting a gradual distinction between context-independent and context-dependent representations. Furthermore, the gradient reveals convergent patterns across neuroimaging modalities (similar location along the gradient) in the presence of divergent responses (opposite effect directions).

Can large language models help augment English psycholinguistic datasets?

Research on language and cognition relies extensively on psycholinguistic datasets or “norms”. These datasets contain judgments of lexical properties like concreteness and age of acquisition, and can be used to norm experimental stimuli, discover empirical relationships in the lexicon, and stress-test computational models. However, collecting human judgments at scale is both time-consuming and expensive. This issue of scale is compounded for multi-dimensional norms and those incorporating context. The current work asks whether large language models (LLMs) can be leveraged to augment the creation of large, psycholinguistic datasets in English. I use GPT-4 to collect multiple kinds of semantic judgments (e.g., word similarity, contextualized sensorimotor associations, iconicity) for English words and compare these judgments against the human “gold standard”. For each dataset, I find that GPT-4’s judgments are positively correlated with human judgments, in some cases rivaling or even exceeding the average inter-annotator agreement displayed by humans. I then identify several ways in which LLM-generated norms differ from human-generated norms systematically. I also perform several “substitution analyses”, which demonstrate that replacing human-generated norms with LLM-generated norms in a statistical model does not change the sign of parameter estimates (though in select cases, there are significant changes to their magnitude). I conclude by discussing the considerations and limitations associated with LLM-generated norms in general, including concerns of data contamination, the choice of LLM, external validity, construct validity, and data quality. Additionally, all of GPT-4’s judgments (over 30,000 in total) are made available online for further analysis.

Severe processing capacity limits for sub-lexical features of letter strings

When reading, the visual system is confronted with many words simultaneously. How much of that information can a reader process at once? Previous studies demonstrated that low-level visual features of multiple words are processed in parallel, but lexical attributes are processed serially, for one word at a time. This implies that an internal bottleneck lies somewhere between early visual and lexical analysis. We used a dual-task behavioral paradigm to investigate whether this bottleneck lies at the stage of letter recognition or phonological decoding. On each trial, two letter strings were flashed briefly, one above and one below fixation, and then masked. In the letter identification experiment, participants indicated whether a vowel was present in a particular letter string. In the phonological decoding experiment, participants indicated whether the letter string was pronounceable. We compared accuracy in a focused attention condition, in which participants judged only one of the two strings, with accuracy in a divided attention condition, in which participants judged both strings independently. In both experiments, the cost of dividing attention was so large that it supported a serial model: participants were able to process only one letter string per trial. Furthermore, we found a stimulus processing trade-off that is characteristic of serial processing: When participants judged one string correctly, they were less likely to judge the other string correctly. Therefore, the bottleneck that constrains word recognition under these conditions arises at a sub-lexical level, perhaps due to a limit on the efficiency of letter recognition.

გვარის გრამატიკული კატეგორიისათვის მეგრულ-ლაზურში

Verbal voice is topical not only from the Kartvelian but also from a general linguistics viewpoint. It is discussed differently in the European and the Georgian linguistic literature. In Georgian, e.g., voice is considered as a lexical-grammatical (A. Shanidze), morphological (A. Chikobava), syntactic-morphological (B. Jorbenadze) or semantical (D. Melikishvili) category. The issue of establishing systems of opposition in the category of voice is also controversial and topical. Such unresolved problems related to verbal voice require a new understanding of the category and approaches that take into account the specifics of the given research language.In Megrelian-Laz, voice is considered as a grammatical category and according to their morphosyntactic behaviour, two voices are distinguished: The type R-AUX as in avǯiluġi ikips ‘does hunting (= hunts)’ is characteristic only of Laz. It is created as an analogy to Turkish formations (avcılık etmek).The present classification is based on a structured database of verb forms created by the author, which comprises up to 10,000 entries. Directly transitive, indirectly transitive, and intransitive forms of active semantics are grouped together in the database (Laz: o-xvec’-ap-s ‘begs’, Megr: u-per-u-an-s ‘is affectionate to sb/sth’, Zan: ġarġal-ap-s / ragad-an-s ‘speaks’): According to their formation, two types of passives can be distinguished in Megrelian-Laz: independent and conversive.A passive is independent if it is not derived from an active stem via conversion. However, according to the nature of passive, its formation can also be secondary; in particular, in most forms it is based on nominal stems (cf. Zan xomu ‘dried’ > xomu-u-n ‘gets dry’, Laz č’itoni ‘reddish’ > Megr. č’iton-d-u-n ‘gets reddish’). This is why the present author strictly differentiates voice and transitivity. From the category of voice, she separates reflexives of more archaic formation (e.g. i-gurgin-u-n ‘it thunders’, i-rdgabal-u-n ‘talks a lot of rubbish’) which often cause confusion.The typological analysis of Zan verbal forms confirms that, structurally, Megrelian-Laz and Georgian belong to the same type although the formal characteristics of the voice category developed independently in Zan. The patterns can even be different between Megrelian and Laz, but in most cases, content and grammatical form are in complete harmony with each other in Zan. The morphology of Megrelian and Laz is much more regular than that of Svan and Georgian.Despite the high degree of correspondences, grammatical voice cannot be regarded only as a lexical property of a given verb since the verbal voices do not include all realisable forms in Megrelian-Laz. Voice permits the classification of verbs and their division into two categories only according to grammatical (or morphosyntactical) properties.

Evaluating Familiarity Ratings of Domain Concepts with Interpretable Machine Learning: A Comparative Study

Psycholinguistic properties such as concept familiarity and concreteness have been investigated in relation to technological innovations in teaching and learning. Due to ongoing advances in semantic representation and machine learning technologies, the automatic extrapolation of lexical psycholinguistic properties has received increased attention across a number of disciplines in recent years. However, little attention has been paid to the reliable and interpretable assessment of familiarity ratings for domain concepts. To address this gap, we present a regression model grounded in advanced natural language processing and interpretable machine learning techniques that can predict domain concepts’ familiarity ratings based on their lexical features. Each domain concept is represented at both the orthographic–phonological level and semantic level by means of pretrained word embedding models. Then, we compare the performance of six tree-based regression models (adaptive boosting, gradient boosting, extreme gradient boosting, a light gradient boosting machine, categorical boosting, and a random forest) on domain concepts’ familiarity rating prediction. Experimental results show that categorical boosting with the lowest MAPE (0.09) and the highest R2 value (0.02) is best suited to predicting domain concepts’ familiarity. Experimental results also revealed the prospect of integrating tree-based regression models and interpretable machine learning techniques to expand psycholinguistic resources. Specifically, findings showed that the semantic information of raw words and parts of speech in domain concepts are reliable indicators when predicting familiarity ratings. Our study underlines the importance of leveraging domain concepts’ familiarity ratings; future research should aim to improve familiarity extrapolation methods. Scholars should also investigate the correlation between students’ engagement in online discussions and their familiarity with domain concepts.