Semantic Types Research Articles

A Study on the Acquisition of Possession Constructions in Chinese Preschool Children’s Natural Utterances

Based on the natural utterances of six Chinese-speaking preschool children, this paper examines the syntactic and semantic features of possession constructions. First of all, in terms of the syntax, there are three main types of possession constructions, including adnominal, predicative, and external possessions, as well as eight variations. Meanwhile, the older the children are, the more syntactic variations and frequencies they produce, and the more complex their syntactic structures become, following a developmental pattern from simple to complex. Secondly, in terms of semantics, the children express four main types of semantics: interpersonal, whole-part, ownership and spatiotemporal relationships, involving nine subtypes, such as social relationships, kinship and so on. The longitudinal features of the semantics have a developmental path from familiar to unfamiliar, concrete to abstract, and single to diverse. Finally, Studies have shown that children actively and gradually construct language in their interactions with adults, in a certain degree, which means that the driving force for children’s syntactic acquisition and development comes from language use.

A Comparative Study of Korean and Chinese Counter-expectation Markers ‘Gikkotaeya’ and ‘Dabuliao’

This paper mainly takes the typical permissive expectation markers ‘gikkotaeya’ and ‘dabuliao’ in Korean and Chinese as the research objects, and makes a comparative analysis of the expectation characteristics and subjective quantities, and the following conclusions can be drawn: In terms of similarities, these two expressions have commonalities in semantic types, internal semantic associations, and lexicalization processes. In terms of differences, in terms of semantic types, the semantic type of ‘gikkotaeya’ representing subjective emotional attitude is more typical, and the semantic type of ‘dabuliao’ representing ‘small quantity’ and ‘situation maintain’ is more characteristic; in terms of internal semantic association, ‘gikkotaeya’ is more typical than ‘dabuliao’ in counter-expectaion and subjective expression; in the process of lexicalization, ‘gikkotaeya’ has a variety of extensions, and ‘dabuliao’ has a relatively single path, which is mainly affected by objective quantities. In terms of the motives for differences, it is mainly related to the strength of subjectivity, the interaction between subjective quantity and expectation category.

Developing a machine learning model to detect diagnostic uncertainty in clinical documentation.

Diagnostic uncertainty, when unrecognized or poorly communicated, can result in diagnostic error. However, diagnostic uncertainty is challenging to study due to a lack of validated identification methods. This study aims to identify distinct linguistic patterns associated with diagnostic uncertainty in clinical documentation. This case-control study compares the clinical documentation of hospitalized children who received a novel uncertain diagnosis (UD) diagnosis label during their admission to a set of matched controls. Linguistic analyses identified potential linguistic indicators (i.e., words or phrases) of diagnostic uncertainty that were then manually reviewed by a linguist and clinical experts to identify those most relevant to diagnostic uncertainty. A natural language processing program categorized medical terminology into semantic types (i.e., sign or symptom), from which we identified a subset of these semantic types that both categorized reliably and were relevant to diagnostic uncertainty. Finally, a competitive machine learning modeling strategy utilizing the linguistic indicators and semantic types compared different predictive models for identifying diagnostic uncertainty. Our cohort included 242 UD-labeled patients and 932 matched controls with a combination of 3070 clinical notes. The best-performing model was a random forest, utilizing a combination of linguistic indicators and semantic types, yielding a sensitivity of 89.4% and a positive predictive value of 96.7%. Expert labeling, natural language processing, and machine learning methods combined with human validation resulted in highly predictive models to detect diagnostic uncertainty in clinical documentation and represent a promising approach to detecting, studying, and ultimately mitigating diagnostic uncertainty in clinical practice.

Types and functions of insubordinate complement clauses with hogy ‘that’ in Hungarian

The aim of this paper is to give a constructional typology, on the basis of a corpus study, of the semantic types of Hungarian insubordinate clauses introduced by the complementizer hogy ‘that’, along with discussing the difficulties of their identification and description, especially the problems raised by the multiple part-of-speech affiliation of hogy (interrogative pronoun, conjunction, particle). In Hungarian, as in other languages, stand-alone versions (wishes, evaluations) that are both syntactically and pragmatically independent can fundamentally be told apart from pragmatically non-independent elaborative clauses (with and without discourse markers). However there are transitional cases between those two extremes (mixed stand-alone-elaborative clauses), as well as within the types (wishing evaluative clauses). In terms of their sentence type affiliation, too, several insubordinate variants exist that can be seen as mixed instances (curses). Pure and transitional cases were separated according to the following parameters: constructional semantic type, structural features (sentence type, verb mood, exaggerative particle is present or not), discourse markers is present or not. Furthermore, transitional cases can arise due to the existence of constructions that exclusively occur with interjections or sentence adverbials, whereas in other types the use of these items is optional. The pragmatic functions of insubordinate constructions are dominantly uniform on the basis of questionnaire studies conducted with native participants: they express emphasis or are highly loaded emotionally.

Caregiver linguistic alignment to autistic and typically developing children: A natural language processing approach illuminates the interactive components of language development

BackgroundLanguage development is a highly interactive activity. However, most research on linguistic environment has focused on the quantity and complexity of linguistic input to children, with current models showing that complexity facilitates language in both typically developing (TD) and autistic children. AimsAfter reviewing existing work on caregiver engagement of children's utterances, we aim to operationalize such engagement with automated measures of linguistic alignment, thereby providing scalable tools to assess caregivers' active reuse of their children's language. By assessing the presence of alignment, its sensitivity to the child's individual differences and how well it predicts language development beyond current models across the two groups, we showcase the usefulness of the approach and provide initial empirical foundations for further conceptual and empirical investigations. MethodsWe measure lexical, syntactic and semantic types of caregiver alignment in a longitudinal corpus involving 32 adult-autistic child and 35 adult-TD child dyads, with children between 2 and 5 years of age. We assess the extent to which caregivers repeat their children's words, syntax, and semantics, and whether these repetitions predict language development beyond more standard predictors. ResultsCaregivers tend to re-use their child's language in a way that is related to the child's individual, primarily linguistic, differences. Caregivers' alignment provides unique information improving our ability to predict future language development in both typical and autistic children. ConclusionsWe provide evidence that language development also relies on interactive conversational processes, previously understudied. We share carefully detailed methods, and open-source scripts so as to systematically extend our approach to new contexts and languages.

Component segmentation of engineering drawings using Graph Convolutional Networks

We present a data-driven framework to automate the vectorization and machine interpretation of 2D engineering part drawings. In industrial settings, most manufacturing engineers still rely on manual reads to identify the topological and manufacturing requirements from drawings submitted by designers. The interpretation process is laborious and time-consuming, which severely inhibits the efficiency of part quotation and manufacturing tasks. While recent advances in image-based computer vision methods have demonstrated great potential in interpreting natural images through semantic segmentation approaches, the application of such methods in parsing engineering technical drawings into semantically accurate components remains a significant challenge. The severe pixel sparsity in engineering drawings also restricts the effective featurization of image-based data-driven methods. To overcome these challenges, we propose a deep learning based framework that predicts the semantic type of each vectorized component. Taking a raster image as input, we vectorize all components through thinning, stroke tracing, and cubic bezier fitting. Then a graph of such components is generated based on the connectivity between the components. Finally, a graph convolutional neural network is trained on this graph data to identify the semantic type of each component. We test our framework in the context of semantic segmentation of text, dimension and, contour components in engineering drawings. Results show that our method yields the best performance compared to recent image-based, and graph-based segmentation methods.

Morlille kan ikke flyve

This paper deals with natural language (NL) semantics an the computer. It presents two major theoretical vicwpoints in linguistic semantics: representational and denotational. In linguistic theory, these two issues are mostly understood as mutually exclusive, as can be seen from Chomskyan and Montague Grammar. The paper discusses representational semantics in connection with the autonomous syntax approach of mainstream transformational grammar, denotational semantics in connection with the model-theoretic approach of Montague grammar. In the next step, I consider the implementation of model-theoretic NL comprehension systems on the computer. In the present context, they are viewed principally as a means to make experimental studies in the formalizations of NL. It is shown that the correct handling of referential NL terms in such systems can be achicved only if these systems combine methods and techniques from both types of semantics. Finally, the cognitive meaning of the concept of computer understanding is touched upon.

Sinonim dalam Berita Agama pada Media Online BBC Arabic

This research aims to identify and collect synonyms in religious news about Hajj published in BBC Arabic online media. The method used is a descriptive qualitative, focusing on lexical cohesion analysis involving synonyms. The primary purpose of this research is to understand the variation of synonym usage in the context of religious news about Hajj and to classify the types of synonyms found. Data for this research was collected through documentation, using sources from news published by BBC Arabic. The research results indicate that 14 synonyms were found in the news. These synonyms can be classified into several types, including similar synonyms (Shibhu al-tarāduf), semantic relationship types (Al-taqārub al-dalālī), implication types (Istilzām), paraphrase types), and interpretation. This analysis provides a deeper understanding of the use of synonyms in the context of religious news about Hajj. It shows the richness of the Arabic language in conveying information on the topic. This research has important implications in linguistics, especially in understanding how synonyms are used in specific religious news contexts. This research also significantly contributes to the development of text analysis methodology and Arabic language research in general libraries.

Semantic Difficulty for Bilingual Children: Effects of Age, Language Exposure, and Language Ability.

Semantic tasks evaluate dimensions of children's lexical-semantic knowledge. However, the relative ease of semantic task completion depends on individual differences in developmental and language experience factors. The purpose of this study was to evaluate how language experience and language ability impact semantic task difficulty in English for school-age Spanish-English bilingual children with and without developmental language disorder (DLD). Participants included 232 Spanish-English bilingual children in second through fifth grade with (n = 35) and without (n = 197) DLD. Data included children's performance on the English Semantics subtest of the Bilingual English-Spanish Assessment-Middle Extension Field Test Version (BESA-ME), age of English acquisition, and percent English language exposure. Task difficulty, a measurement of the relative ease of task completion, was calculated for six semantic task types included on the BESA-ME. Multilevel regression modeling was conducted to estimate longitudinal growth trajectories for each semantic task type. Results showed that language ability and grade level drive semantic task difficulty for all task types, and children with DLD experienced greater difficulty on all task types compared to their typically developing peers. Longitudinally, semantic task difficulty decreased for all children, regardless of language ability, indicating that semantic task types became easier over time. While children made gains on all semantic tasks, the growth rate of task difficulty was not equal across task types, where some task types showed slower growth compared with others. English language exposure emerged as a significant predictor of semantic task difficulty while age of acquisition was not a significant factor. This study clarifies developmental profiles of lexical-semantic performance in bilingual children with and without DLD and supports clinical decision-making regarding children's English language learning.

States and changes-of-state in the semantics of result roots

A major challenge for event structural theories that decompose verbs into event templates and roots relates to the syntactic distribution of roots and what types of event structures roots can be integrated into. Ontological Approaches propose roots fall into semantic classes, such as manner versus result, which determine root distribution (Rappaport Hovav and Levin 1998, 2010). Free Distribution Approaches, in contrast, hold that root distribution is not constrained by semantic content and roots are free to integrate into various types of event structures (Borer 2005; Acedo-Matellán and Mateu 2014). We focus on two different classes of verbs classified as result verbs in Rappaport Hovav and Levin’s (1998, 2010) sense and their ability to appear in resultative constructions. We build on Beavers and Koontz-Garboden’s (2012, 2020) proposal that the roots underlying these verbs fall into two classes: property concept roots, which denote relations between individuals and states, and change-of-state roots, which on our proposal, denote relations between individuals and events of change. We show that change-of-state roots, but not property concept roots, are able to appear in the modifier position of resultative constructions by providing naturally occurring examples of such resultatives. Combining the proposed lexical semantics of these two classes of roots with a reformulation of an Ontological Approach solely dependent on a root’s semantic type, we show that this analysis makes novel and accurate predictions about the possibility of the two classes of roots appearing in resultative constructions and the range of interpretations available when change-of-state roots are integrated into resultative event structure templates.

Onomatopoeia in representing the semantics of love, affection, and gender relations

The paper describes the sound symbolic nature of linguistic units with the semantics of love, affection, gender relations in German and Russian. It is known that onomatopoeic words with the semantics of blow, the physiological sound of the body and the sounds of speech in German and Russian languages regularly form associated with love, affection, and gender relations. In most cases, this type of semantics is expressed by combination of plosives and sonorants sounds, and also vibrants. Inter-relation between sound and meaning, as well as the soundiconic functions of phonotypes is clarified: voiceless fricative sounds – reflection of pejorativeness, less often – femininity (dearness and softness); explosive and vibrant sounds –reflection of strong feelings, pressure, perseverance, aggressiveness in a relationship.

A Brief Analysis of the Metaphorical Meaning of "eat +NP"

In modern Chinese, the verb-object structure composed of "eat +NP" is widely used in modern Chinese vocabulary, and has become an indispensable daily language. The archetypal sense of the verb "to eat" is to put food, etc. into the mouth and then swallow it. As a kind of behavior representing spatial structure, "eating" generates a series of metaphorical meanings through the metaphorical projection to the abstract cognitive domain similar to the structure. The systematization and coherence of conceptual metaphor are of great significance to the study of the semantic structure type of "eat +NP". Based on the conceptual metaphor theory, this paper analyzes the action metaphor meaning of the verb "eat" in Chinese, which can help people better understand the unconventional collocation of "eat".

Towards a Typology of Diachronic Sources of Adversative Markers

This paper is dedicated to the diachronic sources of adversative markers in the worlds languages. It presents a typological classification of meanings from which the linguistic units that have the meanings but, however, nevertheless or whereas (the meanings но, однако) have apparently developed. The classification includes the following sources, which are absent from the previous classifications that we have consulted (or need refinement): hesitant substitution (rather, more like), focalization (exactly), invariable outcome (be that as it may), absence of a hindrance (without hindrance), and also such discourse functions as repetition (again), transition (anyway, now) and continuity (next). The distinction between four semantic types of adversative coordination (concessive, substitutive, oppositive and argumentative) that we make allows us to show the processes of grammaticalization in more detail. We sum up the apparent semantic shifts by putting them onto a semantic map. Our observations are based on the established examples of diachronic semantic shifts, etymological data, and instances of synchronic polysemy.

On the Semantic Typology of Adjective Derivation in the Tungus-Manchu Languages

The article deals with parameters for the study of adjectival derivation from the point of view of semantic typology. The bases of semantic typology of word formation and classification of derived adjectives are considered. It is shown that the parameters of Eurocentric standard classifications are shifted towards formal-content samples (derivative formant ~ formant value). The Tungus-Manchu languages demonstrate a possible system in which a significant role has the correlation between a (specialized) formant with a general meaning of ʻpropertyʼ and a limited semantic class of derivational bases. Of particular typological interest are the principles of semantic selection of derivational bases. In the Tungus-Manchu languages, there is a peculiar specialization of suffixes on bases with perceptual features (ʻcolorʼ, ʻtasteʼ, ʻsmellʼ, etc.), as well as on other core semantic types of adjectives. The distribution of individual characteristics of the Tungus-Manchu word formation within the language subgroup and against the areal background is traced.

MedLexSp – a medical lexicon for Spanish medical natural language processing

BackgroundMedical lexicons enable the natural language processing (NLP) of health texts. Lexicons gather terms and concepts from thesauri and ontologies, and linguistic data for part-of-speech (PoS) tagging, lemmatization or natural language generation. To date, there is no such type of resource for Spanish.Construction and contentThis article describes an unified medical lexicon for Medical Natural Language Processing in Spanish. MedLexSp includes terms and inflected word forms with PoS information and Unified Medical Language System^{circledR } (UMLS) semantic types, groups and Concept Unique Identifiers (CUIs). To create it, we used NLP techniques and domain corpora (e.g. MedlinePlus). We also collected terms from the Dictionary of Medical Terms from the Spanish Royal Academy of Medicine, the Medical Subject Headings (MeSH), the Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT), the Medical Dictionary for Regulatory Activities Terminology (MedDRA), the International Classification of Diseases vs. 10, the Anatomical Therapeutic Chemical Classification, the National Cancer Institute (NCI) Dictionary, the Online Mendelian Inheritance in Man (OMIM) and OrphaData. Terms related to COVID-19 were assembled by applying a similarity-based approach with word embeddings trained on a large corpus. MedLexSp includes 100 887 lemmas, 302 543 inflected forms (conjugated verbs, and number/gender variants), and 42 958 UMLS CUIs. We report two use cases of MedLexSp. First, applying the lexicon to pre-annotate a corpus of 1200 texts related to clinical trials. Second, PoS tagging and lemmatizing texts about clinical cases. MedLexSp improved the scores for PoS tagging and lemmatization compared to the default Spacy and Stanza python libraries.ConclusionsThe lexicon is distributed in a delimiter-separated value file; an XML file with the Lexical Markup Framework; a lemmatizer module for the Spacy and Stanza libraries; and complementary Lexical Record (LR) files. The embeddings and code to extract COVID-19 terms, and the Spacy and Stanza lemmatizers enriched with medical terms are provided in a public repository.

On the Grammatical Basis of Single-Compound Sentences in the Russian and Ingush Languages

The subject of this study is to identify varieties of single–compound sentences in two genetically unrelated languages - Russian and Ingush, to describe the structural and semantic properties of the specified syntactic unit from the typological aspect. The following tasks are put forward in this scientific article: firstly, to conduct a comprehensive analysis of the concepts of the predicative center of one-part sentences of the Russian language in comparison with unrelated Ingush; secondly, to identify criteria for determining the types of one-part sentences in the Russian and Ingush languages; thirdly, to conduct a structural and semantic analysis of the predication of the types of one-part sentences. Sentences from two languages studied: Russian and Ingush. The scientific novelty of the study is that for the first time the main structural and semantic features and grammatical ways of expressing the predicative basis of a one–part sentence of two different-structured languages are revealed from a comparative point of view. In addition, the scientific novelty is due to the fact that the predicative basis of a single-compound sentence in nominative Russian and ergative Ingush languages has not been sufficiently studied from a typological point of view: to date, there are no or few monographs, scientific articles and other scientific works in which it would be elaborated in detail or aspect. As a result of the study, seven varieties of single-compound sentences were identified and it was proved that, despite some structural and semantic differences, in general, the predication and types of single-compound sentences coincide in the Russian and Ingush languages.

Supporting SNOMED CT postcoordination with knowledge graph embeddings

SNOMED CT postcoordination is an underused mechanism that can help to implement advanced systems for the automatic extraction and encoding of clinical information from text. It allows defining non-existing SNOMED CT concepts by their relationships with existing ones. Manually building postcoordinated expressions is a difficult task. It requires a deep knowledge of the terminology and the support of specialized tools that barely exist. In order to support the building of postcoordinated expressions, we have implemented KGE4SCT: a method that suggests the corresponding SNOMED CT postcoordinated expression for a given clinical term. We leverage on the SNOMED CT ontology and its graph-like structure and use knowledge graph embeddings (KGEs). The objective of such embeddings is to represent in a vector space knowledge graph components (e.g. entities and relations) in a way that captures the structure of the graph. Then, we use vector similarity and analogies for obtaining the postcoordinated expression of a given clinical term. We obtained a semantic type accuracy of 98%, relationship accuracy of 90%, and analogy accuracy of 60%, with an overall completeness of postcoordination of 52% for the Spanish SNOMED CT version. We have also applied it to the English SNOMED CT version and outperformed state of the art methods in both, corpus generation for language model training for this task (improvement of 6% for analogy accuracy), and automatic postcoordination of SNOMED CT expressions, with an increase of 17% for partial conversion rate.

RECA: Related Tables Enhanced Column Semantic Type Annotation Framework

Understanding the semantics of tabular data is of great importance in various downstream applications, such as schema matching, data cleaning, and data integration. Column semantic type annotation is a critical task in the semantic understanding of tabular data. Despite the fact that various approaches have been proposed, they are challenged by the difficulties of handling wide tables and incorporating complex inter-table context information. Failure to handle wide tables limits the usage of column type annotation approaches, while failure to incorporate inter-table context harms the annotation quality. Existing methods either completely ignore these problems or propose ad-hoc solutions. In this paper, we propose Related tables Enhanced Column semantic type Annotation framework (RECA), which incorporates inter-table context information by finding and aligning schema-similar and topic-relevant tables based on a novel named entity schema. The design of RECA can naturally handle wide tables and incorporate useful inter-table context information to enhance the annotation quality. We conduct extensive experiments on two web table datasets to comprehensively evaluate the performance of RECA. Our results show that RECA achieves support-weighted F1 scores of 0.853 and 0.937 with macro average F1 scores of 0.674 and 0.783 on the two datasets respectively, which outperform the state-of-the-art methods.

The reuse of DCMI metadata terms in linked open vocabulary

PurposeThis study aims to explore the reusing of Dublin core metadata initiative (DCMI) metadata terms on the linked open vocabulary (LOV) platform in the linked data environment to offer a better understanding of the reusing behaviour during the process of vocabulary construction and further explain why DC has become a popular vocabulary.Design/methodology/approachThe authors selected LOV, as a typical linked data platform. The SPARQL language was used to acquire and parse data to examine the reuse types of DCMI terms, the reuse distribution of classes and properties in different semantic relation types among vocabularies, the subject and size of the reused vocabularies and the correlation between vocabulary reuse and data set reuse.FindingsResults showed that DCMI metadata terms were reused by 83.7% of LOV vocabularies and became the core nodes on the vocabulary-linked network. Among the six relationships between vocabularies and the DCMI metadata terms, the metadata relationship is the most frequently used. DCMI metadata terms are reused by small- and medium-sized vocabularies and are not limited to subject domain.Originality/valueThis is one of the first studies focussing on the roles of DCMI metadata terms in vocabulary reusing. Furthermore, it provides a systematic view of how these DCMI terms participate in the construction of other vocabularies and in features of reused vocabularies.

Высказывания с семантикой подконтрольности

This article fits into the circle of studies of semantic categories. In it, the author considers the semantic type of sentences with the semantics of accountability, which is a subcategory of controllability and is characterized by greater semantic complexity. It is noted that all sentences of this semantic type are polypropositive, regardless of the structure of the sentence. One of the propositions reflects the controlling situation, the other the controlled one. Each control situation includes the following components: subject of control + predicate + object of control. The object of control is the controlled situation with its subject and predicate. The article gives the characteristics of all components of the semantic structure, distinguishes two typesof controllability: with external control and self-control. It is fixed that the distinguished two types of accountability react differently to the introduction of negation. Attention is drawn to the fact that a change or loss of at least one of the differential features of the components of the semantic structure takes the sentence beyond the boundaries of this semantic type and allows it to express related semantics on the basis of controllability/uncontrollability. This allows us to speak about the diffuseness of the semantics of controllability/uncontrollability. A set of different syntactic constructions is revealed to express the studied semantics. It is shown that the choice of construction is connected with the intention of the speaker: to present the situation under control as dominant or background. It is noted that typical linguistic markers of the semantics of controllability have been semantically “emptied” in recent years, losing their specific content or being used as euphemisms, which explains their increased (according to the National Corpus of the Russian Language) frequency of use.