Semantic Categories Of Words Research Articles

A Semantic Analysis of the Top Ten Network Buzzwords of China in 2020 from the Perspective of the Prototype Theory

With the further development of the Internet and the rapid growth of the number of netizens, there are always new network buzzwords appearing during online communication. Most of these buzzwords don’t come out of nowhere but are variants of the extant words or expressions. They inherit, extend or sometimes overturn the original meanings. To some extent, these semantic variations not only influence the language system, expanding the semantic category of words, but also reflect people’s cognition and emotions. Therefore, based on the prototype theory, this paper aims to analyze the semantic variations of the top ten network buzzwords of China in 2020 from three aspects: horizontally, the extension of meanings of the basic-level words; vertically, the emergence of new subordinate-level words; and the combination of the two ways of variation. The analysis of these words shows complex emotions of people, including positive life attitudes, nostalgia to the past and helplessness under huge pressures in real life.

The Measurement of Chinese Sentence Semantic Complexity

The complexity of language is usually reflected in the complexity of sentences. At present, the research of sentence complexity mainly focuses on the analysis of syntactic complexity. In this paper, from the perspective of Leech's theory of sentence semantic structure, the predication structure is taken as the semantic unit to explore the sentence semantic complexity. The predication structures are extracted based on the result of sentence-based syntactic analysis, and then the linear expression sequence of a sentence is converted into a semantic hierarchy based on predicate semantic frameworks; the universality of predicate semantic frameworks is obtained by using the spectral clustering algorithm; and the sentence semantic complexity depends on the universality of predicate semantic frameworks at various layers. The experimental results show that the measurement method of sentence sematic complexity based on predicate semantic frameworks is more effective by comparing with the method that only considers the semantic categories of words in the sentence.

Distinct fronto-temporal substrates of distributional and taxonomic similarity among words: evidence from RSA of BOLD signals

A class of semantic theories defines concepts in terms of statistical distributions of lexical items, basing meaning on vectors of word co-occurrence frequencies. A different approach emphasizes abstract hierarchical taxonomic relationships among concepts. However, the functional relevance of these different accounts and how they capture information-encoding of lexical meaning in the brain still remains elusive.We investigated to what extent distributional and taxonomic models explained word-elicited neural responses using cross-validated representational similarity analysis (RSA) of functional magnetic resonance imaging (fMRI) and model comparisons.Our findings show that the brain encodes both types of semantic information, but in distinct cortical regions. Posterior middle temporal regions reflected lexical-semantic similarity based on hierarchical taxonomies, in coherence with the action-relatedness of specific semantic word categories. In contrast, distributional semantics best predicted the representational patterns in left inferior frontal gyrus (LIFG, BA 47). Both representations coexisted in the angular gyrus supporting semantic binding and integration. These results reveal that neuronal networks with distinct cortical distributions across higher-order association cortex encode different representational properties of word meanings. Taxonomy may shape long-term lexical-semantic representations in memory consistently with the sensorimotor details of semantic categories, whilst distributional knowledge in the LIFG (BA 47) may enable semantic combinatorics in the context of language use.Our approach helps to elucidate the nature of semantic representations essential for understanding human language.

SEMANTIC GROUP IN THE LANGUAGE CORPUS, THE IMPORTANCE OF SEMANTIC TEXT SETTING OF GROUP AND FIELD EXPLANATION

There are morphological, syntactic, semantic, accentic aspects of layout in the language corpus. The corpus is the most convenient means of observing the language fluctuation of the dictionary (neologism, istorism, archaism) and its material must be linguistically processed. The complex of tags is the main tool for the implementation of this linguistic process (analysis). By monitoring the surroundings of this or that linguistic unit, it is possible to identify certain semantic characters that characterize this unity. The word may refer to a few semantic categories simultaneously, so it is important to consider the level of the word or the category of the word. The rank is determined by counting the distribution frequency according to different categories. Thus, by means of a corpus it is possible to find out the semantic category of words and to get acquainted with the meaning of each category. A set of semantic tags should be perfectly designed so that the body can indicate which semantic category it belongs to. This article discusses the significance of semantic groups, colonies and domains in the development of a set of semantic labels. The set of semantic tags is based on the semantic field, semantic colon, and LSG. On the first page of the interface semantic fields of the Uzbek language are given, if a semantic field is selected, the second window opens, the semantic groups of the same area appear, semantic gangs are again separated from the LSG. This structure of the interface works on the basis of the analysis of “analyze the word” principle. At the stage of morphological markup for the horse horoscope, the semantic area affiliation also refers to an explanation of how the LMG enters the label. The use of the semantic theory of the body in the case of a corpus can also be the basis for the ideological arc of the body.

The functional relevance of dorsal motor systems for processing tool nouns– evidence from patients with focal lesions

This study asks whether lesions in different parts of the brain have different effects on the processing of words typically used to refer to objects with and without action affordances, for example tools and animal-related nouns. A cohort of neurological patients with focal lesions participated in a lexical decision paradigm where nouns semantically related to tools, foods and animals were presented along with matched pseudo-words. Differences in semantic features between the categories were confirmed using extensive semantic ratings whereas all semantic word categories were matched for relevant psycholinguistic variables. In a data-driven region of interest analysis, lesions in dorsal pre- and postcentral grey and white matter areas were associated to specific performance deficits for tool nouns when compared to animal nouns. In contrast, patients with lesions primarily affecting perisylvian inferior-frontal and/or temporal regions presented similar deficits across all semantic word categories tested and likewise a group of age and education matched healthy control participants did not show any category specific differences. These findings falsify brain language models denying the fronto-parietal cortex’ role in word recognition and semantic understanding. They are best accounted for by frameworks that acknowledge a role of sensorimotor cortex in the semantic processing of action-related words.

Knowledge of words: An interpretable approach for personality recognition from social media

Personality is one of the fundamental and stable individual characteristics that can be detected from human behavioral data. With the rise of social media, increasing attention has been paid to the ability to recognize personality traits by analyzing the contents of user-generated text. Existing studies have used general psychological lexicons or machine learning, and even deep learning models, to predict personality, but their performance has been relatively poor or they have lacked the ability to interpret personality. In this paper, we present a novel interpretable personality recognition model based on a personality lexicon. First, we use word embedding techniques and prior-knowledge lexicons to automatically construct a Chinese semantic lexicon suitable for personality analysis. Based on this personality lexicon, we analyze the correlations between personality traits and semantic categories of words, and extract the semantic features of users’ microblogs to construct personality recognition models using classification algorithms. Extensive experiments are conducted to demonstrate that the proposed model can achieve significantly better performances compared to previous approaches.

Information extraction from historical handwritten document images with a context-aware neural model

Many historical manuscripts that hold trustworthy memories of the past societies contain information organized in a structured layout (e.g. census, birth or marriage records). The precious information stored in these documents cannot be effectively used nor accessed without costly annotation efforts. The transcription driven by the semantic categories of words is crucial for the subsequent access. In this paper we describe an approach to extract information from structured historical handwritten text images and build a knowledge representation for the extraction of meaning out of historical data. The method extracts information, such as named entities, without the need of an intermediate transcription step, thanks to the incorporation of context information through language models. Our system has two variants, the first one is based on bigrams, whereas the second one is based on recurrent neural networks. Concretely, our second architecture integrates a Convolutional Neural Network to model visual information from word images together with a Bidirecitonal Long Short Term Memory network to model the relation among the words. This integrated sequential approach is able to extract more information than just the semantic category (e.g. a semantic category can be associated to a person in a record). Our system is generic, it deals with out-of-vocabulary words by design, and it can be applied to structured handwritten texts from different domains. The method has been validated with the ICDAR IEHHR competition protocol, outperforming the existing approaches.

РАЗНОВИДНОСТИ ПОЯСНИТЕЛЬНЫХ ОТНОШЕНИЙ В КОНСТРУКЦИИ С АНАЛОГОМ СОЮЗА ТОЧНЕЕ

The article describes the structure of the analogues of a conjunction related to the semantic category of words that form explanatory relationship, the central component of which is the word “ точнее ” / more specifically. The paper defines the semantic basis of the conjunction function appearance in a significant word. The semantic-syntactic functions of the named analogues of the conjunction are analyzed. Particular attention is paid to the lexical content of the construction on the basis of the conjunction analogue “ точнее ” / more specifically and its lexical-syntactic variants. The author comes to the conclusion that, depending on the nature of the vocabulary, various types of explanatory relations are formed in this construction.

Abstract semantics in the motor system? – An event-related fMRI study on passive reading of semantic word categories carrying abstract emotional and mental meaning

Previous research showed that modality-preferential sensorimotor areas are relevant for processing concrete words used to speak about actions. However, whether modality-preferential areas also play a role for abstract words is still under debate. Whereas recent functional magnetic resonance imaging (fMRI) studies suggest an involvement of motor cortex in processing the meaning of abstract emotion words as, for example, ‘love’, other non-emotional abstract words, in particular ‘mental words’, such as ‘thought’ or ‘logic’, are believed to engage ‘amodal’ semantic systems only. In the present event-related fMRI experiment, subjects passively read abstract emotional and mental nouns along with concrete action related words. Contrary to expectation, the results indicate a specific involvement of face motor areas in the processing of mental nouns, resembling that seen for face related action words. This result was confirmed when subject-specific regions of interest (ROIs) defined by motor localizers were used. We conclude that a role of motor systems in semantic processing is not restricted to concrete words but extends to at least some abstract mental symbols previously thought to be entirely ‘disembodied’ and divorced from semantically related sensorimotor processing. Implications for neurocognitive theories of semantics and clinical applications will be highlighted, paying specific attention to the role of brain activations as indexes of cognitive processes and their relationships to ‘causal’ studies addressing lesion and transcranial magnetic stimulation (TMS) effects. Possible implications for clinical practice, in particular speech language therapy, are discussed in closing.

Semantic word category processing in semantic dementia and posterior cortical atrophy

There is general agreement that perisylvian language cortex plays a major role in lexical and semantic processing; but the contribution of additional, more widespread, brain areas in the processing of different semantic word categories remains controversial. We investigated word processing in two groups of patients whose neurodegenerative diseases preferentially affect specific parts of the brain, to determine whether their performance would vary as a function of semantic categories proposed to recruit those brain regions. Cohorts with (i) Semantic Dementia (SD), who have anterior temporal-lobe atrophy, and (ii) Posterior Cortical Atrophy (PCA), who have predominantly parieto-occipital atrophy, performed a lexical decision test on words from five different lexico-semantic categories: colour (e.g., yellow), form (oval), number (seven), spatial prepositions (under) and function words (also). Sets of pseudo-word foils matched the target words in length and bi-/tri-gram frequency. Word-frequency was matched between the two visual word categories (colour and form) and across the three other categories (number, prepositions, and function words). Age-matched healthy individuals served as controls. Although broad word processing deficits were apparent in both patient groups, the deficit was strongest for colour words in SD and for spatial prepositions in PCA. The patterns of performance on the lexical decision task demonstrate (a) general lexicosemantic processing deficits in both groups, though more prominent in SD than in PCA, and (b) differential involvement of anterior-temporal and posterior-parietal cortex in the processing of specific semantic categories of words.

How Indian is Indian English?: Indian Words in Registers of Indian English

The rising status of English as a world language has led to the emergence of several non-native or new varieties of English, with Indian English being a major new variety. Much work on Indian English has focused on establishing the Indianness of the language. As Kachru points out, studies of Indian English consider “linguistic interference and the Indian cultural context as essential for the understanding and description of the Indianness in this variety of English” (The Indianness of Indian English 391). Other early research on the Indianness of Indian English includes Verma, Dubey, Hosali, and Bhatt, to name just a few. The work represented by most previous studies on Indian English, which focus largely on the nature and degree of difference of Indian English from external norms, does not, however, provide a clear picture of the system underlying Indian English. The current study focuses on the Indianness of Indian English, but tests Kachru’s claims regarding the progressive Indianisation of Indian English. The first part of the study is an empirical investigation of Indian words in three spoken and three written registers of the language from a corpus compiled in the year 2000 and earlier, and identifies distinct semantic categories of words in the registers. Based on the results of this first analysis, the second part of the study analyses the occurrence of Indian words in a smaller corpus of a register of written Indian English compiled in 2016. The first part of the study shows that there are marked differences in the degree of Indianisation of Indian English among different registers, and the second part of the study shows that with respect to the register studied, written Indian English seems to be undergoing a process of un-Indianisation.

Predicting the Semantic Category of Internally Generated Words from Neuromagnetic Recordings

In this study, we explore the possibility to predict the semantic category of words from brain signals in a free word generation task. Participants produced single words from different semantic categories in a modified semantic fluency task. A Bayesian logistic regression classifier was trained to predict the semantic category of words from single-trial MEG data. Significant classification accuracies were achieved using sensor-level MEG time series at the time interval of conceptual preparation. Semantic category prediction was also possible using source-reconstructed time series, based on minimum norm estimates of cortical activity. Brain regions that contributed most to classification on the source level were identified. These were the left inferior frontal gyrus, left middle frontal gyrus, and left posterior middle temporal gyrus. Additionally, the temporal dynamics of brain activity underlying the semantic preparation during word generation was explored. These results provide important insights about central aspects of language production.

Brain routes for reading in adults with and without autism: EMEG evidence.

Reading utilises at least two neural pathways. The temporal lexical route visually maps whole words to their lexical entries, whilst the nonlexical route decodes words phonologically via parietal cortex. Readers typically employ the lexical route for familiar words, but poor comprehension plus precocity at mechanically ‘sounding out’ words suggests that differences might exist in autism. Combined MEG/EEG recordings of adults with autistic spectrum conditions (ASC) and controls while reading revealed preferential recruitment of temporal areas in controls and additional parietal recruitment in ASC. Furthermore, a lack of differences between semantic word categories was consistent with previous suggestion that people with ASC may lack a ‘default’ lexical-semantic processing mode. These results are discussed with reference to dual-route models of reading.Electronic supplementary materialThe online version of this article (doi:10.1007/s10803-013-1858-z) contains supplementary material, which is available to authorized users.

N400-like Potentials and Reaction Times Index Semantic Relations between Highly Repeated Individual Words

The N400 ERP is an electrophysiological index of semantic processing. Its amplitude varies with the semantic category of words, their concreteness, or whether their meaning matches that of a preceding context. The results of a number of studies suggest that these effects could be markedly reduced or suppressed for stimuli that are repeated. Nevertheless, we have recently shown that significant effects of semantic matching and category could be obtained on N400-like potentials elicited by massively repeated target words in a prime-target semantic categorization task. If such effects could be obtained when primes also are repeated, it would then be possible to study the semantic associations between individual words. The present study thus aimed to test this hypothesis while (1) controlling for a potential contribution of physical matching to the processing of repeated targets and (2) testing if the N400-like effects obtained in these conditions are modulated by task instruction, as are classic N400 effects. Two category words were used as primes and two exemplars as targets. In one block of trials, subjects had to respond according to the semantic relation between prime and target (semantic instruction) and, in another block, they had to report changes in letter case (physical instruction). Results showed that the amplitude of the N400-like ERP obtained was modulated by semantic matching and category but not by letter case. The effect of semantic matching was observed only in the semantic instruction block. Interestingly, the effect of category was not modulated by task instruction. An independent component analysis showed that the component that made the greatest contribution to the effect of semantic matching in the time window of the N400-like potential had a scalp distribution similar to that reported for the N400 and was best fit as a bilateral generator in the superior temporal gyrus. The use of repetition could thus allow, at least in explicit semantic tasks, a drastic simplification of N400 protocols. Highly repeated individual words could be used to study semantic relations between individual concepts.

Lexical and Grammatical Skills in Toddlers on the Autism Spectrum Compared to Late Talking Toddlers

This study compared language development in 30-month-old toddlers on the autism spectrum and 25- month-old late talking toddlers without autism. Groups were matched on overall productive vocabulary (and nonverbal cognition was controlled) in order to compare language acquisition patterns related to vocabulary composition and early lexical–grammatical relationships. Findings revealed that semantic categories of words— including psychological state terms—used by toddlers on the autism spectrum were very similar to those of late talkers. Both groups were equivalent with respect to grammatical complexity and proportion of toddlers combining words, though late talkers displayed a relatively stronger association between lexical–grammatical abilities. These tentative findings are consistent with a dimensional account of early, core linguistic abilities across different populations of children with language delay.

The Word Processing Deficit in Semantic Dementia: All Categories Are Equal, but Some Categories Are More Equal than Others

It has been claimed that semantic dementia (SD), the temporal variant of fronto-temporal dementia, is characterized by an across-the-board deficit affecting all types of conceptual knowledge. We here confirm this generalized deficit but also report differential degrees of impairment in processing specific semantic word categories in a case series of SD patients (N = 11). Within the domain of words with strong visually grounded meaning, the patients' lexical decision accuracy was more impaired for color-related than for form-related words. Likewise, within the domain of action verbs, the patients' performance was worse for words referring to face movements and speech acts than for words semantically linked to actions performed with the hand and arm. Psycholinguistic properties were matched between the stimulus groups entering these contrasts; an explanation for the differential degrees of impairment must therefore involve semantic features of the words in the different conditions. Furthermore, this specific pattern of deficits cannot be captured by classic category distinctions such as nouns versus verbs or living versus nonliving things. Evidence from previous neuroimaging research indicates that color- and face/speech-related words, respectively, draw most heavily on anterior-temporal and inferior-frontal areas, the structures most affected in SD. Our account combines (a) the notion of an anterior-temporal amodal semantic "hub" to explain the profound across-the-board deficit in SD word processing, with (b) a semantic topography model of category-specific circuits whose cortical distributions reflect semantic features of the words and concepts represented.

Parametric analysis of EEG and fMRI data reveals the neural basis of visual word processing

Parametric analysis of EEG and fMRI data reveals the neural basis of visual word processing

Natural Language-Enabled Data Modeling

Although data modeling has been an area of intensive research, there is a lack of operational procedures for measuring model quality and for integrating large-scale models. Progress has been limited because the meaning associated with the individual elements of a model needs to be taken into account. This meaning rarely is made explicit and therefore not directly available for validation and integration procedures. In this paper we show that natural language processing techniques based on a custom-built dictionary can be used to interpret data models and leverage meaning for validation and integration procedures. We describe a general-purpose dictionary which contains syntactic and semantic word categories for 23.000 English words. We show how the syntactic and semantic information in the dictionary can be used to detect semantic inconsistencies, reject inconsistent naming, identify unregistered abbreviations and detect synonym candidates. To prove feasibility of our approach, we describe a prototype tool which has been implemented on a standard personal computer.

The use of specific semantic word categories in the diagnosis of dysnomic learning-disabled children.

ABSTRACTThe word finding skills of dysnomic children (learning‐disabled children with word finding problems) were compared with those of learning disabled and normal children without word finding problems when naming letters, numbers, and colours. Comparison of word finding skills between groups and conditions were made with respect to completion time, errors, and secondary characteristics. Learning‐disabled children with word finding problems manifested significantly more errors, longer completion times, and more secondary characteristics on letter and colour naming while performing similarly to children without word finding problems on number naming tasks. Learning disabled children without word finding problems did not differ significantly from normal children in their naming of colours, letters, or numbers. Implications for the use of specific semantic categories in the assessment of word finding disorders are discussed.

Interlingual word associations

A word-association experiment was performed with three groups of bilingual S s. Four test sequences were studied, the stimulus word in English responded to in English or the native language, and a translation of the word into the native language responded to in English or the native language. There were five semantic categories of words. The results were that only about one-third of the responses in one language translated those in the other, and the proportion did not differ very much whether the word associations were intralingual or interlingual. Of this proportion, about two-thirds (20% of the total responses) were lexically similar or translations in the interlingual tests. The number of similar associations changed sharply with the semantic category of the word, words referring to concrete, manipulable objects more often having similar associations than words referring to abstract states or emotions. The data are interpreted to mean that experiences and memories of various kinds are not stored in common in some supralinguistic form but are tagged and stored separately in the language S used to define the experience to himself.