Different Types Of Semantic Relations Research Articles

The role of culture and semantic organization in working memory updating

ABSTRACT Westerners tend to relate items in a categorical manner, whereas Easterners focus more on functional relationships. The present study extended research on semantic organization in long-term memory to working memory. First, Americans’ and Turks’ preferences for categorical versus functional relationships were tested. Second, working memory interference was assessed using a 2-back working memory paradigm in which lure items were categorically and functionally related to targets. Next, a mediation model tested direct effects of culture and semantic organization on working memory task behaviour, and the indirect effect, whether semantic organization mediated the relationship between culture and working memory interference. Whereas Americans had slower response times to correctly rejecting functional lures compared to categorical lures, conditions did not differ for Turks. However, semantic organization did not mediate cultural difference in working memory interference. Across cultures, there was evidence that semantic organization affected working memory errors, with individuals who endorsed categorical more than functional pairings committing more categorical than functional errors on the 2-back task. Results align with prior research suggesting individual differences in use of different types of semantic relationships, and further that literature by indicating effects on interference in working memory. However, these individual differences may not be culture-dependent.

Lexical Portrait of an Oral Story: a Corpus Study of Speech of Two Generations of Native Speakers of Russian

The article is devoted to the quantitative and statistical analysis of the vocabulary of the oral stories corpus based on pictures. The subjects were 42 people, monolingual native speakers of the Russian (representatives of 15 families). The purpose of the study is to identify and describe the lexical parameters that distinguish the oral stories of representatives of different generations. An attempt is made to consider the connection of these parameters with the formation of native speakers’ ideas about the lexical structure of the narrative. The following results are obtained. The nature of the stimulus material ensured an approximately equal degree of complexity in the texts of both groups, although the volume of the parents’ active vocabulary significantly exceeded the volume of the children’s vocabulary. At the level of individual texts, both groups use equally diverse vocabulary per unit of text volume. In the children’s stories, the main “plot” vocabulary is quite heterogeneous, while the parents tend to lexical unification of the narrative. In the texts of both groups, local reference and direction of movement are expressed in the same way, which indicates their “basic” narrative function, which does not depend on the age of the subjects. The stories of the parents are also unified in terms of vocabulary, showing the logical structure of the narrative and different types of semantic relations in the text.

Discovery of topic evolution path and semantic relationship based on patent entity representation

PurposeThis paper aims to propose a method for better discovering topic evolution path and semantic relationship from the perspective of patent entity extraction and semantic representation. On the one hand, this paper identifies entities that have the same semantics but different expressions for accurate topic evolution path discovery. On the other hand, this paper reveals semantic relationships of topic evolution for better understanding what leads to topic evolution.Design/methodology/approachFirstly, a Bi-LSTM-CRF (bidirectional long short-term memory with conditional random field) model is designed for patent entity extraction and a representation learning method is constructed for patent entity representation. Secondly, a method based on knowledge outflow and inflow is proposed for discovering topic evolution path, by identifying and computing semantic common entities among topics. Finally, multiple semantic relationships among patent entities are pre-designed according to a specific domain, and then the semantic relationship among topics is identified through the proportion of different types of semantic relationships belonging to each topic.FindingsIn the field of UAV (unmanned aerial vehicle), this method identifies semantic common entities which have the same semantics but different expressions. In addition, this method better discovers topic evolution paths by comparison with a traditional method. Finally, this method identifies different semantic relationships among topics, which gives a detailed description for understanding and interpretation of topic evolution. These results prove that the proposed method is effective and useful. Simultaneously, this method is a preliminary study and still needs to be further investigated on other datasets using multiple emerging deep learning methods.Originality/valueThis work provides a new perspective for topic evolution analysis by considering semantic representation of patent entities. The authors design a method for discovering topic evolution paths by considering knowledge flow computed by semantic common entities, which can be easily extended to other patent mining-related tasks. This work is the first attempt to reveal semantic relationships among topics for a precise and detailed description of topic evolution.

The grammar of Russian preposition: theoretical aspects

At present, linguists do not have sufficient tools to strictly determine whether specific prepositional units belong to prepositions as a part of speech, so it seems relevant to consider the functional-grammatical field of prepositions, which includes both prepositions themselves and units that can function as prepositions. The aim of this article is to identify, classify and describe systemic paradigmatic relations within the functional-grammatical field of prepositions. The study was conducted on the material of a registry of 3,000 prepositional units, which were collected through a continuous sampling from dictionaries and specialized works, as well as by identifying all possible variants of prepositions on the Internet and the National Corpus of the Russian language. The paradigmatic relations of the units that already have lexicographic attribution were analyzed. As a result, the graphical, morphonological and word-formation paradigms of prepositional units were identified, and their morphosyntactic and semantic paradigmatic relations were detailed. The graphical paradigm is a series of variants of different types: alphabetic, written together or separately and uncodified. The morphological paradigm includes vocalized, accented, incomplete-vowel versions, truncated, softened, differentiated by accent and inflective variants. Within the word-formation paradigm the most productive roots of prepositional units are identified. For the morphosyntactic paradigm, the possible structural subtypes were identified: thus, the form of the prepositional noun and the form of the introduced actant were relevant. In addition, the regularities of paradigm formation were revealed for prepositional units expressing different types of semantic relations. For the semantic paradigm, the dependence of a particular paradigm subtype on the type of semantic relations expressed by the prepositional units has been revealed. Considering the functional and grammatical field of the preposition as a set organized by regular relations of different types, we can identify the grammar of the preposition. Understanding the systemic relations between prepositional units opens the way to predicting systematically possible linguistic units within the field in question.

Reliability and Safety of Autonomous Systems Based on Semantic Modelling for Self-Certification

A novel modelling paradigm for online diagnostics and prognostics for autonomous systems is presented. A model for the autonomous system being diagnosed is designed using a logic-based formalism. The model supports the run-time ability to verify that the autonomous system is safe and reliable for operation within a dynamic environment. The paradigm is based on the philosophy that there are different types of semantic relationships between the states of different parts of the system. A finite state automaton is devised for each sensed component and some of the non-sensed components. To capture the interdependencies of components within such a complex robotic platform, automatons were related to each other by semantic relationships. Modality was utilised by the formalism to abstract the relationships and to add measures for the possibility and uncertainty of the relationships. The complexity of the model was analysed to evaluate its scalability and applicability to other systems. The results demonstrate that the complexity is not linear and a computational time of 10 ms was required to achieve run-time diagnostics for 2200 KB of knowledge for complex system interdependences. The ability to detect and mitigate hardware related failures was demonstrated within a confined space autonomous operation. Our findings provide evidence of the applicability of our approach for the significant challenge of run-time safety compliance and reliability in autonomous systems.

Things2Vec: Semantic Modeling in the Internet of Things With Graph Representation Learning

The advent of fifth generation (5G) enables the Internet of Things (IoT) to connect a massive number of things. The interaction and communication among these things generate an enormous amount of context-aware data that is semantically diverse. Traditional data representation approaches, such as semantic annotation, ontology, and semantic Web technology are rule based, which lack flexibility and adaptability when applied to IoT. To address the challenge, this article mainly focuses on the problem of semantic representation, which is essential for processing and fusion of IoT data. To serve as a bridge, we propose a high-level framework, namely, Things2Vec, which aims to produce the latent semantic representations from the interaction of things through the graph embedding technique. These semantic representations benefit various IoT semantic analysis tasks, such as the IoT service recommendation and automation of things. In Things2Vec, we utilize the graph to model the function sequence relationships that are generated by the interaction of things, which is called the IoT context graph. Since these function sequence relationships are heterogeneous in terms of semantics, it causes general graph embeddings to fail to learn complete information. Thus, we propose a biased random walk procedure, which is tailored to capture the neighborhoods of nodes with different types of semantic relationships. Extensive experiments are carried out, and our results show that the proposed method can effectively capture the semantic relationships among context-aware data in IoT.

Semantic association computation: a comprehensive survey

Semantic association computation is the process of quantifying the strength of a semantic connection between two textual units, based on different types of semantic relations. Semantic association computation is a key component of various applications belonging to a multitude of fields, such as computational linguistics, cognitive psychology, information retrieval and artificial intelligence. The field of semantic association computation has been studied for decades. The aim of this paper is to present a comprehensive survey of various approaches for computing semantic associations, categorized according to their underlying sources of background knowledge. Existing surveys on semantic computation have focused on a specific aspect of semantic associations, such as utilizing distributional semantics in association computation or types of spatial models of semantic associations. However, this paper has put a multitude of computational aspects and factors in one picture. This makes the article worth reading for those researchers who want to start off in the field of semantic associations computation. This paper introduces the fundamental elements of the association computation process, evaluation methodologies and pervasiveness of semantic measures in a variety of fields, relying on natural language semantics. Along the way, there is a detailed discussion on the main categories of background knowledge sources, classified as formal and informal knowledge sources, and the underlying design models, such as spatial, combinatorial and network models, that are used in the association computation process. The paper classifies existing approaches of semantic association computation into two broad categories, based on their utilization of background knowledge sources: knowledge-rich approaches; and knowledge-lean approaches. Each category is divided further into sub-categories, according to the type of underlying knowledge sources and design models of semantic association. A comparative analysis of strengths and limitations of various approaches belonging to each research stream is also presented. The paper concludes the survey by analyzing the pivotal factors that affect the performance of semantic association measures.

FUNCTIONAL SPACE OF THE PEDAGOGICAL TERM (SEMANTIC ASPECT)

The article is an attempt to make a general analysis of the semantic and functional features of the term of the pedagogical industry, which is considered as the basic unit of professional speech and is characterized by semantic, notional and derivative specificity.The purpose of the article is to define the semantic and functional space of terms of pedagogy used in one of the dynamic spheres.The object of the study is the pedagogical term.The subject of the study is the semantic and functional features of the term of the pedagogical industry, in particular, semantic relations and their term-formative potential.The methods used in the study are theoretical analysis, synthesis, component and definitive method.It is stated that the pedagogical term is the result of the process of cognition of concepts, objects and phenomena of objective reality associated with the scientific and educational process (cognitive function), is included in the system of industry terminology, accurately and concisely fixing a certain professional content (nominative and significative functions). The named terminological functions are qualified as the main ones.Pedagogical terms reflect concepts which are interrelated and correspond to the scientific concept of the industry. The meaning of the term consists of components (semas), which are determined by comparison. The definition of the semantics of the term involves the definition of differential features. They distinguish the term from other lexical units or other meanings.It is proved that the condition for the functioning of the terminology of pedagogy is different types of semantic relations. They act as factors of systemicity and play a term-formative role (stylistic figures are the means of formation a polysemant), and the semantic way of formation of pedagogical terms is a priority.The prospects for studying the semantics of the terms of the pedagogical industry can be seen in the study of their conceptual potential.

User preference modeling based on meta paths and diversity regularization in heterogeneous information networks

Recommendation methods based on heterogeneous information networks (HINs) have been attracting increased attention recently. Meta paths in HINs represent different types of semantic relationships. Meta path-based recommendation methods aim to use meta paths in HINs to evaluate the similarity or relevancy between nodes to make recommendations. In previous work, the meta paths have usually been selected manually (based on experience), and the path weight optimization methods usually suffer from overfitting. To solve these problems, we propose to automatically select and combine the meta paths through weight optimization. Diversity is introduced into the objective function as a regularization term to avoid overfitting. Inspired by the ambiguity decomposition theory in ensemble learning, we present a new diversity measure and use it to encourage diversity among meta paths to improve recommendation performance. Experimental results on item recommendation and tag recommendation tasks confirm the effectiveness of the proposed method compared with traditional collaborative filtering and state-of-the-art HIN-based recommendation methods.

МЕНТАЛЬНОЕ КАРТИРОВАНИЕ НОМИНАЦИЙ МИФИЧЕСКИХ СУЩЕСТВ В АНГЛИЙСКОМ ЯЗЫКЕ

The article focuses on creating and analyzing the mental maps of certain lexemes nominating mythological creatures in the English language. Relying on the data of the Visual Thesaurus software and the lexicographical analysis of eighteen English dictionaries the author identifies some problems concerning the categorization of the mentioned notions. The paper proposes a scheme of semantic valency and invariation of lexemes in their mythologized meanings. The suggested model establishes different types of semantic relations between some mytho-lexemes and identifies their semantic invariant.

When Wine and Apple Both Help the Production of Grapes: ERP Evidence for Post-lexical Semantic Facilitation in Picture Naming.

Background: Producing a word in referential naming requires to select the right word in our mental lexicon among co-activated semantically related words. The mechanisms underlying semantic context effects during speech planning are still controversial, particularly for semantic facilitation which investigation remains under-represented in contrast to the plethora of studies dealing with interference. Our aim is to study the time-course of semantic facilitation in picture naming, using a picture-word “interference” paradigm and event-related potentials (ERPs).Methods: We compared two different types of semantic relationships, associative and categorical, in a single word priming and a double word priming paradigm. The primes were presented visually with a long negative Stimulus Onset Asynchrony (SOA), which is expected to cause facilitation.Results: Shorter naming latencies were observed after both associative and categorical primes, as compared to unrelated primes, and even shorter latencies after two primes. Electrophysiological results showed relatively late modulations of waveform amplitudes for both types of primes (beginning ~330 ms post picture onset with a single prime and ~275 ms post picture onset with two primes), corresponding to a shift in latency of similar topographic maps across conditions.Conclusion: The present results are in favor of a post-lexical locus of semantic facilitation for associative and categorical priming in picture naming and confirm that semantic facilitation is as relevant as semantic interference to inform on word production. The post-lexical locus argued here might be related to self-monitoting or/and to modulations at the level of word-form planning, without excluding the participation of strategic processes.

Doctor, Teacher, and Stethoscope: Neural Representation of Different Types of Semantic Relations.

Concepts can be related in many ways. They can belong to the same taxonomic category (e.g., "doctor" and "teacher," both in the category of people) or be associated with the same event context (e.g., "doctor" and "stethoscope," both associated with medical scenarios). How are these two major types of semantic relations coded in the brain? We constructed stimuli from three taxonomic categories (people, manmade objects, and locations) and three thematic categories (school, medicine, and sports) and investigated the neural representations of these two dimensions using representational similarity analyses in human participants (10 men and nine women). In specific regions of interest, the left anterior temporal lobe (ATL) and the left temporoparietal junction (TPJ), we found that, whereas both areas had significant effects of taxonomic information, the taxonomic relations had stronger effects in the ATL than in the TPJ ("doctor" and "teacher" closer in ATL neural activity), with the reverse being true for thematic relations ("doctor" and "stethoscope" closer in TPJ neural activity). A whole-brain searchlight analysis revealed that widely distributed regions, mainly in the left hemisphere, represented the taxonomic dimension. Interestingly, the significant effects of the thematic relations were only observed after the taxonomic differences were controlled for in the left TPJ, the right superior lateral occipital cortex, and other frontal, temporal, and parietal regions. In summary, taxonomic grouping is a primary organizational dimension across distributed brain regions, with thematic grouping further embedded within such taxonomic structures.SIGNIFICANCE STATEMENT How are concepts organized in the brain? It is well established that concepts belonging to the same taxonomic categories (e.g., "doctor" and "teacher") share neural representations in specific brain regions. How concepts are associated in other manners (e.g., "doctor" and "stethoscope," which are thematically related) remains poorly understood. We used representational similarity analyses to unravel the neural representations of these different types of semantic relations by testing the same set of words that could be differently grouped by taxonomic categories or by thematic categories. We found that widely distributed brain areas primarily represented taxonomic categories, with the thematic categories further embedded within the taxonomic structure.

Teaching adults with intellectual disability to combine symbols in a reading context

The aim of this study is teaching adults con intellectual disability to combine symbols to convey semantic relations during shared reading. A single subject, multiple probe design across three different types of semantic relations generated from a matrix and replicated across three adults with moderate to severe intellectual disability was carried out. A prompts hierarchy in order to produce symbol combinations on an augmentative and alternative device was applied. Clear changes in trend and level were observed in the percentage of trained correct graphic-symbol combinations and the generalization of semantic relations to untrained combinations, and post-intervention maintenance of skills.

Semantic concept model using Wikipedia semantic features

Wikipedia has become a high coverage knowledge source which has been used in many research areas such as natural language processing, text mining and information retrieval. Several methods have been introduced for extracting explicit or implicit relations from Wikipedia to represent semantics of concepts/words. However, the main challenge in semantic representation is how to incorporate different types of semantic relations to capture more semantic evidences of the associations of concepts. In this article, we propose a semantic concept model that incorporates different types of semantic features extracting from Wikipedia. For each concept that corresponds to an article, four semantic features are introduced: template links, categories, salient concepts and topics. The proposed model is based on the probability distributions that are defined for these semantic features of a Wikipedia concept. The template links and categories are the document-level features which are directly extracted from the structured information included in the article. On the other hand, the salient concepts and topics are corpus-level features which are extracted to capture implicit relations among concepts. For the salient concepts feature, the distributional-based method is utilised on the hypertext corpus to extract this feature for each Wikipedia concept. Then, the probability product kernel is used to improve the weight of each concept in this feature. For the topic feature, the Labelled latent Dirichlet allocation is adapted on the supervised multi-label of Wikipedia to train the probabilistic model of this feature. Finally, we used the linear interpolation for incorporating these semantic features into the probabilistic model to estimate the semantic relation probability of the specific concept over Wikipedia articles. The proposed model is evaluated on 12 benchmark datasets in three natural language processing tasks: measuring the semantic relatedness of concepts/words in general and in the biomedical domain, semantic textual relatedness measurement and measuring the semantic compositionality of noun compounds. The model is also compared with five methods that depends on separate semantic features in Wikipedia. Experimental results show that the proposed model achieves promising results in three tasks and outperforms the baseline methods in most of the evaluation datasets. This implies that incorporation of explicit and implicit semantic features is useful for representing semantics of concepts in Wikipedia.

Estrategia de matrices y jerarquía creciente de incitaciones para expresar relaciones semánticas combinando símbolos en la discapacidad intelectual

IntroductionThere are few studies on the expression of semantic relationships using graphic symbol combinations by the application of the matrix strategy and a hierarchy of increasing prompts to intellectually disabled adults. The aim of this study is to investigate the effect of the matrix strategy and an increasing prompt hierarchy in the context of shared story reading on the production of graphic symbol combinations in three adults with moderate to severe intellectual disability. MethodsA single subject, multiple probe design across different types of semantic relationships generated from a matrix combination was used. A hierarchy of increasing prompts for the production of symbol combinations was applied to three individuals. ResultsIt was shown that the application of the training strategies was successful in teaching the participants to produce targeted semantic relationships combining symbols, in order to extrapolate to untrained models, and for the maintenance of post-intervention skills. ConclusionsEven significant intellectually disabled adults may learn to combine symbols for the expression of semantic relationships when appropriate interventions techniques are applied.

СМЫСЛОВАЯ СВЯЗЬ КАК ОСНОВА ПОСТРОЕНИЯ РЕЧИ

Filatova Elena Vladimirovna – Candidate of Philological Sciences, Docent, Acting Head of the Department of English for Natural Sciences and Humanities, Donetsk National University. The article develops the idea of L. V. Scherba that in the organization of connected speech, along with grammatical relationship it can be used as an independent means of organizing situational-semantic compatibility of the source speech units in the form of “addition”. Highlighting the question of the relation of initial components in the generation and perception of speech involves figuring out what units are reciprocal. In this regard, it is clearly and consistently demarcated the spheres of language and speech as a nationwide arsenal of linguistic means, and individually creative sphere of their usage in speech activities. Attention is paid to minimal speech structures – syntagma, as well as different types of semantic relationships in the structure of syntagma and between their linear increase in the process of speech production: semantic, the addition of meaning, mediated by semantic and associative relationships. On the basis of the postulate about the purpose of the speech to be a means of communication and understanding it is affirmed the idea that semantic relations in reciprocal units and between them can be considered as the main unifying tool of coherent speech. Key words : syntagma, innersyntagmatic connection, intersyntagmatic connection, semantic relationship, mediated semantic relationship, associative semantic relationship .

Cumulative semantic interference for associative relations in language production

Associations between conceptual representations and thematic relations play an important role in the organization of semantic memory. However, language production research on semantic context effects shows that associative (e.g., dog and bone) and categorical relations (dog and horse) seem to diverge. While categorical contexts typically induce semantic interference that has traditionally been taken to reflect competitive lexical selection, evidence for comparable associative modulations is rare. In three experiments we tested whether thematic associations between objects induce cumulative interference in the continuous naming paradigm, assuming that this paradigm hampers lexical selection via the activation of highly active lexical cohorts steadily increasing in size. Indeed, naming times increased linearly with each newly named member of thematic contexts irrespective of the pre-activation of associations before the naming task (Experiment 1), and irrespective of whether categorical links were partially included (Experiments 1 and 2) or entirely absent (Experiment 3). These findings demonstrate that different types of semantic relations induce interference.

A Complex Network Approach to Distributional Semantic Models.

A number of studies on network analysis have focused on language networks based on free word association, which reflects human lexical knowledge, and have demonstrated the small-world and scale-free properties in the word association network. Nevertheless, there have been very few attempts at applying network analysis to distributional semantic models, despite the fact that these models have been studied extensively as computational or cognitive models of human lexical knowledge. In this paper, we analyze three network properties, namely, small-world, scale-free, and hierarchical properties, of semantic networks created by distributional semantic models. We demonstrate that the created networks generally exhibit the same properties as word association networks. In particular, we show that the distribution of the number of connections in these networks follows the truncated power law, which is also observed in an association network. This indicates that distributional semantic models can provide a plausible model of lexical knowledge. Additionally, the observed differences in the network properties of various implementations of distributional semantic models are consistently explained or predicted by considering the intrinsic semantic features of a word-context matrix and the functions of matrix weighting and smoothing. Furthermore, to simulate a semantic network with the observed network properties, we propose a new growing network model based on the model of Steyvers and Tenenbaum. The idea underlying the proposed model is that both preferential and random attachments are required to reflect different types of semantic relations in network growth process. We demonstrate that this model provides a better explanation of network behaviors generated by distributional semantic models.

Teaching Graphic Symbol Combinations to Children with Limited Speech During Shared Story Reading

The aim of this study was to determine the effect of an intervention strategy on the production of graphic symbol combinations in children with limited speech. Four children between the ages of 6;5 and 10;8 (years;months) with limited speech participated in the study. A single-subject, multiple probe design across three different types of semantic relations was used. Generalization to untrained exemplars was also monitored. Results were mixed across the four participants: two participants learned to combine symbols across different types of relations, maintained these skills post intervention, and generalized their skills to untrained combinations; and two participants showed less consistent evidence of learning. The effects, as measured during structured probes, were strong for one participant, moderate for another, and inconclusive for the two others. Responses during shared story reading suggested that the measurement probes might have underestimated participants’ ability to combine symbols.

Semantic relations differentially impact associative recognition memory: Electrophysiological evidence

Though associative recognition memory is thought to rely primarily on recollection, recent research indicates that familiarity might also make a substantial contribution when to-be-learned items are integrated into a coherent structure by means of an existing semantic relation. It remains unclear how different types of semantic relations, such as categorical (e.g., dancer–singer) and thematic (e.g., dancer–stage) relations might affect associative recognition, however. Using event-related potentials (ERPs), we addressed this question by manipulating the type of semantic link between paired words in an associative recognition memory experiment. An early midfrontal old/new effect, typically linked to familiarity, was observed across the relation types. In contrast, a robust left parietal old/new effect was found in the categorical condition only, suggesting a clear contribution of recollection to associative recognition for this kind of pairs. One interpretation of this pattern is that familiarity was sufficiently diagnostic for associative recognition of thematic relations, which could result from the integrative nature of the thematic relatedness compared to the similarity-based nature of categorical pairs. The present study suggests that the extent to which recollection and familiarity are involved in associative recognition is at least in part determined by the properties of semantic relations between the paired associates.