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This paper proposes an explanatory model for the lexical representation of the native speakers' lexical knowledge of English prepositions. Lexical knowledge of prepositions as relational predicates includes argument structure (trajector-landmark) as in Cognitive Grammar, situation types (position vs state) as in Functional Grammar, lexical hierarchies (spatial subdomains) based on semantic primitives, as in Natural Semantic Metalanguage, and embodied perceptual parameters configured in four dimensions, namely, geometry, topology, force-dynamics and function (from Cognitive Linguistics). This model is illustrated here by expounding three lexical templates compatible with constructional templates in the Lexical Constructional Model, representing the semantic decomposition of English prepositions at, on and in. [ABSTRACT FROM AUTHOR]

Animacy is an intrinsic semantic property of words referring to living things. A long line of evidence shows that words with animate referents require lower processing costs during word recognition than words with inanimate referents, leading among others to a decreased N400 amplitude in reaction to animate relative to inanimate objects. In the current study, we use this animacy effect to provide evidence for access to the semantic properties of constituents in German noun-noun compounds. While morphological decomposition of noun-noun compounds is well-researched and illustrated by the robust influence of lexical constituent properties like constituent length and frequency, findings for semantic decomposition are less clear in the current literature. Bymanipulating the animacy of compound modifiers and heads, we are able to manipulate the relative ease of lexical access strictly due to intrinsic semantic properties of the constituents. Our results show additive effects of constituent animacy, with a higher number of animate constituents leading to gradually attenuated N400 amplitudes. We discuss the implications of our findings for current models of complex word recognition, as well as stimulus construction practices in psycho-and neurolinguistic research. [ABSTRACT FROM AUTHOR]

Animacy is an intrinsic semantic property of words referring to living things. A long line of evidence shows that words with animate referents require lower processing costs during word recognition than words with inanimate referents, leading among others to a decreased N400 amplitude in reaction to animate relative to inanimate objects. In the current study, we use this animacy effect to provide evidence for access to the semantic properties of constituents in German noun-noun compounds. While morphological decomposition of noun-noun compounds is well-researched and illustrated by the robust influence of lexical constituent properties like constituent length and frequency, findings for semantic decomposition are less clear in the current literature. By manipulating the animacy of compound modifiers and heads, we are able to manipulate the relative ease of lexical access strictly due to intrinsic semantic properties of the constituents. Our results show additive effects of constituent animacy, with a higher number of animate constituents leading to gradually attenuated N400 amplitudes. We discuss the implications of our findings for current models of complex word recognition, as well as stimulus construction practices in psycho-and neurolinguistic research.

Social appropriation of knowledge has become an increasingly common term in different academic and political communities, but the literature still shows a lack of clarity and consensus regarding its meaning. To contribute to the understanding of this phenomenon, this article seeks to identify the semantic primes associated with it and determine its essential properties in order to propose a definition of this concept. To establish said definition, this study implemented semantic decomposition and followed the recommendations offered in the scientific literature and Colombian public policy on social appropriation. The sample studied here was composed of 19 definitions taken from articles indexed in the Web of Science and Scopus databases, as well as other 14 definitions taken from documents on Colombian public policy. As a result, six semantic primes were found to be associated with social appropriation of knowledge: enablers, target entity, object, scope, ends, and implications. In general, the results suggest that there is a fusion between this concept and its impacts; unclear or vague terminology; circularity and lack of conciseness; a conception limited to how, what it is, and what it is for; and five trends regarding the meaning of social appropriation of knowledge. [ABSTRACT FROM AUTHOR]

Deep hashing has garnered considerable interest and has shown impressive performance in the domain of retrieval. However, the majority of the current hashing techniques rely solely on binary similarity evaluation criteria to assess the semantic relationships between multi-label instances, which presents a challenge in overcoming the feature gap across various modalities. In this paper, we propose semantic decomposition and enhancement hashing (SDEH) by extensively exploring the multi-label semantic information shared by different modalities for cross-modal retrieval. Specifically, we first introduce two independent attention-based feature learning subnetworks to capture the modality-specific features with both global and local details. Subsequently, we exploit the semantic features from multi-label vectors by decomposing the shared semantic information among multi-modal features such that the associations of different modalities can be established. Finally, we jointly learn the common hash code representations of multimodal information under the guidelines of quadruple losses, making the hash codes informative while simultaneously preserving multilevel semantic relationships and feature distribution consistency. Comprehensive experiments on four commonly used multimodal datasets offer strong support for the exceptional effectiveness of our proposed SDEH. [ABSTRACT FROM AUTHOR]

Many face attribute manipulation methods can only provide global attribute manipulation according to the attribute labels. In this paper, we propose a self-attention-masking semantic decomposition method which is able to learn an attribute attention mask for each attribute. User can adjust the strength and color of each attribute smoothly and more freely. We decouple the attention of different attributes and overcome the disadvantage of overlap between different attribute attention masks by an attention weighting module. Thanks to the attribute attention masks, our method allows manipulate facial attribute without generator after only once generation. Moreover, we can perform facial semantic segmentation without pixel level semantic labels. Experiments show that our method simultaneously improves the freedom of attribute manipulation and the authenticity of synthetic face. The mean intersection over union of semantic segmentation is over 65% for hair and skin. Our code is available at github.com/flyfeatherok/SAMSD.

The paper aims to demonstrate that the main contribution of Anna Wierzbicka to linguistics is the idea of semantic decomposition - that is, representing meaning in terms of structurally organized configurations of simpler meanings - and a huge amount of specific decompositions of lexical meanings from many languages. One of possible developments of this idea of Wierzbicka’s is the Meaning-Text linguistic approach, and in particular - the Meaning-Text model of natural language. To illustrate the importance and fruitfulness of semantic decomposition, two Meaning-Text mini-models are presented for English and Russian. Two semantically equivalent sentences of these languages are considered: (1) a. Eng. A honeymooner was fatally attacked by a shark. ~ b. Rus. Molodožën pogib v rezul´tate napadenija akuly vo vremja medovogo mesjaca lit. ‘Young.husband died as result of.attack of.shark during honey month’ The formal representations of these sentences at four levels-Meaning-Text style-are shown: semantic, deep-syntactic, surface-syntactic, and deep-morphological. Examples of formal rules relating the representations of two adjacent levels are presented.

Three-dimensional (3D) building models are closely related to human activities in urban environments. Due to the variations in building styles and complexity in roof structures, automatically reconstructing 3D buildings with semantics and topology information still faces big challenges. In this paper, we present an automated modeling approach that can semantically decompose and reconstruct the complex building light detection and ranging (LiDAR) point clouds into simple parametric structures, and each generated structure is an unambiguous roof semantic unit without overlapping planar primitive. The proposed method starts by extracting roof planes using a multi-label energy minimization solution, followed by constructing a roof connection graph associated with proximity, similarity, and consistency attributes. Furthermore, a progressive decomposition and reconstruction algorithm is introduced to generate explicit semantic subparts and hierarchical representation of an isolated building. The proposed approach is performed on two various datasets and compared with the state-of-the-art reconstruction techniques. The experimental modeling results, including the assessment using the International Society for Photogrammetry and Remote Sensing (ISPRS) benchmark LiDAR datasets, demonstrate that the proposed modeling method can efficiently decompose complex building models into interpretable semantic structures.

Devoted to problems of translations, the work presents a qualitative analysis of semantic peculiarities of sociocultural stereotypes that influence the solutions of a translator in the process of interpreting. We hypothesised that a specific social category that exists in a source culture in the form of pragmatic predispositions, namely a sociocultural stereotype, which has no equivalent notion as well as a word in translation culture and language, is a lacunar stereotype. The words denoting sociocultural stereotypes evoke numerous images, built in accordance with cultural and ethnic patterns, therefore such stereotypes are almost impossible to preserve in translation language. The work is aimed to recognise parameters of lacunarity, to determine the semantic components that frame the reference in translation, and to suggest the ways in rendering lacunar sociocultural stereotypes. The data for research material is taken from the Corpus of Contemporary American English. As it was ascertained in the course of semantic analysis of lexical units that denote sociocultural stereotypes, the lacunarity of these stereotypes for a target culture is determined by linguistic, historic, social, and cultural parameters. A lack of appropriate lexical means in the translation language inevitably leads to some losses in denotative meaning, whereas connotative meaning is not possible to render adequately because of discrepancy between axiological systems of source and receiving cultures. Decomposition of stereotype pragmatic information into figurative, historic and connotative components enables to emphasise relevant for a particular context pragmatic component. Highlighting the relevant component, with consideration for peculiarities of represented genre and context, is a solution to convey a part of the original pragmatic information. We deduced two major techniques for translation of sociocultural stereotypes: (1) interpreting the unknown for the recipient word or (2) introducing a cultural element, which is familiar to the recipient. Representation of socio-cultural stereotypes by means of another language requires rigorous analysis of pragmatic information that stereotypes code. The work outlines future prospects for research of lacunarity and strategies to eliminate lacunae in a target culture.

3D building models are important for many applications related to human activities in urban environments. However, due to the high complexity of the building structures, it is still difficult to automatically reconstruct building models with accurate geometric description and semantic information. To simplify this problem, this article proposes a novel approach to automatically decompose the compound buildings with symmetric roofs into semantic primitives by exploiting local symmetry contained in the building structure. In this approach, the proposed decomposition allows the overlapping of neighbor primitives and each decomposed primitive can be represented as a parametric form, which simplify the complexity of the building reconstruction and facilitate the integration of LiDAR data and aerial imagery into a parameters optimization process. The proposed method starts by extracting isolated building regions from the LiDAR point clouds. Next, point clouds belonging to each compound building are segmented into planar patches to construct an attributed graph, and then the local symmetries contained in the attributed graph are exploited to automatically decompose the compound buildings into different semantic primitives. In the final step, 2D image features are extracted depending on the initial 3D primitives generated from LiDAR data, and then the compound building is reconstructed using constraints from LiDAR data and aerial imagery by a nonlinear least squares optimization. The proposed method is applied to two datasets with different point densities to show that the complexity of building reconstruction can be reduced considerably by decomposing the compound buildings into semantic primitives. The experimental results also demonstrate that the traditional model driven methods can be further extended to the automated reconstruction of compound buildings by using the proposed semantic decomposition method.

This paper describes an algorithm for semantic decomposition. For that we surveys languages used to enrich contextual information with semantic descriptions. Such descriptions can be e.g. applied to enable reasoning when collecting vast amounts of information. In particular, we focus on the elements of the languages that make up their semantic. To do so, we compare the expressiveness of the well-known languages OWL, PDDL and MOF with a theory from linguistic called the Natural Semantic Metalanguage. We then analyze how the semantic of the language is build up and describe how semantic decomposition based on the semantic primes can be used for a so called mental lexicon. This mental lexicon can be used to reason upon semantic service description in the research domain of service match making.

Understanding continuous human actions is a non-trivial but important problem in computer vision. Although there exists a large corpus of work in the recognition of action sequences, most approaches suffer from problems relating to vast variations in motions, action combinations, and scene contexts. In this paper, we introduce a novel method for semantic segmentation and recognition of long and complex manipulation action tasks, such as 'preparing a breakfast' or 'making a sandwich'. We represent manipulations with our recently introduced 'Semantic Event Chain' (SEC) concept, which captures the underlying spatiotemporal structure of an action invariant to motion, velocity, and scene context. Solely based on the spatiotemporal interactions between manipulated objects and hands in the extracted SEC, the framework automatically parses individual manipulation streams performed either sequentially or concurrently. Using event chains, our method further extracts basic primitive elements of each parsed manipulation. Without requiring any prior object knowledge, the proposed framework can also extract object-like scene entities that exhibit the same role in semantically similar manipulations. We conduct extensive experiments on various recent datasets to validate the robustness of the framework. [ABSTRACT FROM AUTHOR]

Complex Optimization Problems has existed in many fields of science, including economics, healthcare, logistics and finance where a complex problem has to be solved. Thus, modeling a complex problem is a fundamental step to relax its complexity and achieve to a final solution of the master problem. Hierarchical optimization is a main step in optimization problems handling process. It consists of decomposing an optimization problem into two or more sub-problems; each sub-problem has its own objectives and constraints. It will help to prove the correct understanding and represent the problem in a different form that facilitates its solving. In this work, we stipulate that a hierarchical decomposition of complex problems can yield to more effective solutions. The proposed framework will contain four possible strategies which will be detailed through this paper; objective based decomposition; constraints based decomposition, semantic decomposition and data partitioning strategy. Each strategy will be argued by a set of examples from the literature to validate our framework. However, some conditions shall be verified to model the problem using such conditions are problems’ characteristics that will help to identify if a combinatorial optimization problem can be modeled within the proposed framework and they are detailed in the following subsections. [ABSTRACT FROM AUTHOR]

In the ongoing philosophical debate over the origins and nature of lexical concepts stemming from the work of Fodor (1970, 1998, 2000, 2008), the potential of first language acquisition studies as a source of evidence has been somewhat overlooked. At the lexical interface with syntax, a restricted set of lexical conceptual elements can be shown to play a pivotal role in the generation of syntactic representations, and patterns of syntactic development can elucidate the nature of such elements. An experiment is described which reveals mature knowledge of interface principles in this domain in early syntactic production. It is argued that first language research of this type can provide much-needed observable evidence for lexical semantic decomposition and against radical concept nativism. [ABSTRACT FROM AUTHOR]

The translation of lexical items is still a formidableobstacle in the field of Machine Translation. The present articleaddresses this problem from the perspective of modernlexicography, putting forth detailed monolingual lexica whichcontain highly elaborated descriptions of lexical items. Leavingaside the resolution of homonymy on the source side and theselection of the appropriate synonym on the target side, weconcentrate on the case of ``multiple lexical correspondences'’,i.e., mismatches between monosemic lexical items. We proposeto use the lexical transfer methodology. Our proposal exploits afine-grained semantic decomposition of the meaning of both sourceand target lexical items as well as information on ``actantialvalency'' (semantic restrictions) and on collocations. The lexicaldescriptions are formalized so as to allow for automatic matchingthereof. We present a rule-based procedure that selects the mostappropriate translation equivalent of a source lexical item byusing matching. The proposal is illustrated drawing on threeGerman and three Russian monosemic speech-act nouns; their lexicalentries are given in an appendix. Two examples of lexical transferbetween these lexemes are analyzed in detail. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) building models are closely related to human activities in urban environments. Due to the variations in building styles and complexity in roof structures, automatically reconstructing 3D buildings with semantics and topology information still faces big challenges. In this paper, we present an automated modeling approach that can semantically decompose and reconstruct the complex building light detection and ranging (LiDAR) point clouds into simple parametric structures, and each generated structure is an unambiguous roof semantic unit without overlapping planar primitive. The proposed method starts by extracting roof planes using a multi-label energy minimization solution, followed by constructing a roof connection graph associated with proximity, similarity, and consistency attributes. Furthermore, a progressive decomposition and reconstruction algorithm is introduced to generate explicit semantic subparts and hierarchical representation of an isolated building. The proposed approach is performed on two various datasets and compared with the state-of-the-art reconstruction techniques. The experimental modeling results, including the assessment using the International Society for Photogrammetry and Remote Sensing (ISPRS) benchmark LiDAR datasets, demonstrate that the proposed modeling method can efficiently decompose complex building models into interpretable semantic structures. [ABSTRACT FROM AUTHOR]