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Literatura académica sobre el tema "Generative Semantik"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 31 de enero de 2023

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Artículos de revistas sobre el tema "Generative Semantik"

1

Zhao, Xiaojie, Yuming Shen, Shidong Wang y Haofeng Zhang. "Boosting Generative Zero-Shot Learning by Synthesizing Diverse Features with Attribute Augmentation". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 3 (28 de junio de 2022): 3454–62. http://dx.doi.org/10.1609/aaai.v36i3.20256.

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The recent advance in deep generative models outlines a promising perspective in the realm of Zero-Shot Learning (ZSL). Most generative ZSL methods use category semantic attributes plus a Gaussian noise to generate visual features. After generating unseen samples, this family of approaches effectively transforms the ZSL problem into a supervised classification scheme. However, the existing models use a single semantic attribute, which contains the complete attribute information of the category. The generated data also carry the complete attribute information, but in reality, visual samples usually have limited attributes. Therefore, the generated data from attribute could have incomplete semantics. Based on this fact, we propose a novel framework to boost ZSL by synthesizing diverse features. This method uses augmented semantic attributes to train the generative model, so as to simulate the real distribution of visual features. We evaluate the proposed model on four benchmark datasets, observing significant performance improvement against the state-of-the-art.

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2

Jun, Hee-Gook y Dong-Hyuk Im. "Semantics-Preserving RDB2RDF Data Transformation Using Hierarchical Direct Mapping". Applied Sciences 10, n.º 20 (12 de octubre de 2020): 7070. http://dx.doi.org/10.3390/app10207070.

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Direct mapping is an automatic transformation method used to generate resource description framework (RDF) data from relational data. In the field of direct mapping, semantics preservation is critical to ensure that the mapping method outputs RDF data without information loss or incorrect semantic data generation. However, existing direct-mapping methods have problems that prevent semantics preservation in specific cases. For this reason, a mapping method is developed to perform a semantics-preserving transformation of relational databases (RDB) into RDF data without semantic information loss and to reduce the volume of incorrect RDF data. This research reviews cases that do not generate semantics-preserving results, and the corresponding problems into categories are arranged. This paper defines lemmas that represent the features of RDF data transformation to resolve those problems. Based on the lemmas, this work develops a hierarchical direct-mapping method to strictly abide by the definition of semantics preservation and to prevent semantic information loss, reducing the volume of incorrect RDF data generated. Experiments demonstrate the capability of the proposed method to perform semantics-preserving RDB2RDF data transformation, generating semantically accurate results. This work impacts future studies, which should involve the development of synchronization methods to achieve RDF data consistency when original RDB data are modified.

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3

Sun, Jingxiang, Xuan Wang, Yichun Shi, Lizhen Wang, Jue Wang y Yebin Liu. "IDE-3D". ACM Transactions on Graphics 41, n.º 6 (30 de noviembre de 2022): 1–10. http://dx.doi.org/10.1145/3550454.3555506.

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Existing 3D-aware facial generation methods face a dilemma in quality versus editability: they either generate editable results in low resolution, or high-quality ones with no editing flexibility. In this work, we propose a new approach that brings the best of both worlds together. Our system consists of three major components: (1) a 3D-semantics-aware generative model that produces view-consistent, disentangled face images and semantic masks; (2) a hybrid GAN inversion approach that initializes the latent codes from the semantic and texture encoder, and further optimizes them for faithful reconstruction; and (3) a canonical editor that enables efficient manipulation of semantic masks in canonical view and produces high-quality editing results. Our approach is competent for many applications, e.g. free-view face drawing, editing and style control. Both quantitative and qualitative results show that our method reaches the state-of-the-art in terms of photorealism, faithfulness and efficiency.

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4

Saquil, Yassir, Qun-Ce Xu, Yong-Liang Yang y Peter Hall. "Rank3DGAN: Semantic Mesh Generation Using Relative Attributes". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 5586–94. http://dx.doi.org/10.1609/aaai.v34i04.6011.

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In this paper, we investigate a novel problem of using generative adversarial networks in the task of 3D shape generation according to semantic attributes. Recent works map 3D shapes into 2D parameter domain, which enables training Generative Adversarial Networks (GANs) for 3D shape generation task. We extend these architectures to the conditional setting, where we generate 3D shapes with respect to subjective attributes defined by the user. Given pairwise comparisons of 3D shapes, our model performs two tasks: it learns a generative model with a controlled latent space, and a ranking function for the 3D shapes based on their multi-chart representation in 2D. The capability of the model is demonstrated with experiments on HumanShape, Basel Face Model and reconstructed 3D CUB datasets. We also present various applications that benefit from our model, such as multi-attribute exploration, mesh editing, and mesh attribute transfer.

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5

Koktová, E. "Focus in generative grammar Michael S. Rochemont, Joachim Jacobs, Focus and Skalen: Zur Syntax and Semantik der Gradpartikeln im Deutschen Marit R. Westergaard, Definite NP Anaphora: A Pragmatic Approach". Journal of Pragmatics 12, n.º 2 (abril de 1988): iv. http://dx.doi.org/10.1016/0378-2166(88)90063-x.

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6

Yang, Ximing, Yuan Wu, Kaiyi Zhang y Cheng Jin. "CPCGAN: A Controllable 3D Point Cloud Generative Adversarial Network with Semantic Label Generating". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 4 (18 de mayo de 2021): 3154–62. http://dx.doi.org/10.1609/aaai.v35i4.16425.

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Generative Adversarial Networks (GAN) are good at generating variant samples of complex data distributions. Generating a sample with certain properties is one of the major tasks in the real-world application of GANs. In this paper, we propose a novel generative adversarial network to generate 3D point clouds from random latent codes, named Controllable Point Cloud Generative Adversarial Network(CPCGAN). A two-stage GAN framework is utilized in CPCGAN and a sparse point cloud containing major structural information is extracted as the middle-level information between the two stages. With their help, CPCGAN has the ability to control the generated structure and generate 3D point clouds with semantic labels for points. Experimental results demonstrate that the proposed CPCGAN outperforms state-of-the-art point cloud GANs.

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7

Cipriani, Enrico. "Semantics in generative grammar". Lingvisticæ Investigationes. International Journal of Linguistics and Language Resources 42, n.º 2 (31 de diciembre de 2019): 134–85. http://dx.doi.org/10.1075/li.00033.cip.

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Abstract I provide a critical survey of the role that semantics took in the several models of generative grammar, since the 1950s until the Minimalist Program. I distinguish four different periods. In the first section, I focus on the role of formal semantics in generative grammar until the 1970s. In Section 2 I present the period of linguistic wars, when the role of semantics in linguistic theory became a crucial topic of debate. In Section 3 I focus on the formulation of conditions on transformations and Binding Theory in the 1970s and 1980s, while in the last Section I discuss the role of semantics in the minimalist approach. In this section, I also propose a semantically-based model of generative grammar, which fully endorses minimalism and Chomsky’s later position concerning the primary role of the semantic interface in the Universal Grammar modelization (Strong Minimalist Thesis). In the Discussion, I point out some theoretical problems deriving from Chomsky’s internalist interpretation of model-theoretic semantics.

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8

Yang, Guan, Ayou Han, Xiaoming Liu, Yang Liu, Tao Wei y Zhiyuan Zhang. "Enhancing Semantic-Consistent Features and Transforming Discriminative Features for Generalized Zero-Shot Classifications". Applied Sciences 12, n.º 24 (9 de diciembre de 2022): 12642. http://dx.doi.org/10.3390/app122412642.

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Generalized zero-shot learning (GZSL) aims to classify classes that do not appear during training. Recent state-of-the-art approaches rely on generative models, which use correlating semantic embeddings to synthesize unseen classes visual features; however, these approaches ignore the semantic and visual relevance, and visual features synthesized by generative models do not represent their semantics well. Although existing GZSL methods based on generative model disentanglement consider consistency between visual and semantic models, these methods consider semantic consistency only in the training phase and ignore semantic consistency in the feature synthesis and classification phases. The absence of such constraints may lead to an unrepresentative synthesized visual model with respect to semantics, and the visual and semantic features are not modally well aligned, thus causing the bias between visual and semantic features. Therefore, an approach for GZSL is proposed to enhance semantic-consistent features and discriminative features transformation (ESTD-GZSL). The proposed method can enhance semantic-consistent features at all stages of GZSL. A semantic decoder module is first added to the VAE to map synthetic and real features to the corresponding semantic embeddings. This regularization method allows synthesizing unseen classes for a more representative visual representation, and synthetic features can better represent their semantics. Then, the semantic-consistent features decomposed by the disentanglement module and the features output by the semantic decoder are transformed into enhanced semantic-consistent discriminative features and used in classification to reduce the ambiguity between categories. The experimental results show that our proposed method achieves more competitive results on four benchmark datasets (AWA2, CUB, FLO, and APY) of GZSL.

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9

Yao, Xuchen, Gosse Bouma y Yi Zhang. "Semantics-based Question Generation and Implementation". Dialogue & Discourse 3, n.º 2 (16 de marzo de 2012): 11–42. http://dx.doi.org/10.5087/dad.2012.202.

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This paper presents a question generation system based on the approach of semantic rewriting. The state-of-the-art deep linguistic parsing and generation tools are employed to convert (back and forth) between the natural language sentences and their meaning representations in the form of Minimal Recursion Semantics (MRS). By carefully operating on the semantic structures, we show a principled way of generating questions without ad-hoc manipulation of the syntactic structures. Based on the (partial) understanding of the sentence meaning, the system generates questions which are semantically grounded and purposeful. And with the support of deep linguistic grammars, the grammaticality of the generation results is warranted. Further, with a specialized ranking model, the linguistic realizations from the general purpose generation model are further refined for our the question generation task. The evaluation results from QGSTEC2010 show promising prospects of the proposed approach.

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10

Zhao, Jiaojiao, Jungong Han, Ling Shao y Cees G. M. Snoek. "Pixelated Semantic Colorization". International Journal of Computer Vision 128, n.º 4 (7 de diciembre de 2019): 818–34. http://dx.doi.org/10.1007/s11263-019-01271-4.

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AbstractWhile many image colorization algorithms have recently shown the capability of producing plausible color versions from gray-scale photographs, they still suffer from limited semantic understanding. To address this shortcoming, we propose to exploit pixelated object semantics to guide image colorization. The rationale is that human beings perceive and distinguish colors based on the semantic categories of objects. Starting from an autoregressive model, we generate image color distributions, from which diverse colored results are sampled. We propose two ways to incorporate object semantics into the colorization model: through a pixelated semantic embedding and a pixelated semantic generator. Specifically, the proposed network includes two branches. One branch learns what the object is, while the other branch learns the object colors. The network jointly optimizes a color embedding loss, a semantic segmentation loss and a color generation loss, in an end-to-end fashion. Experiments on Pascal VOC2012 and COCO-stuff reveal that our network, when trained with semantic segmentation labels, produces more realistic and finer results compared to the colorization state-of-the-art.

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Tesis sobre el tema "Generative Semantik"

1

Öhl, Peter. "Economical computation of structural descriptions in natural language a minimally radicalist theory /". [S.l. : s.n.], 2003. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB10633960.

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2

Teunissen, Lisanne Maria. "Semantic syntax : evaluation by implementation /". Utrecht (Pays-Bas) : LOT, 2002. http://catalogue.bnf.fr/ark:/12148/cb39075632j.

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3

Berger, Mikael. "Ledföljdsvariation i svenska : Verb, semantik och syntax i samband med lång objektsflytt". Thesis, Linnéuniversitetet, Institutionen för svenska språket (SV), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-32232.

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The term ‘Object Shift’ means that a weak object pronoun has been raised to a position to the left of a sentence adverbial. Primarily, there are two positions to which a weak object pronoun can be raised; one of them is called Long Object Shift. In this paper, I primarily deal with finite verb frequencies in sentences with Long Object Shift in Swedish. Also, in order to further define the verbs in these sentences I discuss verb semantic and syntactic valence in relation to the shifted object. Furthermore, I discuss the search methods which have generated sentences with Long Object Shift in written sources in Swedish; an equivalent study has never been done before. The most salient results of my study indicate that: verbs with physical denotation, and specifically vänta, möta and slå, frequently recur in sentences with Long Object Shift in written sources in Swedish; monotransitive verbs appear more often than ditransitive ones; shifted objects are most often assigned the semantic role of experiencer in the studied sentences; the search methods show divergent results, which indicates the significance of sentence adverbial initiating sentences with Long Object Shift. Lastly, the results show that semantic analyses, terminology and search methods need to be extended in order to further define the finite verb in sentences with Long Object Shift in Swedish.

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4

Kroik, David. "Differential object marking in South Saami". Licentiate thesis, Umeå universitet, Institutionen för språkstudier, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-124959.

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This licentiate thesis investigates the case and the syntactic position of the direct object in South Saami. The focus is on plural direct objects, which have Differential Object Marking, a phenomenon in which the case alternates between different types of direct objects. In South Saami, some direct objects carry the accusative case form in the plural, while others only carry the plural marker. This variation of suffix displayed on the direct object is contingent on definiteness; definite direct objects consistently display the accusative case form in the plural while indefinite direct objects, specific and nonspecific alike, lack accusative morphology. In addition to case marking, the study presents an analysis of the alternation of the syntactic position of some direct objects. Definite and indefinite specific direct objects can be realized in two positions: as the complement of the verb or in a position as specifier of the light verb projection. By contrast, indefinite nonspecific direct objects obligatorily surface in the complement position of the verb. This variability in syntactic position of some direct objects is analyzed by means of a Specificity Operator, adjoined to the DP-level of every specific NP, definite and indefinite. The operator moves as an instance of quantifier raising in order to take scope over Existential Closure (EC). EC binds NPs in its domain and give them an existential reading. Therefore, when the Specificity Operator raises, it anchors the DP it is adjoined to in a domain, which is unbound by EC and therefore facilitates a specific interpretation. The operator, void of phonological content, can raise alone to the specifier of vP as an instance of covert movement. The operator can also Pied-pipe the DP it is adjoined to, which results in overt movement of the DP. Indefinite nonspecific direct objects lack the Specificity Operator and therefore they remain in-situ in the VP, where they are bound by EC. In addition to its theoretical value, the thesis will be of use for teachers, students and others with an interest in a better understanding of the case form and the position of the direct object in South Saami.
Daennie licentiaatetjaalegisnie gïehtjedem guktie Åarjelsamien direkte objeekth gelliengiertesne kaasushgïetjieh åadtjoeh. Manne gelliengiertem veeljeme juktie åarjelaemien gïele Differential Object Marking åtna. Naakenh direkte objeekth dam giehtjiem -idie guedtieh, mij ackusatijvem gelliengïertesne muana. Jeatjah direkte objeekth barre låhkoegiehtjiem -h guedtieh, mij ajve gelliengïertem muana, menh ij kaasusem. Dan åvteste direkte objeekti kaasushaamoeh molsedieh. Mov gïehtjidimmie vuesehte ahte definijte direkteobjeekth gelliengiertesne dam ackusatijvegïehtjiem. Eah indefinijte direkte objeekth dam gïethjiem utnieh, valla barre gelliengierehaamoem utnieh. Manne vielie gïehtjedem gusnie, dennie raaje- sisnie, leah dej direkte objeekti sijjieh. Gaavneme ahte joekehtsh leah aaj ovmessie direkte objeekti gaskoeh. Definijte jïh indefinijte specifijke direkte objeekth utnieh göökte sijjieh gusnie maehtieh jïjhtedh, valla indefinite ovspecifijke direkte objeekth utnieh ajve aktem sijjiem gusnie maehtieh årrodh. Gaajhkh dah golme ovmessie direkte objeekth maehtieh maadthsijjesne årrodh goh verben komplemeente, valla definijte jïh indefinijte specifijke direkte objeekth maehtieh aaj aktene vP:n specificeerijisnie jïjhtedh. Manne daam joekehtehtem jïh vuesehtem mannasinie naemhtie jis. Mov innovasjovne lea akte specifijkeoperatovre. Dïhte lea adjungeradamme fïerhten DP:se mij lea definijte jallh indefinijte specifijke. Dïhte operatovre iktesth bæjjene DP:n sistie vP specificeerijen sïjse, men dïhte maahta aaj dam DP:m buektedh Pied-pipingen tjïrrh. Dïhte specifijkeoperatovre bæjjene juktie edtja baataridh Existential Closuren (EC) jaksoste. Gosse operatovre bæjjene, dïhte dan sov DP:m dïbrehte akten domeenese, gusnie specifijke guarkoe daerpies sjædta. Dah direkte objeekt mah eah specifijkeoperatovrem utnieh tjoerieh baetsedh VP:n sijse, jïh dannasinie EC dejtie veadta. Dannasinie existentielle guarkoem åadtjoeh. Daate tjaalege vihkeles lingvistihke teorijese, valla aaj lohkehtæjjide, learoehkidie jïh jeatjide guhth sïjhth buerebe guarkedh mij kaasusidie lea direkte objeekten jïh gusnie, dennie raajesisnie, dïhte objeekte jæjhta.
I den här licentiatavhandling undersöks kasusformen hos de direkta objekten och deras syntaktiska position i sydsamiskan. Fokus ligger på direkta objekt i pluralis, vilka uppvisar fenomenet differentiell objektsmarkering, som innebär att vissa direkta objekt bär ackusativsuffixet i plural medan andra endast bär pluralsuffixet. Denna variation i objektsmarkering är känslig för definithet. Definita direkta objekt har accusativändelsen medan indefinita, både specifika och icke-specifika direkta objekt, saknar den. Utöver själva realiseringen av kasussuffix undersöks också de direkta objektens syntaktiska position. En analys presenteras som definierar olika typer av nominalfraser och skiljer definita och specifika direkta objekt från icke-specifika direkta objekt. Den första typen uppvisar variation i sin syntaktiska placering och har möjligheten att dyka upp både i komplementställning till verbet och i en den lilla verbfrasens specificerare, det vill säga vid gränsen för den lexikala fasen. Indefinita icke-specifika direkta objekt, som utgör den andra typen, kan bara uppträda i en position som komplement till verbet. På basis av den analys som inkluderar min innovation Specifikhetsoperatorn, vilken är adjungerad till alla definita och specifika direkta objekts DP-nivå, kan de två positionerna förklaras. Specifikhetsoperatorn flyttar alltid till vP:s specifierare som en kvantifierarinteraktion, där Specifikhetstoperatorn får räckvidd över Existential Closure (EC) och förankrar sin DP i en domän där en specifik tolkning blir nödvändig. Detta är en typ av osynlig flytt. Flytten kan också vara synlig. I det fallet sker medfraktning (Pied-piping) när Specifikhetsoperatorn tar med sig den DP den är adjungerad till när den flyttar till vPs specifierare. Direkta object som saknar Specifikhetsoperatorn stannar i positionen som komplement till verbet och binds därför av EC, vilket leder till att de får en existentiell tolkning. Bortom sitt värde för lingvistisk teoribildning kommer avhandlingen också att bli viktig för lärare, studenter och elever såväl som för andra med ett intresse av att bättre förstå vilket kasus som uppträder på sydsamiska direkta objekt och dessa objekts position i satsen.

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5

Han, Wei. "Wrapper application generation for semantic web". Diss., Georgia Institute of Technology, 2003. http://hdl.handle.net/1853/5407.

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Al-Jabri, S. K. "Generating Arabic words from semantic descriptions". Thesis, University of Edinburgh, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.640291.

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This thesis addresses Arabic word generation from semantic descriptions. The purpose of this study is two-fold. Firstly, to analyse semantic specifications of Arabic derivation and, secondly, to build a computational model that performs Arabic lexical choices using a knowledge-base that is motivated by the semantics of derivation. Lexical choice in a highly derived language, such as Arabic, is non-trivial in MT and NLG systems where the semantic input cannot be biased directly towards the words that the language provides. On the linguistic side, the major contribution of this study is a new framework that organises semantic interactions in Arabic derivation in a two-layer semantics: an inner layer accounts for core meanings realised by Arabic consonantal roots and an outer layer accounts for semantic features associated with derivational moulds. The meaning of derived words can be established using controlled interactions between the two layers. The interactions, accordingly, state the link between semantic concepts on the one hand and their surface realisations on the other. Furthermore, derivational conditions imposed on the interactions account for many derivational gaps in Arabic. On the computational side, semantic generalisation motivated by the two layer semantics are organised into a taxonomic knowledge-base. Following the tradition in the KL-ONE family, the knowledge-base adopts a separation between definitional and assertional components. The former is used to represent semantic specifications of Arabic derived concepts while the latter is used to describe their linguistic properties such as roots and moulds. The knowledge-base, as a whole, is built around structured hierarchies that represent an integration of knowledge resources such as default and multiple inheritance, and subsumption. This integration allows not only for an expressive representation of Arabic derivation but also for the mapping of semantic inputs into Arabic derived lexical items by means of automatic classification.

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7

Green, Stephen Joseph. "Automatically generating hypertext by computing semantic similarity". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape16/PQDD_0019/NQ27656.pdf.

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Ball, Stephen Wayne. "Semantic web service generation for text classification". Thesis, University of Southampton, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.430674.

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9

Ablonskis, Linas. "Programos kodo generavimas naudojant UML veiksmų semantiką". Master's thesis, Lithuanian Academic Libraries Network (LABT), 2006. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2006~D_20060602_001134-80600.

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The recent version of UML 2.0 (in year 2006) specifies activities and actions, which allow describing low level behavior of software system being modeled, in implementation independent fashion. This work analyzes suitability of UML 2.0 activities and actions for generating full program code (or as many as possible). It also proposes a method for generating program code from UML 2.0 activities and actions, which consists of a way to express UML activities with concurrently executing actions in a sequential execution scenario and a way to determine the exact code template (among the few possible) for UML activity elements based on identifying the particular context of element in question.

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10

Buys, Jan Moolman. "Incremental generative models for syntactic and semantic natural language processing". Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:a9a7b5cf-3bb1-4e08-b109-de06bf387d1d.

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This thesis investigates the role of linguistically-motivated generative models of syntax and semantic structure in natural language processing (NLP). Syntactic well-formedness is crucial in language generation, but most statistical models do not account for the hierarchical structure of sentences. Many applications exhibiting natural language understanding rely on structured semantic representations to enable querying, inference and reasoning. Yet most semantic parsers produce domain-specific or inadequately expressive representations. We propose a series of generative transition-based models for dependency syntax which can be applied as both parsers and language models while being amenable to supervised or unsupervised learning. Two models are based on Markov assumptions commonly made in NLP: The first is a Bayesian model with hierarchical smoothing, the second is parameterised by feed-forward neural networks. The Bayesian model enables careful analysis of the structure of the conditioning contexts required for generative parsers, but the neural network is more accurate. As a language model the syntactic neural model outperforms both the Bayesian model and n-gram neural networks, pointing to the complementary nature of distributed and structured representations for syntactic prediction. We propose approximate inference methods based on particle filtering. The third model is parameterised by recurrent neural networks (RNNs), dropping the Markov assumptions. Exact inference with dynamic programming is made tractable here by simplifying the structure of the conditioning contexts. We then shift the focus to semantics and propose models for parsing sentences to labelled semantic graphs. We introduce a transition-based parser which incrementally predicts graph nodes (predicates) and edges (arguments). This approach is contrasted against predicting top-down graph traversals. RNNs and pointer networks are key components in approaching graph parsing as an incremental prediction problem. The RNN architecture is augmented to condition the model explicitly on the transition system configuration. We develop a robust parser for Minimal Recursion Semantics, a linguistically-expressive framework for compositional semantics which has previously been parsed only with grammar-based approaches. Our parser is much faster than the grammar-based model, while the same approach improves the accuracy of neural Abstract Meaning Representation parsing.

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Más fuentes

Libros sobre el tema "Generative Semantik"

1

The generative lexicon. Cambridge, Mass: MIT Press, 1995.

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2

Discourse semantics. Oxford, OX, UK: B. Blackwell, 1985.

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3

Rohr, Rupprecht. Sigmatik: Beziehungen zwischen sprachlichen Zeichen und Wirklichkeitsausschnitten dargestellt auf der Grundlage der Explikativen Semantik : Vorüberlegungen und Vorarbeiten. Frankfurt am Main: Haag + Herchen, 1997.

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4

Events and semantic architecture. Oxford: Oxford University Press, 2005.

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5

A crosslinguistic study on the semantics of motion verbs in English and Spanish. München: LINCOM Europa, 2009.

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6

Lexico-logical form: A radically minimalist theory. Cambridge, Mass: MIT Press, 1995.

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7

Brody, Michael. Lexico-logical form: A radically minimalist theory. Cambridge, Mass: MIT Press, 1995.

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8

V, T͡Selishchev V., ed. Lingvistika i strukturnai͡a semantika. Novosibirsk: Izd-vo "Nauka," Sibirskoe otd-nie, 1987.

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Angelika, Kratzer, ed. Semantics in generative grammar. Malden, MA: Blackwell, 1998.

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Paducheva, E. V. O semantike sintaksisa: Materialy k transformat︠s︡ionnoĭ grammatike russkogo i︠a︡zyka. 2^a ed. Moskva: URSS, 2007.

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Más fuentes

Capítulos de libros sobre el tema "Generative Semantik"

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McCawley, James D. "Generative semantics". En Handbook of Pragmatics, 311–19. Amsterdam: John Benjamins Publishing Company, 1995. http://dx.doi.org/10.1075/hop.m.gen2.

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McCawley, James D. "Generative semantics". En Grammar, Meaning and Pragmatics, 117–28. Amsterdam: John Benjamins Publishing Company, 2009. http://dx.doi.org/10.1075/hoph.5.07mcc.

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McCawley, James D. "Generative semantics". En Handbook of Pragmatics, 657–66. Amsterdam: John Benjamins Publishing Company, 2022. http://dx.doi.org/10.1075/hop.m2.gen2.

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Bense, Hermann, Ulrich Schade y Michael Dembach. "Generating Natural Language Texts". En Semantic Applications, 75–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-55433-3_6.

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Zhu, Fangwei, Zhengguo Wang, Juanzi Li, Lei Hou, Jiaxin Shi, Shining Lv, Ran Shen y Junjun Jiang. "Event-Oriented Wiki Document Generation". En Semantic Technology, 50–66. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41407-8_4.

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Wang, Xin, Steffen Staab y Thanassis Tiropanis. "ASPG: Generating OLAP Queries for SPARQL Benchmarking". En Semantic Technology, 171–85. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50112-3_13.

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Gupta, Dhruv, Klaus Berberich, Jannik Strötgen y Demetrios Zeinalipour-Yazti. "Generating Semantic Aspects for Queries". En The Semantic Web, 162–78. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21348-0_11.

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Moschoyiannis, Sotiris, Alexandros Marinos y Paul Krause. "Generating SQL Queries from SBVR Rules". En Semantic Web Rules, 128–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16289-3_12.

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Giacometti, Arnaud, Béatrice Markhoff y Arnaud Soulet. "Comparison Table Generation from Knowledge Bases". En The Semantic Web, 179–94. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77385-4_11.

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Löbner, Sebastian. "Cascades. Goldman’s Level-Generation, Multilevel Categorization of Action, and Multilevel Verb Semantics". En Language, Cognition, and Mind, 263–307. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-50200-3_13.

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AbstractThe paper proposes a novel theory of the categorization of acts and applies it to the semantics of action verbs, with fundamental consequences for semantic theory and beyond. The theory is based on Goldman’s (Theory of human action. Princeton University Press, Princeton, NJ, 1970) multilevel theory of action which is taken here as a theory of categorization. Goldman’s central notion is level-generation: acts of a type may under circumstances generate acts of other, more abstract types. The acts form a hierarchical structure which Goldman calls an act-tree. Level-generation results in a conceptual relation called c-constitution here, i.e. constitution under the given circumstances; I also introduce the more general term cascade for act-trees. In the second part, multilevel cascade-structure categorization is combined with a cognitive semantics that models meanings with Barsalou frames. A multilevel analysis of the concept of writing is discussed in depth and detail in order to illustrate the potential and the consequences of a cascade approach to verb semantics. It is shown that the concept of c-constitution can be generalized as to cover the roles of persons and objects across levels in a cascade. The generalization suggests that multilevel categorization may be a very general and fundamental phenomenon in the psychology of categorization.

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Actas de conferencias sobre el tema "Generative Semantik"

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Jiang, Xiaoze, Jing Yu, Yajing Sun, Zengchang Qin, Zihao Zhu, Yue Hu y Qi Wu. "DAM: Deliberation, Abandon and Memory Networks for Generating Detailed and Non-repetitive Responses in Visual Dialogue". En Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/96.

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Visual Dialogue task requires an agent to be engaged in a conversation with human about an image. The ability of generating detailed and non-repetitive responses is crucial for the agent to achieve human-like conversation. In this paper, we propose a novel generative decoding architecture to generate high-quality responses, which moves away from decoding the whole encoded semantics towards the design that advocates both transparency and flexibility. In this architecture, word generation is decomposed into a series of attention-based information selection steps, performed by the novel recurrent Deliberation, Abandon and Memory (DAM) module. Each DAM module performs an adaptive combination of the response-level semantics captured from the encoder and the word-level semantics specifically selected for generating each word. Therefore, the responses contain more detailed and non-repetitive descriptions while maintaining the semantic accuracy. Furthermore, DAM is flexible to cooperate with existing visual dialogue encoders and adaptive to the encoder structures by constraining the information selection mode in DAM. We apply DAM to three typical encoders and verify the performance on the VisDial v1.0 dataset. Experimental results show that the proposed models achieve new state-of-the-art performance with high-quality responses. The code is available at https://github.com/JXZe/DAM.

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Hoffmann, Christoph M. "On the Semantics of Generative Geometry Representations". En ASME 1993 Design Technical Conferences. American Society of Mechanical Engineers, 1993. http://dx.doi.org/10.1115/detc1993-0414.

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Abstract We argue that geometry constructs and solid operations currently emerging in commercial, feature-based CAD systems are without an appropriate semantic foundation, and that a rigorous semantics poses significant research problems. In particular, we argue that a formalization of feature attachment and other generative constructs in the presence of geometric constraints cannot be based solely on conventional boundary representation or on current geometry standards.

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Li, Chen, Chikashige Yamanaka, Kazuma Kaitoh y Yoshihiro Yamanishi. "Transformer-based Objective-reinforced Generative Adversarial Network to Generate Desired Molecules". En Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/539.

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Deep generative models of sequence-structure data have attracted widespread attention in drug discovery. However, such models cannot fully extract the semantic features of molecules from sequential representations. Moreover, mode collapse reduces the diversity of the generated molecules. This paper proposes a transformer-based objective-reinforced generative adversarial network (TransORGAN) to generate molecules. TransORGAN leverages a transformer architecture as a generator and uses a stochastic policy gradient for reinforcement learning to generate plausible molecules with rich semantic features. The discriminator grants rewards that guide the policy update of the generator, while an objective-reinforced penalty encourages the generation of diverse molecules. Experiments were performed using the ZINC chemical dataset, and the results demonstrated the usefulness of TransORGAN in terms of uniqueness, novelty, and diversity of the generated molecules.

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Zhu, Huawei y Yusheng Liu. "Abstraction of Semantic Mid-Surface Based on Rib-Feature Recognition". En ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47054.

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Mid-surface abstraction is an effective simplification method for thin-wall models. The complexity of finite element analysis (FEA) for a mid-surface model can be reduced greatly after abstraction. Although the model decomposition method is adopted for mid-surface extraction, it is hard to obtain the correct mid-surface model for complex models since the existing heuristic rule based methods lack of design intention. In addition, the mid-surface model is not easy to reuse. In this study, a semantic based mid-surface model representation and generation method is proposed. Firstly, a hierarchical semantic mid-surface model based on rib-feature decomposition is proposed. Secondly, based on the reorganization of rib-features and decomposition of the thin-wall model, the rib-features’ semantic information are obtained by the abstraction of the structure and connection in the thin-wall model. Then the hierarchical structure is generated by connection semantics. According to the various structure semantics, different abstraction methods will be employed to get the mid-surface patch for each sub region. Finally, the hierarchical semantic mid-surface model is constructed by the generation of the connection relationship between mid-surface patches based on the connection semantics between the rib-features. This semantic model ensures the high efficiency and accuracy of mid-surface regeneration when local modifications occur to a thin-wall model. A typical example is given to demonstrate the process.

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Zhang, Ge, Di Jin, Jian Gao, Pengfei Jiao, Françoise Fogelman-Soulié y Xin Huang. "Finding Communities with Hierarchical Semantics by Distinguishing General and Specialized topics". En Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/507.

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Using network topology and semantic contents to find topic-related communities is a new trend in the field of community detection. By analyzing texts in social networks, we find that topics in networked contents are often hierarchical. In most cases, they have a two-level semantic structure with general and specialized topics, to respectively denote common and specific interests of communities. However, the existing community detection methods ignore such a hierarchy and take all words used to describe node semantics from an identical perspective. This indiscriminate use of words leads to natural defects in depicting networked content in which the deep semantics is not fully utilized. To address this problem, we propose a novel probabilistic generative model. By distinguishing the general and specialized topics of words, our model not only can find community structures more accurately, but also provide two-level semantic interpretation for each community. We train the model by deriving an efficient inference method under the framework of variational expectation-maximization. We provide a case study to show the ability of our algorithm in deep semantic interpretability of communities. The superiority of our algorithm for community detection is further demonstrated in comparison with eight state-of-the-art algorithms on eight real-world networks.

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6

Hong, Ziming, Shiming Chen, Guo-Sen Xie, Wenhan Yang, Jian Zhao, Yuanjie Shao, Qinmu Peng y Xinge You. "Semantic Compression Embedding for Generative Zero-Shot Learning". En Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/134.

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Generative methods have been successfully applied in zero-shot learning (ZSL) by learning an implicit mapping to alleviate the visual-semantic domain gaps and synthesizing unseen samples to handle the data imbalance between seen and unseen classes. However, existing generative methods simply use visual features extracted by the pre-trained CNN backbone. These visual features lack attribute-level semantic information. Consequently, seen classes are indistinguishable, and the knowledge transfer from seen to unseen classes is limited. To tackle this issue, we propose a novel Semantic Compression Embedding Guided Generation (SC-EGG) model, which cascades a semantic compression embedding network (SCEN) and an embedding guided generative network (EGGN). The SCEN extracts a group of attribute-level local features for each sample and further compresses them into the new low-dimension visual feature. Thus, a dense-semantic visual space is obtained. The EGGN learns a mapping from the class-level semantic space to the dense-semantic visual space, thus improving the discriminability of the synthesized dense-semantic unseen visual features. Extensive experiments on three benchmark datasets, i.e., CUB, SUN and AWA2, demonstrate the signiﬁcant performance gains of SC-EGG over current state-of-the-art methods and its baselines.

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Ayanthi, D. M. A. y Sarasi Munasinghe. "Text-to-Face Generation with StyleGAN2". En 10th International Conference on Foundations of Computer Science & Technology (FCST 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120805.

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Synthesizing images from text descriptions has become an active research area with the advent of Generative Adversarial Networks. The main goal here is to generate photo-realistic images that are aligned with the input descriptions. Text-to-Face generation(T2F) is a sub-domain of Text-to-Image generation(T2I) that is more challenging due to the complexity and variation of facial attributes. It has a number of applications mainly in the domain of public safety. Even though several models are available for T2F, there is still the need to improve the image quality and the semantic alignment. In this research, we propose a novel framework, to generate facial images that are well-aligned with the input descriptions. Our framework utilizes the highresolution face generator, StyleGAN2, and explores the possibility of using it in T2F. Here, we embed text in the input latent space of StyleGAN2 using BERT embeddings and oversee the generation of facial images using text descriptions. We trained our framework on attributebased descriptions to generate images of 1024x1024 in resolution. The images generated exhibit a 57% similarity to the ground truth images, with a face semantic distance of 0.92, outperforming state-of-the-artwork. The generated images have a FID score of 118.097 and the experimental results show that our model generates promising images.

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Martini, Massimo, Roberto Pierdicca, Marina Paolanti, Ramona Quattrini, Eva Savina Malinverni y Emanuele Frontoni. "GENERATIVE NETWORKS FOR POINT CLOUD GENERATION IN CULTURAL HERITAGE DOMAIN". En ARQUEOLÓGICA 2.0 - 9th International Congress & 3rd GEORES - GEOmatics and pREServation. Editorial Universitat Politécnica de Valéncia: Editorial Universitat Politécnica de Valéncia, 2021. http://dx.doi.org/10.4995/arqueologica9.2021.12101.

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In the Cultural Heritage (CH) domain, the semantic segmentation of 3D point clouds with Deep Learning (DL) techniques allows to recognize historical architectural elements, at a suitable level of detail, and hence expedite the process of modelling historical buildings for the development of BIM models from survey data. However, it is more difficult to collect a balanced dataset of labelled architectural elements for training a network. In fact, the CH objects are unique, and it is challenging for the network to recognize this kind of data. In recent years, Generative Networks have proven to be proper for generating new data. Starting from such premises, in this paper Generative Networks have been used for augmenting a CH dataset. In particular, the performances of three state-of-art Generative Networks such as PointGrow, PointFLow and PointGMM have been compared in terms of Jensen-Shannon Divergence (JSD), the Minimum Matching Distance-Chamfer Distance (MMD-CD) and the Minimum Matching Distance-Earth Mover’s Distance (MMD-EMD). The objects generated have been used for augmenting two classes of ArCH dataset, which are columns and windows. Then a DGCNN-Mod network was trained and tested for the semantic segmentation task, comparing the performance in the case of the ArCH dataset without and with augmentation.

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Xue, Yuting, Heng Zhou, Yuxuan Ding y Xiao Shan. "Adaptive Forgetting, Drafting and Comprehensive Guiding: Text-to-Image Synthesis with Hierarchical Generative Adversarial Networks". En 11th International Conference on Embedded Systems and Applications (EMSA 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120623.

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The generation task from text to image generates cross modal data with consistent content by mining the semantic consistency contained in two different modal information of text and image. Due to the differences between the two modes, the task of text to image generation faces many difficulties and challenges. In this paper, we propose to boost the text-to-image synthesis through an adaptive learning and generating generative adversarial networks (ALG-GANs). First, we propose an adaptive forgetting mechanism in the generator to reduce the error accumulation and learn knowledge flexibly in the cascade structure. Besides, to evade the mode collapse caused by a strong biased surveillance, we propose a multi-task discriminator using weaksupervision information to guide the generator more comprehensively and maintain the semantic consistency in the cascade generation process. To avoid the refine difficulty aroused by the bad initialization, we judge the quality of initialization before further processing. The generator will re-sample the noise and re-initialize the bad initializations to obtain good ones. All the above contributions have been integrated in a unified framework, which is an adaptive forgetting, drafting and comprehensive guiding based text-to-image synthesis method with hierarchical generative adversarial networks. The model is evaluated on the Caltech-UCSD Birds 200 (CUB) dataset and the Oxford 102 Category Flowers (Oxford) dataset with standard metrics. The results on Inception Score (IS) and Fréchet Inception Distance (FID) show that our model outperforms the previous methods.

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Pogodalla, Sylvain. "Generation, Lambek calculus, Montague's semantics and semantic proof nets". En the 18th conference. Morristown, NJ, USA: Association for Computational Linguistics, 2000. http://dx.doi.org/10.3115/992730.992737.

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Informes sobre el tema "Generative Semantik"

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Yegneswaran, Vinod, Jonathon T. Giffin, Paul Barford y Somesh Jha. An Architecture for Generating Semantics-Aware Signatures. Fort Belvoir, VA: Defense Technical Information Center, enero de 2006. http://dx.doi.org/10.21236/ada449063.

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González-Montaña, Luis Antonio. Semantic-based methods for morphological descriptions: An applied example for Neotropical species of genus Lepidocyrtus Bourlet, 1839 (Collembola: Entomobryidae). Verlag der Österreichischen Akademie der Wissenschaften, noviembre de 2021. http://dx.doi.org/10.1553/biosystecol.1.e71620.

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The production of semantic annotations has gained renewed attention due to the development of anatomical ontologies and the documentation of morphological data. Two methods are proposed in this production, differing in their methodological and philosophical approaches: class-based method and instance-based method. The first, the semantic annotations are established as class expressions, while in the second, the annotations incorporate individuals. An empirical evaluation of the above methods was applied in the morphological description of Neotropical species of the genus Lepidocyrtus (Collembola: Entomobryidae: Lepidocyrtinae). The semantic annotations are expressed as RDF triple, which is a language most flexible than the Entity-Quality syntax used commonly in the description of phenotypes. The morphological descriptions were built in Protégé 5.4.0 and stored in an RDF store created with Fuseki Jena. The semantic annotations based on RDF triple increase the interoperability and integration of data from diverse sources, e.g., museum data. However, computational challenges are present, which are related with the development of semi-automatic methods for the generation of RDF triple, interchanging between texts and RDF triple, and the access by non-expert users.

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Decleir, Cyril, Mohand-Saïd Hacid y Jacques Kouloumdjian. A Database Approach for Modeling and Querying Video Data. Aachen University of Technology, 1999. http://dx.doi.org/10.25368/2022.90.

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Indexing video data is essential for providing content based access. In this paper, we consider how database technology can offer an integrated framework for modeling and querying video data. As many concerns in video (e.g., modeling and querying) are also found in databases, databases provide an interesting angle to attack many of the problems. From a video applications perspective, database systems provide a nice basis for future video systems. More generally, database research will provide solutions to many video issues even if these are partial or fragmented. From a database perspective, video applications provide beautiful challenges. Next generation database systems will need to provide support for multimedia data (e.g., image, video, audio). These data types require new techniques for their management (i.e., storing, modeling, querying, etc.). Hence new solutions are significant. This paper develops a data model and a rule-based query language for video content based indexing and retrieval. The data model is designed around the object and constraint paradigms. A video sequence is split into a set of fragments. Each fragment can be analyzed to extract the information (symbolic descriptions) of interest that can be put into a database. This database can then be searched to find information of interest. Two types of information are considered: (1) the entities (objects) of interest in the domain of a video sequence, (2) video frames which contain these entities. To represent these information, our data model allows facts as well as objects and constraints. We present a declarative, rule-based, constraint query language that can be used to infer relationships about information represented in the model. The language has a clear declarative and operational semantics. This work is a major revision and a consolidation of [12, 13].

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