Relevant bibliographies by topics / Robust Minimal Recursion Semantic

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Academic literature on the topic 'Robust Minimal Recursion Semantic'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "Robust Minimal Recursion Semantic"

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Jørgensen, Fredrik, and Jan Tore Lønning. "A Minimal Recursion Semantic Analysis of Locatives." Computational Linguistics 35, no. 2 (June 2009): 229–70. http://dx.doi.org/10.1162/coli.06-69-prep5.

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The article describes a pilot implementation of a grammar containing different types of locative PPs. In particular, we investigate the distinction between static and directional locatives, and between different types of directional locatives. Locatives may act as modifiers as well as referring expressions depending on the syntactic context. We handle this with a single lexical entry. The implementation is of Norwegian locatives, but English locatives are both discussed and compared to Norwegian locatives. The semantic analysis is based on a proposal by Markus Kracht (2002), and we show how this analysis can be incorporated into Minimal Recursion Semantics (MRS) (Copestake et al. 2005). We discuss how the resulting system may be applied in a transfer-based machine translation system, and how we can map from a shallow MRS representation to a deeper semantic representation.
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Manshadi, Mehdi, Daniel Gildea, and James F. Allen. "A Notion of Semantic Coherence for Underspecified Semantic Representation." Computational Linguistics 44, no. 1 (March 2018): 39–83. http://dx.doi.org/10.1162/coli_a_00307.

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The general problem of finding satisfying solutions to constraint-based underspecified representations of quantifier scope is NP-complete. Existing frameworks, including Dominance Graphs, Minimal Recursion Semantics, and Hole Semantics, have struggled to balance expressivity and tractability in order to cover real natural language sentences with efficient algorithms. We address this trade-off with a general principle of coherence, which requires that every variable introduced in the domain of discourse must contribute to the overall semantics of the sentence. We show that every underspecified representation meeting this criterion can be efficiently processed, and that our set of representations subsumes all previously identified tractable sets.
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Xu, Yang, Emmy Liu, and Terry Regier. "Numeral Systems Across Languages Support Efficient Communication: From Approximate Numerosity to Recursion." Open Mind 4 (August 2020): 57–70. http://dx.doi.org/10.1162/opmi_a_00034.

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Languages differ qualitatively in their numeral systems. At one extreme, some languages have a small set of number terms, which denote approximate or inexact numerosities; at the other extreme, many languages have forms for exact numerosities over a very large range, through a recursively defined counting system. Why do numeral systems vary as they do? Here, we use computational analyses to explore the numeral systems of 30 languages that span this spectrum. We find that these numeral systems all reflect a functional need for efficient communication, mirroring existing arguments in other semantic domains such as color, kinship, and space. Our findings suggest that cross-language variation in numeral systems may be understood in terms of a shared functional need to communicate precisely while using minimal cognitive resources.
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Simov, Kiril, and Petya Osenova. "Special Thematic Section on Semantic Models for Natural Language Processing (Preface)." Cybernetics and Information Technologies 18, no. 1 (March 1, 2018): 93–94. http://dx.doi.org/10.2478/cait-2018-0008.

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Abstract With the availability of large language data online, cross-linked lexical resources (such as BabelNet, Predicate Matrix and UBY) and semantically annotated corpora (SemCor, OntoNotes, etc.), more and more applications in Natural Language Processing (NLP) have started to exploit various semantic models. The semantic models have been created on the base of LSA, clustering, word embeddings, deep learning, neural networks, etc., and abstract logical forms, such as Minimal Recursion Semantics (MRS) or Abstract Meaning Representation (AMR), etc. Additionally, the Linguistic Linked Open Data Cloud has been initiated (LLOD Cloud) which interlinks linguistic data for improving the tasks of NLP. This cloud has been expanding enormously for the last four-five years. It includes corpora, lexicons, thesauri, knowledge bases of various kinds, organized around appropriate ontologies, such as LEMON. The semantic models behind the data organization as well as the representation of the semantic resources themselves are a challenge to the NLP community. The NLP applications that extensively rely on the above discussed models include Machine Translation, Information Extraction, Question Answering, Text Simplification, etc.
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Yao, Xuchen, Gosse Bouma, and Yi Zhang. "Semantics-based Question Generation and Implementation." Dialogue & Discourse 3, no. 2 (March 16, 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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McArthur, J. J., and Brandon Bortoluzzi. "Lean-Agile FM-BIM: a demonstrated approach." Facilities 36, no. 13/14 (October 1, 2018): 676–95. http://dx.doi.org/10.1108/f-04-2017-0045.

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Purpose This paper aims to respond to the high cost of facility management-enabled building information model (FM-BIM) creation and maintenance, a significant and under-researched barrier to adoption for existing buildings. The resultant approach focuses on only value-adding content (“Lean”) developed flexibly and iteratively in collaboration with end-users (“Agile”). Design/methodology/approach Five case studies were developed for university and hospital buildings in collaboration with end-users, guided by the process presented. These informed the refinement of a robust and flexible approach to increase BIM functionality with minimal geometry, focusing instead on the development of specific parameters to map semantic information necessary for each desired FM use. Findings The resulting BIM provided a breadth of model functionality with minimal modeling effort: 15 hours average implementation time per supported FM use. This low level of effort was achieved by limiting geometry to where it is necessary for the FM use implementation. Instead, the model incorporated the majority of geometry by reference and focused on semantic and topological parameters to house FM information. Research limitations/implications This study provides the basis for a new ontology structure focused on defining the rules for hosting asset management data (host entity, parameter type and characteristics) to reduce the reliance on complex geometric model development. Practical implications By prioritizing highly beneficial applications, early investment is minimized, providing quick returns at low risk, demonstrating the value of FM-BIM to end-users. Originality/value The Lean-Agile approach addresses the known research gap of low-effort, flexible approaches to FM-BIM model creation and maintenance and its effectiveness is analyzed through five case studies.
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Sugiura, Motoaki, Yoko Mano, Akihiro Sasaki, and Norihiro Sadato. "Beyond the Memory Mechanism: Person-selective and Nonselective Processes in Recognition of Personally Familiar Faces." Journal of Cognitive Neuroscience 23, no. 3 (March 2011): 699–715. http://dx.doi.org/10.1162/jocn.2010.21469.

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Special processes recruited during the recognition of personally familiar people have been assumed to reflect the rich episodic and semantic information that selectively represents each person. However, the processes may also include person nonselective ones, which may require interpretation in terms beyond the memory mechanism. To examine this possibility, we assessed decrease in differential activation during the second presentation of an identical face (repetition suppression) as an index of person selectivity. During fMRI, pictures of personally familiar, famous, and unfamiliar faces were presented to healthy subjects who performed a familiarity judgment. Each face was presented once in the first half of the experiment and again in the second half. The right inferior temporal and left inferior frontal gyri were activated during the recognition of both types of familiar faces initially, and this activation was suppressed with repetition. Among preferentially activated regions for personally familiar over famous faces, robust suppression in differential activation was exhibited in the bilateral medial and anterior temporal structures, left amygdala, and right posterior STS, all of which are known to process episodic and semantic information. On the other hand, suppression was minimal in the posterior cingulate, medial prefrontal, right inferior frontal, and intraparietal regions, some of which were implicated in social cognition and cognitive control. Thus, the recognition of personally familiar people is characterized not only by person-selective representation but also by nonselective processes requiring a research framework beyond the memory mechanism, such as a social adaptive response.
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Hodosh, M., P. Young, and J. Hockenmaier. "Framing Image Description as a Ranking Task: Data, Models and Evaluation Metrics." Journal of Artificial Intelligence Research 47 (August 30, 2013): 853–99. http://dx.doi.org/10.1613/jair.3994.

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The ability to associate images with natural language sentences that describe what is depicted in them is a hallmark of image understanding, and a prerequisite for applications such as sentence-based image search. In analogy to image search, we propose to frame sentence-based image annotation as the task of ranking a given pool of captions. We introduce a new benchmark collection for sentence-based image description and search, consisting of 8,000 images that are each paired with five different captions which provide clear descriptions of the salient entities and events. We introduce a number of systems that perform quite well on this task, even though they are only based on features that can be obtained with minimal supervision. Our results clearly indicate the importance of training on multiple captions per image, and of capturing syntactic (word order-based) and semantic features of these captions. We also perform an in-depth comparison of human and automatic evaluation metrics for this task, and propose strategies for collecting human judgments cheaply and on a very large scale, allowing us to augment our collection with additional relevance judgments of which captions describe which image. Our analysis shows that metrics that consider the ranked list of results for each query image or sentence are significantly more robust than metrics that are based on a single response per query. Moreover, our study suggests that the evaluation of ranking-based image description systems may be fully automated.
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Frank, Anette, Kathrin Spreyer, Witold Drożdżyński, Hans-Ulrich Krieger, and Ulrich Schäfer. "Constraint-based RMRS construction from shallow grammars." Proceedings of the International Conference on Head-Driven Phrase Structure Grammar, October 1, 2004. http://dx.doi.org/10.21248/hpsg.2004.22.

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We present a constraint-based syntax-semantics interface for the construction of RMRS (Robust Minimal Recursion Semantics) representations from shallow grammars. The architecture is designed to allow modular interfaces to existing shallow grammars of various depth - ranging from chunk grammars to context-free stochastic grammars. We define modular semantics construction principles in a typed feature structure formalism that allow flexible adaptation to alternative grammars and different languages.
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Bonami, Olivier. "syntax-semantics interface for tense and aspect in French." Proceedings of the International Conference on Head-Driven Phrase Structure Grammar, May 1, 2002. http://dx.doi.org/10.21248/hpsg.2001.3.

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This paper proposes an HPSG account of the French tense and aspect system, focussing on the analysis of the passé simple (simple past) and imparfait (imperfective) tenses and their interaction with aspectually sensitive adjuncts. Starting from de Swart's (1998) analysis of the semantics of tense and aspect, I show that while the proposed semantic representations are appropriate, the analysis of implicit aspectual operators as coercion operators is inadequate. The proposed HPSG analysis relies on Minimal Recursion Semantics to relate standard syntactic structures with de Swart-style semantic representations. The analysis has two crucial features: first, it assumes that the semantic contribution of tense originates in the verb's semantic representation, despite the fact that tense can get wide scope over other semantic elements. Second, it allows the occurrence of implicit aspectual operators to be controlled by the verb's inflectional class, which accounts for their peculiar distribution.
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Dissertations / Theses on the topic "Robust Minimal Recursion Semantic"

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Канищева, Ольга Валерьевна. "Модели представления лингвистических данных." Thesis, НТУ "ХПИ", 2012. http://repository.kpi.kharkov.ua/handle/KhPI-Press/3423.

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Kanishcheva, O. V. "Using of Minimal Recursion Semantic representation for linguistic information." Thesis, Сумський державний університет, 2013. http://essuir.sumdu.edu.ua/handle/123456789/31777.

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In this article will discuss current approaches to the representation of linguistic information. Particular attention is paid Minimal Recursion Semantic (MRS) and the Robust Minimal Recursion Semantic (RMRS) as one of the most promising directions. Shows the use of Minimal Recursion Semantic (MRS) for the Russian language. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/31777
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Emerson, Guy Edward Toh. "Functional distributional semantics : learning linguistically informed representations from a precisely annotated corpus." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/284882.

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The aim of distributional semantics is to design computational techniques that can automatically learn the meanings of words from a body of text. The twin challenges are: how do we represent meaning, and how do we learn these representations? The current state of the art is to represent meanings as vectors - but vectors do not correspond to any traditional notion of meaning. In particular, there is no way to talk about 'truth', a crucial concept in logic and formal semantics. In this thesis, I develop a framework for distributional semantics which answers this challenge. The meaning of a word is not represented as a vector, but as a 'function', mapping entities (objects in the world) to probabilities of truth (the probability that the word is true of the entity). Such a function can be interpreted both in the machine learning sense of a classifier, and in the formal semantic sense of a truth-conditional function. This simultaneously allows both the use of machine learning techniques to exploit large datasets, and also the use of formal semantic techniques to manipulate the learnt representations. I define a probabilistic graphical model, which incorporates a probabilistic generalisation of model theory (allowing a strong connection with formal semantics), and which generates semantic dependency graphs (allowing it to be trained on a corpus). This graphical model provides a natural way to model logical inference, semantic composition, and context-dependent meanings, where Bayesian inference plays a crucial role. I demonstrate the feasibility of this approach by training a model on WikiWoods, a parsed version of the English Wikipedia, and evaluating it on three tasks. The results indicate that the model can learn information not captured by vector space models.
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Jakob, Max. "Mapping the Prague Dependency Treebank Annotation Scheme onto Robust Minimal Recursion Semantics." Master's thesis, 2010. http://www.nusl.cz/ntk/nusl-286253.

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This thesis investigates the correspondence between two semantic formalisms, namely the tectogrammatical layer of the Prague Dependency Treebank 2.0 (PDT) and Robust Minimal Recursion Semantics (RMRS). It is a rst attempt to relate the dependency based annotation scheme of PDT to a compositional semantics approach like RMRS. An iterative mapping algorithm that converts PDT trees into RMRS structures is developed that associates RMRSs to each node in the dependency tree. Therefore, composition rules are formulated and the complex relation between dependency in PDT and semantic heads in RMRS is analyzed in detail. It turns out that structure and dependencies, morphological categories and some coreferences can be preserved in the target structures. Furthermore, valency and free modi cations are distinguished using the valency dictionary of PDT as an additional resource. The evaluation result of 81% recall shows that systematically correct underspeci ed target structures can be obtained by a rule-based mapping approach, which is an indicator that RMRS is capable of representing Czech data. This nding is novel as Czech, with its free word order and rich morphology, is typologically di erent from language that used RMRS thus far.
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Conference papers on the topic "Robust Minimal Recursion Semantic"

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Copestake, Ann. "Semantic composition with (robust) minimal recursion semantics." In the Workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2007. http://dx.doi.org/10.3115/1608912.1608925.

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Dridan, Rebecca, and Francis Bond. "Sentence comparison using robust minimal recursion semantics and an ontology." In the Workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2006. http://dx.doi.org/10.3115/1641976.1641982.

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Lien, Elisabeth, and Milen Kouylekov. "UIO-Lien: Entailment Recognition using Minimal Recursion Semantics." In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014). Stroudsburg, PA, USA: Association for Computational Linguistics, 2014. http://dx.doi.org/10.3115/v1/s14-2125.

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Jahanifar, Mostafa, Neda Zamani Tajeddin, Navid Alemi Koohbanani, and Nasir Rajpoot. "Robust Interactive Semantic Segmentation of Pathology Images with Minimal User Input." In 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). IEEE, 2021. http://dx.doi.org/10.1109/iccvw54120.2021.00081.

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