146,413 research outputs found

    A Unified Approach for Representing Structurally-Complex Models in SBML Level 3

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    The aim of this document is to explore a unified approach to handling several of the proposed extensions to the SBML Level 3 Core specification. The approach is illustrated with reference to Simile, a modelling environment which appears to have most of the capabilities of the various SBML Level 3 package proposals which deal with model structure. Simile (http://www.simulistics.com) is a visual modelling environment for continuous systems modelling which includes the ability to handle complex disaggregation of model structure, by allowing the modeller to specify classes of object and the relationships between them.

The note is organised around the 6 packages listed on the SBML Level 3 Proposals web page (http://sbml.org/Community/Wiki/SBML_Level_3_Proposals) which deal with model structure, namely comp, arrays, spatial, geom, dyn and multi. For each one, I consider how the requirements which motivated the package can be handled using Simile's unified approach. Although Simile has a declarative model-representation language (in both Prolog and XML syntax), I use Simile diagrams and equation syntax throughout, since this is more compact and readable than large chunks of XML.

The conclusion is that Simile can indeed meet most of the requirements of these various packages, using a generic set of constructs - basically, the multiple-instance submodel, the concept of a relationship (association) between submodels, and array variables. This suggests the possibility of having a single SBML Level 3 extension package similar to the Simile data model, rather than a series of separate packages. Such an approach has a number of potential advantages and disadvantages compared with having the current set of discrete packages: these are discussed in this paper

    A hybrid representation based simile component extraction

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    Simile, a special type of metaphor, can help people to express their ideas more clearly. Simile component extraction is to extract tenors and vehicles from sentences. This task has a realistic significance since it is useful for building cognitive knowledge base. With the development of deep neural networks, researchers begin to apply neural models to component extraction. Simile components should be in cross-domain. According to our observations, words in cross-domain always have different concepts. Thus, concept is important when identifying whether two words are simile components or not. However, existing models do not integrate concept into their models. It is difficult for these models to identify the concept of a word. What’s more, corpus about simile component extraction is limited. There are a number of rare words or unseen words, and the representations of these words are always not proper enough. Exiting models can hardly extract simile components accurately when there are low-frequency words in sentences. To solve these problems, we propose a hybrid representation-based component extraction (HRCE) model. Each word in HRCE is represented in three different levels: word level, concept level and character level. Concept representations (representations in concept level) can help HRCE to identify the words in cross-domain more accurately. Moreover, with the help of character representations (representations in character levels), HRCE can represent the meaning of a word more properly since words are consisted of characters and these characters can partly represent the meaning of words. We conduct experiments to compare the performance between HRCE and existing models. The experiment results show that HRCE significantly outperforms current models

    The Analysis of the Elements of Poetry in a Poem Sunflower by Pam Stewart

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    Penelitian ini bertujuan untuk menggambarkan tentang unsur-unsur intrinsik di dalam puisi yang berjudul Sunflower karangan Pam Stewart. Pengarang sengaja memakai unsur intrinsik puisi seperti unsur denotasi dan konotasi, citraan, dan juga majas sebagai daya tarik di dalam puisi ini.Unsur citraan yang dominan di dalam puisi ini adalah citra penglihatan, dan citra gerakan. Selain itu pengarang juga memakai dua buah majas yaitu simile dan personifikasi di dalam puisi ini agar pembaca dapat merasakan puisi tersebut menjadi lebih hidup. Hal yang membuat puisi ini menarik adalah pemakaian majas simile dengan bunga matahari yang ternyata memiliki sebuah sisi gelap yang disembunyikan.Hasil penelitian menunjukkan bahwa Pam Stewart sengaja memakai unsur citraan dan majas simile untuk memberikan kesan misterius dan indah pada puisi bunga matahari ini

    Simile

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    SIMILES IN GEORGE BAMBER’S RETURN TO DUST

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    The objective of this study was to investigate similes in George Bamber's Return to Dust. This study used descriptive qualitative in order to explore the kinds of simile in the dialogue utterance of characters in such drama. The results found that the most frequent kind was descriptive simile 40%, close simile 35%, illustrative simile 15%, open simile 5% and illuminative simile 5%. Descriptive simile occurs on a character named James and Daphne as a scientist while describing the things in their work of experiments. A closed simile occurs in James's utterance when comparing the definition of one object to the tenor. Illustrative similes were also found in James's utterance while describing the process of experiment in science. Open and illuminative similes were found while explaining the quality of the condition and comparing the character to scientific experiments. Similes were used in the drama Return to Dust in describing something in order to create a lasting impression in the readers’ minds. Keywords: simile, drama, return to dus

    Neural Simile Recognition with Cyclic Multitask Learning and Local Attention

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    Simile recognition is to detect simile sentences and to extract simile components, i.e., tenors and vehicles. It involves two subtasks: {\it simile sentence classification} and {\it simile component extraction}. Recent work has shown that standard multitask learning is effective for Chinese simile recognition, but it is still uncertain whether the mutual effects between the subtasks have been well captured by simple parameter sharing. We propose a novel cyclic multitask learning framework for neural simile recognition, which stacks the subtasks and makes them into a loop by connecting the last to the first. It iteratively performs each subtask, taking the outputs of the previous subtask as additional inputs to the current one, so that the interdependence between the subtasks can be better explored. Extensive experiments show that our framework significantly outperforms the current state-of-the-art model and our carefully designed baselines, and the gains are still remarkable using BERT.Comment: AAAI 202

    I-WAS: a Data Augmentation Method with GPT-2 for Simile Detection

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    Simile detection is a valuable task for many natural language processing (NLP)-based applications, particularly in the field of literature. However, existing research on simile detection often relies on corpora that are limited in size and do not adequately represent the full range of simile forms. To address this issue, we propose a simile data augmentation method based on \textbf{W}ord replacement And Sentence completion using the GPT-2 language model. Our iterative process called I-WAS, is designed to improve the quality of the augmented sentences. To better evaluate the performance of our method in real-world applications, we have compiled a corpus containing a more diverse set of simile forms for experimentation. Our experimental results demonstrate the effectiveness of our proposed data augmentation method for simile detection.Comment: 15 pages, 1 figur

    Negative Simile

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    'Like the Dickens': Charles Dickens and Simile

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    This thesis proposes that Dickens’s prolific and distinctive use of simile is essential to his style. It takes a cross-disciplinary approach, using stylistic analysis and literary criticism to identify the features of simile and interpret the effects of Dickens’s use of simile. It builds on previous linguistic scholarship to expand the definition of simile to include any explicit linguistic structure that creates direct figurative comparison. ‘Similic’ language, or structures of direct figurative comparison, underpins much of Dickens’s unique figurative style. The language of analogy, and similes in particular, was commonly used by Dickens’s contemporaries, and he was thus not unusual in the number of similes he employed. However, ‘Dickensian simile’ is highly unconventional in its remarkably self-conscious and peculiar character: his comparisons often manipulate the ordinarily clarifying and explanatory aspect of a similic comparison to create exaggerated, absurd, or bizarre imagery to serve his narrative purposes. The chronological approach of the thesis shows how Dickens’s use of simile developed throughout his career. From Sketches by ‘Boz’ to Martin Chuzzlewit, simile can be identified as emphasising Dickens’s authorial flair; it is typically hyperbolic and self-conscious, often with comical effect. From Dombey and Son onwards, Dickens uses simile for increasingly subtle narrative strategies of characterisation. Even in his last, unfinished novel, The Mystery of Edwin Drood, Dickens was still experimenting with simile. From a discussion of his transition from journalistic reporting to writing fiction, to a discussion of how his similic style in his letters works to create the image of himself as the ‘Inimitable,’ this thesis shows how simile is a significant authorial signature of Dickens

    SIMILE USED IN LENKA’S SONG EVERYTHING AT ONCE

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    The research was related to figurative language but the researchers focused on simile, aimed at identifying the dominant type of simile used based on the theory put forward by Kennedy and Giola. Simile is a comparison of one thing to another which is always listed because the words "like" or as are used. Simile is also commonly used in song lyrics and this is referred to as the object of the research, that is the song lyrics taken by Lenka’s song Everything at Once. The researchers take this song because all the lyrics are full of similes, from the beginning to the end of the song. Qualitative content analysis as the use of replicable and valid methods to make specific inferences from the text to other circumstances or properties of the source under the Descriptive Qualitative Approach, pioneered by Krippendorff is applied here. The results show that there are two types of simile in this song and the dominant types used in this song is close simile
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