Formal semantics offers a complete and rigorous definition of a language. It is important to define different semantic models for a language and different models serve different purposes. Building equivalence between different semantic models of a language strengthen its formal foundation. This paper shows the derivation of denotational semantics from operational semantics of the language cCSP. The aim is to show the correspondence between operational and trace semantics. We extract traces from operational rules and use induction over traces to show the correspondence between the two semantics of cCSP.
A similar problem was also investigated in @cite_4 , where a metric structure was employed to relate the operational and denotational models of a given language. In order to relate the semantic models it was proved that the two models coincide. The denotational models were extended and structural induction was applied over the terms of the language to relate the semantic models.
@cite_4
1991224384 2137132216 2142863987 2153419563
Our focus is on the semantics of programming and specification languages. Over the years, different approaches to give semantics to these languages have been put forward. We restrict ourselves to the operational and the denotational approach, two main streams in the field of semantics. Two notions which play an important role in this paper are (non)determinism and (non)termination. Nondeterminism arises naturally in concurrent languages and it is a key concept in specification languages. Nontermination is usually caused by recursive constructs which are crucial in programming. The operational models are based on labelled transition systems. The definition of these systems is guided by the structure of the language. Metric spaces are an essential ingredient of our denotational models. We exploit the metric structure to model recursive constructs and to define operators on infinite entities. Furthermore, we also employ the metric structure to relate operational and denotational models for a given language. On the basis of four toy languages, we develop some general theory for defining operational and denotational semantic models and for relating them. This theory is applicable to a wide variety of languages. We start with a very simple deterministic and terminating imperative programming language. By adding the recursive while statement, we obtain a deterministic and nonterminating language. Next, we augment the language with the parallel composition resulting in a bounded nondeterministic and nonterminating language. Finally, we add some timed constructs. We obtain an unbounded nondeterministic and nonterminating specification language. Current investigations in data-driven models of parsing have shifted from purely syntactic analysis to richer semantic representations, showing that the successful recovery of the meaning of text requires structured analyses of both its grammar and its semantics. In this article, we report on a joint generative history-based model to predict the most likely derivation of a dependency parser for both syntactic and semantic dependencies, in multiple languages. Because these two dependency structures are not isomorphic, we propose a weak synchronization at the level of meaningful subsequences of the two derivations. These synchronized subsequences encompass decisions about the left side of each individual word. We also propose novel derivations for semantic dependency structures, which are appropriate for the relatively unconstrained nature of these graphs. To train a joint model of these synchronized derivations, we make use of a latent variable model of parsing, the Incremental Sigmoid Belief Network ISBN architecture. This architecture induces latent feature representations of the derivations, which are used to discover correlations both within and between the two derivations, providing the first application of ISBNs to a multi-task learning problem. This joint model achieves competitive performance on both syntactic and semantic dependency parsing for several languages. Because of the general nature of the approach, this extension of the ISBN architecture to weakly synchronized syntactic-semantic derivations is also an exemplification of its applicability to other problems where two independent, but related, representations are being learned. Metric learning aims at finding a distance that approximates a task-specific notion of semantic similarity. Typically, a Mahalanobis distance is learned from pairs of data labeled as being semantically similar or not. In this paper, we learn such metrics in a weakly supervised setting where "bags" of instances are labeled with "bags" of labels. We formulate the problem as a multiple instance learning (MIL) problem over pairs of bags. If two bags share at least one label, we label the pair positive, and negative otherwise. We propose to learn a metric using those labeled pairs of bags, leading to MildML, for multiple instance logistic discriminant metric learning. MildML iterates between updates of the metric and selection of putative positive pairs of examples from positive pairs of bags. To evaluate our approach, we introduce a large and challenging data set, Labeled Yahoo! News, which we have manually annotated and contains 31147 detected faces of 5873 different people in 20071 images. We group the faces detected in an image into a bag, and group the names detected in the caption into a corresponding set of labels. When the labels come from manual annotation, we find that MildML using the bag-level annotation performs as well as fully supervised metric learning using instance-level annotation. We also consider performance in the case of automatically extracted labels for the bags, where some of the bag labels do not correspond to any example in the bag. In this case MildML works substantially better than relying on noisy instance-level annotations derived from the bag-level annotation by resolving face-name associations in images with their captions. We introduce an approach to learn discriminative visual representations while exploiting external semantic knowledge about object category relationships. Given a hierarchical taxonomy that captures semantic similarity between the objects, we learn a corresponding tree of metrics (ToM). In this tree, we have one metric for each non-leaf node of the object hierarchy, and each metric is responsible for discriminating among its immediate subcategory children. Specifically, a Mahalanobis metric learned for a given node must satisfy the appropriate (dis)similarity constraints generated only among its subtree members' training instances. To further exploit the semantics, we introduce a novel regularizer coupling the metrics that prefers a sparse disjoint set of features to be selected for each metric relative to its ancestor (supercategory) nodes' metrics. Intuitively, this reflects that visual cues most useful to distinguish the generic classes (e.g., feline vs. canine) should be different than those cues most useful to distinguish their component fine-grained classes (e.g., Persian cat vs. Siamese cat). We validate our approach with multiple image datasets using the WordNet taxonomy, show its advantages over alternative metric learning approaches, and analyze the meaning of attribute features selected by our algorithm.