Developing Semantic Rich Internet Applications with the Sm4RIA Extension for OIDE

This paper introduces the Sm4RIA Extension for OIDE, which implements the Sm4RIA approach in OIDE (OOH4RIA Integrated Development Environment). The application, based on the Eclipse framework, supports the design of the Sm4RIA models as well as the model-to-model and model-to-text transformation processes that facilitate the generation of Semantic Rich Internet Applications, i.e., RIA applications capable of sharing data as Linked data and consuming external data from other sources in the same manner. Moreover, the application implements mechanisms for the creation of RIA interfaces from ontologies and the automatic generation of administration interfaces for a previously design application.

The role of statistical and semantic features in single-document extractive summarization

This paper reports on the further results of the ongoing research analyzing the impact of a range of commonly used statistical and semantic features in the context of extractive text summarization. The features experimented with include word frequency, inverse sentence and term frequencies, stopwords filtering, word senses, resolved anaphora and textual entailment. The obtained results demonstrate the relative importance of each feature and the limitations of the tools available. It has been shown that the inverse sentence frequency combined with the term frequency yields almost the same results as the latter combined with stopwords filtering that in its turn proved to be a highly competitive baseline. To improve the suboptimal results of anaphora resolution, the system was extended with the second anaphora resolution module. The present paper also describes the first attempts of the internal document data representation.

Applying semantic knowledge to the automatic processing of temporal expressions and events in natural language

This paper addresses the problem of the automatic recognition and classification of temporal expressions and events in human language. Efficacy in these tasks is crucial if the broader task of temporal information processing is to be successfully performed. We analyze whether the application of semantic knowledge to these tasks improves the performance of current approaches. We therefore present and evaluate a data-driven approach as part of a system: TIPSem. Our approach uses lexical semantics and semantic roles as additional information to extend classical approaches which are principally based on morphosyntax. The results obtained for English show that semantic knowledge aids in temporal expression and event recognition, achieving an error reduction of 59% and 21%, while in classification the contribution is limited. From the analysis of the results it may be concluded that the application of semantic knowledge leads to more general models and aids in the recognition of temporal entities that are ambiguous at shallower language analysis levels. We also discovered that lexical semantics and semantic roles have complementary advantages, and that it is useful to combine them. Finally, we carried out the same analysis for Spanish. The results obtained show comparable advantages. This supports the hypothesis that applying the proposed semantic knowledge may be useful for different languages.

Syntactic and Semantic Relationships in Models of Complex Systems: An Ecological Case

In this paper, the authors extend and generalize the methodology based on the dynamics of systems with the use of differential equations as equations of state, allowing that first order transformed functions not only apply to the primitive or original variables, but also doing so to more complex expressions derived from them, and extending the rules that determine the generation of transformed superior to zero order (variable or primitive). Also, it is demonstrated that for all models of complex reality, there exists a complex model from the syntactic and semantic point of view. The theory is exemplified with a concrete model: MARIOLA model.

Semantic localization in the PCL library

The semantic localization problem in robotics consists in determining the place where a robot is located by means of semantic categories. The problem is usually addressed as a supervised classification process, where input data correspond to robot perceptions while classes to semantic categories, like kitchen or corridor. In this paper we propose a framework, implemented in the PCL library, which provides a set of valuable tools to easily develop and evaluate semantic localization systems. The implementation includes the generation of 3D global descriptors following a Bag-of-Words approach. This allows the generation of fixed-dimensionality descriptors from any type of keypoint detector and feature extractor combinations. The framework has been designed, structured and implemented to be easily extended with different keypoint detectors, feature extractors as well as classification models. The proposed framework has also been used to evaluate the performance of a set of already implemented descriptors, when used as input for a specific semantic localization system. The obtained results are discussed paying special attention to the internal parameters of the BoW descriptor generation process. Moreover, we also review the combination of some keypoint detectors with different 3D descriptor generation techniques.

Semantic change and intersubjectification: The origin of reprise evidential conditional in Old Catalan

The reprise evidential conditional (REC) is nowadays not very usual in Catalan: it is restricted to journalistic language and to some very formal genres (such as academic or legal language), it is not present in spontaneous discourse. On the one hand, it has been described among the rather new modality values of the conditional. On the other, the normative tradition tended to reject it for being a gallicism, or to describe it as an unsuitable neologism. Thanks to the extraction from text corpora, we surprisingly find this REC in Catalan from the beginning of the fourteenth century to the contemporary age, with semantic and pragmatic nuances and different evidence of grammaticalization. Due to the current interest in evidentiality, the REC has been widely studied in French, Italian and Portuguese, focusing mainly on its contemporary uses and not so intensively on the diachronic process that could explain the origin of this value. In line with this research, that we initiated studying the epistemic and evidential future in Catalan, our aim is to describe: a) the pragmatic context that could have been the initial point of the REC in the thirteenth century, before we find indisputable attestations of this use; b) the path of semantic change followed by the conditional from a ‘future in the past’ tense to the acquisition of epistemic and evidential values; and c) the role played by invited inferences, subjectification and intersubjectification in this change.

Semantic change

Presentation of the volume.

A semantic framework for textual data enrichment

In this work we present a semantic framework suitable of being used as support tool for recommender systems. Our purpose is to use the semantic information provided by a set of integrated resources to enrich texts by conducting different NLP tasks: WSD, domain classification, semantic similarities and sentiment analysis. After obtaining the textual semantic enrichment we would be able to recommend similar content or even to rate texts according to different dimensions. First of all, we describe the main characteristics of the semantic integrated resources with an exhaustive evaluation. Next, we demonstrate the usefulness of our resource in different NLP tasks and campaigns. Moreover, we present a combination of different NLP approaches that provide enough knowledge for being used as support tool for recommender systems. Finally, we illustrate a case of study with information related to movies and TV series to demonstrate that our framework works properly.

JAABA: interactive machine learning for automatic annotation of animal behavior

We present a machine learning-based system for automatically computing interpretable, quantitative measures of animal behavior. Through our interactive system, users encode their intuition about behavior by annotating a small set of video frames. These manual labels are converted into classifiers that can automatically annotate behaviors in screen-scale data sets. Our general-purpose system can create a variety of accurate individual and social behavior classifiers for different organisms, including mice and adult and larval Drosophila.

Advances in Research on Semantic Roles

Especially in functional-typological linguistics, semantic roles have been studied thoroughly, because they constitute a good starting point for any study on argument marking due to their semantically defined nature. However, the very concept of semantic roles is far from being without problems, and there is still no consensus on how the roles are best defined. In this volume, the notion will be discussed from novel perspectives with the aim of providing new insights into our understanding of semantic roles. Two of the papers deal with semantic role clusters, one with semantic roles in verbless constructions, one with diachrony of semantic roles and two with individual semantic roles that have not been studied in too much detail in previous studies. The book may not offer answers to all questions the readers may have, but at least it raises interesting further questions relevant to arriving at a better understanding of semantic roles.

The conditional sales acts : being an annotation of the Act Respecting Conditional Sales of Chattels (R. S. O. (1897) Cap. 149) and amendments thereto : to which is appended a complete set of forms /

Includes index.

A semantic definition system (a preliminary description) /

Bibliography: p. 87.