Teanga: a linked data based platform for natural language processing

In this paper, we describe Teanga, a linked data based platform for natural language processing (NLP). Teanga enables the use of many NLP services from a single interface, whether the need was to use a single service or multiple services in a pipeline. Teanga focuses on the problem of NLP services interoperability by using linked data to define the types of services input and output. Teanga s strengths include being easy to install and run, easy to use, able to run multiple NLP tasks from one interface and helping users to build a pipeline of tasks through a graphical user interface

OTTO -Ontology Translation System

Abstract. To enable knowledge access across languages, ontologies that are often represented only in English, need to be translated into different languages. For this reason, we present OTTO, an OnTology TranslatiOn System, which enhances ontologies with multilingual information. Rather a different task than the classic document translation, ontology label translation faces highly specific vocabulary and lack contextual information. Therefore, OTTO takes advantage of the semantic information of the ontology to improve the translation of labels

OTTO - ontology translation system

To enable knowledge access across languages, ontologies that are often represented only in English, need to be translated into different languages. For this reason, we present OTTO, an OnTology TranslatiOn System, which enhances ontologies with multilingual information. Rather a different task than the classic document translation, ontology label translation faces highly specific vocabulary and lack contextual information. Therefore, OTTO takes advantage of the semantic information of the ontology to improve the translation of labels.This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289.non-peer-reviewe

MixedEmotions: An open-source toolbox for multi-modal emotion analysis

Recently, there is an increasing tendency to embed the functionality of recognizing emotions from the user generated contents, to infer richer profile about the users or contents, that can be used for various automated systems such as call-center operations, recommendations, and assistive technologies. However, to date, adding this functionality was a tedious, costly, and time consuming effort, and one should look for different tools that suits one's needs, and should provide different interfaces to use those tools. The MixedEmotions toolbox leverages the need for such functionalities by providing tools for text, audio, video, and linked data processing within an easily integrable plug-and-play platform. These functionalities include: (i) for text processing: emotion and sentiment recognition, (ii) for audio processing: emotion, age, and gender recognition, (iii) for video processing: face detection and tracking, emotion recognition, facial landmark localization, head pose estimation, face alignment, and body pose estimation, and (iv) for linked data: knowledge graph. Moreover, the MixedEmotions Toolbox is open-source and free. In this article, we present this toolbox in the context of the existing landscape, and provide a range of detailed benchmarks on standardized test-beds showing its state-of-the-art performance. Furthermore, three real-world use-cases show its effectiveness, namely emotion-driven smart TV, call center monitoring, and brand reputation analysis.peer-reviewe