This paper presents two systems for textual entailment, both employing decision trees as a supervised learning algorithm. The first one is based primarily on the concept of lexical overlap, considering a bag of words similarity overlap measure to form a mapping of terms in the hypothesis to the source text. The second system is a lexicosemantic matching between the text and the hypothesis that attempts an alignment between chunks in the hypothesis and chunks in the text, and a representation of the text and hypothesis as two dependency graphs. Their performances are compared and their positive and negative aspects are analyzed.

In this paper we describe a coreference resolution method that employs a classification and a clusterization phase. In a novel way, the clusterization is produced as a graph cutting algorithm, in which nodes of the graph correspond to the mentions of the text, whereas the edges of the graph constitute the confidences derived from the coreference classification. In experiments, the graph cutting algorithm for coreference resolution, called BESTCUT, achieves state-of-the-art performance.

The present article analyses the issue of subtitling for the d/Deaf and hard-of-hearing audience in Romania, or more accurately put, the need for SDH services and the (postponed) implementation (on a large scale) of such projects that would pursue audience accessibility while following national and international regulations. At the same time, we detail on a selection of specific features of SDH, in both intralingual and interlingual parametres of television services.

Internet of Things (IoT) technologies have started to impact society as a whole and have become a key enabler for sustainable development. The purpose of this paper is to examine the connection between IoT and sustainability providing a critical reflection on current literature. The research methodology consists of a literature review in the research field of IoT technologies and their beneficial impact in various sectors, such as sustainable urban development or farming. The aim is to provide an in-depth, detailed analysis on the various ways in which IoT can help with current global environmental issues and achieve sustainable development. The paper highlights the importance of IoT technologies in protecting the environment and emphasizes the need for all stakeholders to adapt such technologies nowadays and in the future. The main value of the current paper is to help enrich the literature on IoT technologies, sustainable development, and environmental protection. The Internet of Things (IoT) is believed to be one of the enabling paradigms of sustainable digital transformation and environmental protection (Salam, 2019). IoT is emerging as a powerful enabler in many application domains, such as water and energy management, environmental monitoring, health, smart cities, smart industry and supply chain management. This paper contributes to academic literature and business by revealing a possible link between sustainability and Internet of Things. This subject sparks interest among both marketers and researchers.

In this paper we present a semantic architecture that was employed for processing two different SemEval 2007 tasks: Task 4 (Classification of Semantic Relations between Nominals) and Task 8 (Metonymy Resolution). The architecture uses multiple forms of syntactic, lexical, and semantic information to inform a classification-based approach that generates a different model for each machine learning algorithm that implements the classification. We used decision trees, decision rules, logistic regression and lazy classifiers. A voting module selects the best performing module for each task evaluated in SemEval 2007. The paper details the results obtained when using the semantic architecture.