With the rise of human-machine interactions, it has become necessary for machines to better understand humans in order to respond appropriately. Hence, in order to increase communication and interaction, it would be ideal for machines to automatically detect human emotions. Speech Emotion Recognition (SER) has been a focus of a lot of studies in the past few years. However, they can be considered poor in accuracy and must be improved. In our work, we propose a new loss function that aims to encode speeches instead of classifying them directly as the majority of the existing models do. The encoding will be done in a way that utterances with the same labels would have similar encodings. The encoded speeches were tested on two datasets and we managed to get 88.19% accuracy with the RAVDESS (Ryerson Audiovisual Database of Emotional Speech and Song) dataset and 91.66% accuracy with the RML (Ryerson Multimedia Research Lab) dataset.

Arabic WordNet (AWN) represents one of the best-known lexical resources for the Arabic language.However, it contains various issues that affect its use in different Natural Language Processing (NLP) applications.Due to resources deficiency, the update of Arabic WordNet requires much effort.There have only been only two updates it was first published in 2006.The most significant of those being in 2013, which represented a significant development in the usability and coverage of Arabic WordNet.This paper provides a study case on the updates of the Arabic Word-Net and the development of its contents.More precisely, we present the new content in terms of relations that have been added to the extended version of Arabic WordNet.We also validate and evaluate its contents at different levels.We use its different versions in a Word Sense Disambiguation system.Finally, we compare the results and evaluate them.Results show that newly added semantic relations can improve the performance of a Word Sense Disambiguation system.

When derivational relations deficiency exists in a wordnet, such as the Arabic Word-Net, it makes it very difficult to exploit in the natural language processing community.Such deficiency is raised when many wordnets follow the same development path of Princeton WordNet.A rulebased approach for Arabic derivational relations is proposed in this paper to deal with this deficiency.The proposed approach is explained step by step.It involves the gathering of lexical entries that share the same root, into a bag of words.Rules are then used to affect the appropriate derivational relations, i.e. to relate existing words in the AWN, involving partof-speech switch.The method is implemented using Java.Manual verification by a lexicographer takes place to ensure good results.The described approach gave good results.It could be useful for other morphologically complex languages as well.

Named Entity Recognition plays an important role in locating and classifying atomic elements into predefined categories such as person names, locations, organizations, expression of times, temporal expressions etc. Several approaches with rule based and machine learning based techniques have been applied on English and some other Latin languages successfully. Arabic has a complex and rich morphology, which makes the named entities recognition a challenging process. In this paper we propose our hybrid NER system that applies conditional random fields (CRF), bilingual NE lexicon and grammar rules to the task of Named Entity Recognition in Arabic languages. The aim of our system is enhancing the overall performance of NER tasks. The empirical results indicate that the hybrid system outperforms the state-of-the-art of Arabic NER in terms of precision when applied to ANERcorp dataset, with f-measures 83.36 for Person, 89.58for Location, and 72.26 for Organization.

In this paper, the authors propose formalism for representing a knowledge base (KB) by network. The objective is to achieve a high coverage of this base. This type of network is similar to the semantic network with the difference that the arcs are quantified by a value indicating the semantic proximity between the concepts. This semantic proximity presents taxonomic relations, synonyms, and non-taxonomic relations (contextual relations). This latter are discovered based on the association rules model. This model is based on (i) indexing method (ii) the French lexical database EuroWordNet (EWNF) and (iii) the Apriori algorithm. The contextual relations are the latent relations buried in the KB, carried by the semantic context. Evaluating our representation formalism shows better result about 80% of coverage of the KB.