# Special Issue on \"Natural Language Processing: Emerging Neural Approaches and Applications\"
###### Abstract  
Nowadays, systems based on artificial intelligence are being developed, leading to impressive achievements in a variety of complex cognitive tasks, matching or even beating humans [1, 2, 3, 4]. Natural language processing (NLP) is a field where the use of deep learning (DL) models in the last five years has allowed AI to advance toward human levels in translation and reading comprehension, as well as other real-world NLP applications, such as question answering and conversational systems, information retrieval, sentiment analysis, and recommender systems. However, due to the difficulties associated with natural language understanding and generation, which are human capabilities among the least understood by computer systems from a cognitive perspective, and despite the remarkable success of DL in different NLP tasks, this is still a field of research of increasing interest [5, 6, 7]. In order to improve DL methods, current models have been scaled up, but their complexity has grown toward directions assumed by empirical engineering solutions [8, 9, 10, 11]. Moreover, they are not applicable to languages without extensive datasets [12], and the lack of explainability inhibits further improvements [13]. This Special Issue highlights the most recent research being carried out in the NLP field to discuss these open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains. There are 30 contributions selected for this Special Issue representing progress and potential applications in the NLP area from original contributions of researchers with a broad expertise in various fields: NLP, cognitive science and psychology, artificial intelligence and neural networks, computational modeling and neuroscience covering the whole range of theoretical and practical aspects, technologies, and systems. This collection includes one review paper, which focuses on text corpus-based tourism big data mining [14]. [PERSON] et al. summarized and discussed different text representation strategies, text-based NLP techniques for topic extraction, text classification, sentiment analysis, and text clustering in the context of tourism text mining, as well as their applications in tourist profiling, destination image analysis, and market demand, among others. Their work also provides guidelines for constructing new tourism big data applications and outlines promising research areas in this field for the coming years. One letter is also included in this issue, employing evolutionary a neural architecture search for Korean grammaticality tasks [15]. Regarding the other 28 research papers, the following NLP areas are specifically addressed:  
**Natural language understanding, generation, and grounding:** In [16], Ontology-Fixer is presented, a web-based tool that supports a methodology to build, assess, andimprove the quality of Ontology Web Language (OWL) ontologies. Another paper [17] addresses the problem of paraphrase identification and presents an approach for leveraging contextual features with a neural-based learning model based on lexical, syntactic, and sentential encodings, incorporating relational graph convolutional networks (R-GCNs) to make use of different features from local contexts (e.g., word encoding, position encoding, and full dependency structures). In addition, in [18], the authors revisited the recurrent neural network (RNN) language model, achieving highly competitive results with the appropriate network structure and hyperparameters. **Universal language models:** In [19], [PERSON] and the co-author used a method recently proposed, called the causal feature extractor (CFE), for encoder-decoder models on different text processing tasks. The same authors applied this method to text normalization in [20], which is a ubiquitous problem that appears as the first step of many text-to-speech (TTS) systems.