# Special Issue on \"Natural Language Processing: Emerging Neural Approaches and Applications\"
###### Abstract
**Conversational systems or interfaces and question answering:** The authors in [21] proposed the best practices for question classification in different languages using convolutional neural networks (CNNs), finding the optimal settings depending on the language and validating their transferability. The authors in [22] addressed the time-consuming development of manual user simulator policy and introduced a multi-agent dialogue model, where an end-to-end dialogue manager and a user simulator are optimized simultaneously for dialogue management by cooperative multi-agent reinforcement learning. Moreover, in [23], the authors proposed a Medical Instructed Real-time Assistant (MIRA) that listens to the user's chief complaint and predicts a specific disease, thus referring the user to a nearby appropriate medical specialist. Furthermore, in [24], the authors presented a multi-turn chatbot model in which the preceding utterances are exploited in response generation by using different weights. **Sentiment analysis, emotion detection, and opinion mining:** The study in [25] investigated a comparison of various DL models used to identify the toxic comments in Internet discussions. Moreover, in [26], the authors proposed a novel hybrid model XGA (namely an XLNet-based bidirectional gated recurrent unit (BiGRU) network with an attention mechanism) for Cantonese rumor detection on Twitter, taking advantage of both semantic and sentiment features for detection. Furthermore, the authors of [27] proposed an intensive study regarding a domain-independent classification model for sentiment analysis using neural models, showing high performance when using different evaluation metrics compared with the state-of-the-art results. Another study in [28] tested different approaches for handling long documents and proposed a novel technique for sentiment enrichment of the Bidirectional Encoder Representations from Transformers (BERT) model as an intermediate training step. In [29], [PERSON] et al. proposed an embedding approach that is designed to capture the polarity issue for sentiment analysis. **Document analysis, information extraction, and text mining:** In [30], [PERSON] et al. evaluated the combination of different types of embedding features in a bidirectional long short-term memory (Bi-LSTM) conditional random field (CRF) model for named entity recognition (NER). The authors in [31] investigated the transferability of the features from an open information extraction (OIE) domain to another and applied the approach for relation extraction (RE). The authors in [32] proposed a rule-based approach for text document classification. The study in [33] proposed an RE model based on a dual pointer network with a multi-head attention mechanism to address the association of multiple entities in a sentence according to various relations. The work in [34] investigated an RE method to solve the possible overlapping among multiple relational triples contained in a sentence. Another topic was introduced by the authors of [35], who introduced a novel hybrid model of extractive-abstractive text summarization to combine BERT word embedding with reinforcement learning. Two contributions to this special issue are focused on medical information extraction. The authors in [36] compared different architectures of DL models, including CNNs, LSTM, and hybrid models. Furthermore, they proposed a hybrid architecture for protein-protein interaction extraction from the biomedical literature. The authors in [37] developed a multitask attention-based Bi-LSTM-CRF model with pre-trained embeddings from language models (ELMo) in order to achieve improved performance in clinical NER. **Search and information retrieval:** In [38], [PERSON] et al. adapted language modeling-based methods for sentence retrieval to test the partial matching of terms through combining sentence retrieval with sequence similarity. This method allows for matching words that are similar but not identical.