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Aiming at the problems of insufficient labelled samples and low-generalization performance in text classification tasks, this paper studies text classification problems under the condition of few labelled samples and proposes a few-shot short-text classification method (Meta-FCS) that combines the advantages of text semantic vector representation, meta-learning, fine-tuning and vector similarity measurement. The method not only effectively transfers the common features of different fields but also highlights the individual features of this field through fine-tuning. In addition, to facilitate the downstream text classification task, a deep language representation model is proposed. On this basis, the similarity between the query set and the class centroid of the support set is compared to determine the query set category. We evaluate the proposed method on a well-studied sentiment classification dataset, an entity-relationship classification dataset and an news topic dataset. The experimental results show that on these three datasets, the proposed method significantly outperforms the existing state-of-the-art approaches. It can thus be further suggested that the combination of deep language representation, episode training mechanism, and similarity measurement can be a promising solution for few-shot learning (FSL) of natural language processing (NLP) tasks. [ABSTRACT FROM AUTHOR]

This paper describes a language wrapper for the NetHack Learning Environment (NLE) [1]. The wrapper replaces the non-language observations and actions with comparable language versions. The NLE offers a grand challenge for AI research while MiniHack [2] extends this potential to more specific and configurable tasks. By providing a language interface, we can enable further research on language agents and directly connect language models to a versatile environment. [ABSTRACT FROM AUTHOR]

In the Big Data era, there is an increasing need to fully exploit and analyze the huge quantity of information available about health. Natural Language Processing (NLP) technologies can contribute by extracting relevant information from unstructured data contained in Electronic Health Records (EHR) such as clinical notes, patients' discharge summaries and radiology reports. The extracted information can help in health-related decision making processes. The Named Entity Recognition (NER) task, which detects important concepts in texts (e.g., diseases, symptoms, drugs, etc.), is crucial in the information extraction process yet has received little attention in languages other than English. In this work, we develop a deep learning-based NLP pipeline for biomedical entity extraction in Spanish clinical narratives. We explore the use of contextualized word embeddings, which incorporate context variation into word representations, to enhance named entity recognition in Spanish language clinical text, particularly of pharmacological substances, compounds, and proteins. Various combinations of word and sense embeddings were tested on the evaluation corpus of the PharmacoNER 2019 task, the Spanish Clinical Case Corpus (SPACCC). This data set consists of clinical case sections extracted from open access Spanish-language medical publications. Our study shows that our deep-learning-based system with domain-specific contextualized embeddings coupled with stacking of complementary embeddings yields superior performance over a system with integrated standard and general-domain word embeddings. With this system, we achieve performance competitive with the state-of-the-art.