Recent developments in large language models (LLMs) have been accompanied by rapidly growing public interest in natural language processing (NLP). This attention is reflected by major news venues, which sometimes invite NLP researchers to share their knowledge and views with a wide audience. Recognizing the opportunities of the present, for both the research field and for individual researchers, this paper shares recommendations for communicating with a general audience about the capabilities and limitations of NLP. These recommendations cover three themes: vague terminology as an obstacle to public understanding, unreasonable expectations as obstacles to sustainable growth, and ethical failures as obstacles to continued support. Published NLP research and popular news coverage are cited to illustrate these themes with examples. The recommendations promote effective, transparent communication with the general public about NLP, in order to strengthen public understanding and encourage support for research.

Abstract: Entity and relationship extraction is a crucial component in natural language processing tasks such as knowledge graph construction, question answering system design, and semantic analysis. Most of the information of the Yishui school of traditional Chinese Medicine (TCM) is stored in the form of unstructured classical Chinese text. The key information extraction of TCM texts plays an important role in mining and studying the academic schools of TCM. In order to solve these problems efficiently using artificial intelligence methods, this study constructs a word segmentation and entity relationship extraction model based on conditional random fields under the framework of natural language processing technology to identify and extract the entity relationship of traditional Chinese medicine texts, and uses the common weighting technology of TF-IDF information retrieval and data mining to extract important key entity information in different ancient books. The dependency syntactic parser based on neural network is used to analyze the grammatical relationship between entities in each ancient book article, and it is represented as a tree structure visualization, which lays the foundation for the next construction of the knowledge graph of Yishui school and the use of artificial intelligence methods to carry out the research of TCM academic schools. Key words: Natural language processing; Knowledge graph; Yi Shui school; Syntactic analysis; Traditional Chinese Medicine; 1 Introduction In the era of artificial intelligence and big data technology, the mining and utilization of ancient Chinese medicine literature knowledge is one of the important basic tasks for the inheritance and innovation and development of traditional Chinese medicine.

Recent advances in deep learning methods for natural language processing (NLP) have created new business opportunities and made NLP research critical for industry development. As one of the big players in the field of NLP, together with governments and universities, it is important to track the influence of industry on research. In this study, we seek to quantify and characterize industry presence in the NLP community over time. Using a corpus with comprehensive metadata of 78,187 NLP publications and 701 resumes of NLP publication authors, we explore the industry presence in the field since the early 90s. We find that industry presence among NLP authors has been steady before a steep increase over the past five years (180% growth from 2017 to 2022). A few companies account for most of the publications and provide funding to academic researchers through grants and internships. Our study shows that the presence and impact of the industry on natural language processing research are significant and fast-growing. This work calls for increased transparency of industry influence in the field.

Advancements in natural language processing (NLP) enable computers to understand what humans say and help people communicate through tools like machine translation, voice-controlled assistants and chatbots.

With all of the massive and relentless advancements of natural language processing recently, keeping up with research breakthroughs and SOTA practices can be fraught with challenges. Where to find papers, which papers present which ideas, tracking down code that goes along with papers, these are all very real struggles. What if there was a single spot you could go to get the jump on all of these different activities, and come away with everything you need to keep up with the NLP Joneses? If you haven't heard, Ricky Costa, CEO at Quantum Stat, very recently announced the launch of The NLP Index. Ricky describes the NLP index as "a new asset in NLP code discovery," and goes on to say: It has over 3,000 code repositories and I've already created a nice side bar with some of the most important topics in NLP today!