Data driven identification of international cutting edge science and technologies using SpaCy

Abstract

Difficulties in collecting, processing, and identifying massive data have slowed research on cutting-edge science and technology hotspots. Promoting these technologies will not be successful without an effective data-driven method to identify cutting-edge technologies. This paper proposes a data-driven model for identifying global cutting-edge science technologies based on SpaCy. In this model, we collected data released by 17 well-known American technology media websites from July 2019 to July 2020 using web crawling with Python. We combine graph-based neural network learning with active learning as the research method in this paper. Next, we introduced a ten-fold cross-check to train the model through machine learning with repeated experiments. The experimental results show that this model performed very well in entity recognition tasks with an F value of 98.11%. The model provides an information source for cutting-edge technology identification. It can promote innovations in cutting-edge technologies through its effective identification and tracking and explore more efficient scientific and technological research work modes.

Establishing an Optimal Online Phishing Detection Method: Evaluating Topological NLP Transformers on Text Message Data

“…SMS data cannot be adequately analysed if latent sentence relationships are not fully captured (Gormley et al, 2015). Components of NNs identified as important for short-textual modelling, for example sentencizers and dependency parsers, are implemented within spaCy (Hu et al, 2022). A RoBERTa-derived model is adapted for spaCy transformer implementation and is an extension of SBERT concepts.…”

“…Inherent edge approximations during topological inference generation must be implemented to avoid exceeding O(n 3 T) runtime. Implementations based on comparing specific topological sentence components (Hu et al, 2022) effectively limit exponential processing overheads. Honnibal and Johnson (2014) argue that lightweight transformer implementation is achieved by choosing appropriate within-model parameters.…”

Frontiers of policy and governance research in a smart city and artificial intelligence: an advanced review based on natural language processing

“…In the third step, spaCy, a word segmentation tool, was used for segmentation processing on the text. It is also currently the fastest and best method for deep learning from text and can be written in the programming language Python (Honnibal and Johnson, 2015;Hu et al, 2022). The executed commands included removing stop words and stemming to reduce the total number of unique words in the dictionary.…”