title: Identification and visualization of key topics in scientific publications with transformer-based language models and document clustering methods

authors: Weng, Min-Hsien

abstract: With the rapidly growing number of scientific publications, researchers face an increasing challenge of discovering the current research topics and methodologies in a scientific domain. This paper describes an unsupervised topic detection approach that utilizes the new development of transformer-based GPT-3 (Generative Pretrained Transformer 3) similarity embedding models and modern document clustering techniques. In total, 593 publication abstracts across urban study and machine learning domains were used as a case study to demonstrate the three phases of our approach. The iterative clustering phase uses the GPT-3 embeddings to represent the semantic meaning of abstracts and deploys the HDBSCAN (Hierarchical Density-based Spatial Clustering of Applications with Noise) clustering algorithm along with silhouette scores to group similar abstracts. The keyword extraction phase identifies candidate words from each abstract and selects keywords using the Maximal Marginal Relevance ranking algorithm. The keyword grouping phase produces the keyword groups to represent topics in each abstract cluster, again using GPT-3 embeddings, the HDBSCAN algorithm, and silhouette scores. The results are visualized in a web-based interactive tool that allows users to explore abstract clusters and examine the topics in each cluster through keyword grouping. Our unsupervised topic detection approach does not require labeled datasets for training and has the potential to be used in bibliometric analysis in a large collection of publications.

text: Identification and visualization of key topics in scientific publications with transformer-based language models and document clustering methods With the rapidly growing number of scientific publications, researchers face an increasing challenge of discovering the current research topics and methodologies in a scientific domain. This paper describes an unsupervised topic detection approach that utilizes the new development of transformer-based GPT-3 (Generative Pretrained Transformer 3) similarity embedding models and modern document clustering techniques. In total, 593 publication abstracts across urban study and machine learning domains were used as a case study to demonstrate the three phases of our approach. The iterative clustering phase uses the GPT-3 embeddings to represent the semantic meaning of abstracts and deploys the HDBSCAN (Hierarchical Density-based Spatial Clustering of Applications with Noise) clustering algorithm along with silhouette scores to group similar abstracts. The keyword extraction phase identifies candidate words from each abstract and selects keywords using the Maximal Marginal Relevance ranking algorithm. The keyword grouping phase produces the keyword groups to represent topics in each abstract cluster, again using GPT-3 embeddings, the HDBSCAN algorithm, and silhouette scores. The results are visualized in a web-based interactive tool that allows users to explore abstract clusters and examine the topics in each cluster through keyword grouping. Our unsupervised topic detection approach does not require labeled datasets for training and has the potential to be used in bibliometric analysis in a large collection of publications.

year: 2022-11-01

title: Structure theory for second order 2D superintegrable systems with 1-parameter potentials

authors: Kalnins, Ernie G.

subjects: maths

abstract: The structure theory for the quadratic algebra generated by first and second order constants of the motion for 2D second order superintegrable systems with nondegenerate (3-parameter) and or 2-parameter potentials is well understood, but the results for the strictly 1-parameter case have been incomplete. Here we work out this structure theory and prove that the quadratic algebra generated by first and second order constants of the motion for systems with 4 second order constants of the motion must close at order three with the functional relationship between the 4 generators of order four. We also show that every 1-parameter superintegrable system is Stäckel equivalent to a system on a constant curvature space.

text: Structure theory for second order 2D superintegrable systems with 1-parameter potentials The structure theory for the quadratic algebra generated by first and second order constants of the motion for 2D second order superintegrable systems with nondegenerate (3-parameter) and or 2-parameter potentials is well understood, but the results for the strictly 1-parameter case have been incomplete. Here we work out this structure theory and prove that the quadratic algebra generated by first and second order constants of the motion for systems with 4 second order constants of the motion must close at order three with the functional relationship between the 4 generators of order four. We also show that every 1-parameter superintegrable system is Stäckel equivalent to a system on a constant curvature space.

year: 2009

title: Kirihaehae

authors: Maxwell, Te Kahautu

abstract: A haka composed about moko. My grand aunt Tangiwai took the moko chin tattoo so when the man from Tauranga, Kōraurau Te Kani that she had been betrothed to arrived at Whitianga Bay on the East Coast of the Bay of Plenty, he would readily recognise her his intended bride to be. This haka was composed for Te Kapa Haka o Tūtawake. This composition will be performed at the Mātaatua Kapa Haka regional Competition in Ruatoki 2016.

text: Kirihaehae A haka composed about moko. My grand aunt Tangiwai took the moko chin tattoo so when the man from Tauranga, Kōraurau Te Kani that she had been betrothed to arrived at Whitianga Bay on the East Coast of the Bay of Plenty, he would readily recognise her his intended bride to be. This haka was composed for Te Kapa Haka o Tūtawake. This composition will be performed at the Mātaatua Kapa Haka regional Competition in Ruatoki 2016.

year: 2013