# Exploring the relationship between team institutional composition and novelty in academic papers based on fine-grained knowledge entities

arXiv: https://arxiv.org/abs/2606.31058 · HTML: https://mlsanity.com/paper/2606.31058/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 49 references that are in the map.
- First posted on arXiv 30 Jun 2026.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Tradition and Innovation in Scientists' Research Strategies](https://mlsanity.com/paper/1302.6906/) (2013)
- [Continual Learning for Sentence Representations Using Conceptors](https://mlsanity.com/paper/1904.09187/) (2019)
- [Keyphrase Extraction from Scholarly Articles as Sequence Labeling using Contextualized Embeddings](https://mlsanity.com/paper/1910.08840/) (2019)
- [International Research Collaboration: Novelty, Conventionality, and Atypicality in Knowledge Recombination](https://mlsanity.com/paper/1804.09070/) (2018)
- [Pandemics are catalysts of scientific novelty: Evidence from COVID-19](https://mlsanity.com/paper/2009.12500/) (2020)
- [Using the Full-text Content of Academic Articles to Identify and Evaluate Algorithm Entities in the Domain of Natural Language Processing](https://mlsanity.com/paper/2010.10817/) (2020)
- [Entitymetrics: Measuring the Impact of Entities](https://mlsanity.com/paper/1309.2486/) (2013)
- [Team Power Dynamics and Team Impact: New Perspectives on Scientific Collaboration using Career Age as a Proxy for Team Power](https://mlsanity.com/paper/2108.04108/) (2021)
- [Flat Teams Drive Scientific Innovation](https://mlsanity.com/paper/2201.06726/) (2022)
- [Revealing the Technology Development of Natural Language Processing: A Scientific Entity-Centric Perspective](https://mlsanity.com/paper/2606.29836/) (2026)

## Abstract (arXiv)

The composition of author teams is an important factor influencing the novelty of academic papers. However, existing studies have paid limited attention to the role of institutional composition, and most novelty measures remain at a general level, making it difficult to explain the specific sources and types of novelty in papers. Taking the field of natural language processing as an example, this study investigates the relationship between team institutional composition and the fine-grained novelty of academic papers. Author teams are classified into three types: academic institutions, industrial institutions, and mixed academic and industrial institutions. Four types of fine-grained knowledge entities are extracted from full-text papers, including methods, datasets, tools, and metrics. The novelty of papers is then measured based on entity combinations, and pairwise combinations of different entity types are further analyzed to examine their contributions to novel papers. The results show that, in the field of natural language processing, collaboration between industrial and academic institutions is more likely to produce novel papers than purely industrial collaboration. From the perspective of fine-grained knowledge entities, mixed academic and industrial teams pay more attention to the novelty of method-metric combinations, whereas industrial teams pay more attention to the novelty of method-tool combinations. This study reveals the relationship between institutional team composition and paper novelty through fine-grained novelty measurement, providing useful evidence for improving paper quality and promoting industry-academia-research collaboration.