Exploring and Verbalizing Academic Ideas by Concept Co-occurrence

• URL: http://arxiv.org/abs/2306.02282v1
• Date: Sun, 4 Jun 2023 07:01:30 GMT
• Title: Exploring and Verbalizing Academic Ideas by Concept Co-occurrence
• Authors: Yi Xu, Shuqian Sheng, Bo Xue, Luoyi Fu, Xinbing Wang, Chenghu Zhou
• Abstract summary: This study devises a framework based on concept co-occurrence for academic idea inspiration. We construct evolving concept graphs according to the co-occurrence relationship of concepts from 20 disciplines or topics. We generate a description of an idea based on a new data structure called co-occurrence citation quintuple.
• Score: 42.16213986603552
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Researchers usually come up with new ideas only after thoroughly comprehending vast quantities of literature. The difficulty of this procedure is exacerbated by the fact that the number of academic publications is growing exponentially. In this study, we devise a framework based on concept co-occurrence for academic idea inspiration, which has been integrated into a research assistant system. From our perspective, the fusion of two concepts that co-occur in an academic paper can be regarded as an important way of the emergence of a new idea. We construct evolving concept graphs according to the co-occurrence relationship of concepts from 20 disciplines or topics. Then we design a temporal link prediction method based on masked language model to explore potential connections between different concepts. To verbalize the newly discovered connections, we also utilize the pretrained language model to generate a description of an idea based on a new data structure called co-occurrence citation quintuple. We evaluate our proposed system using both automatic metrics and human assessment. The results demonstrate that our system has broad prospects and can assist researchers in expediting the process of discovering new ideas.

Related papers

• Discovering emergent connections in quantum physics research via dynamic word embeddings [0.562479170374811]
  We introduce a novel approach based on dynamic word embeddings for concept combination prediction. Unlike knowledge graphs, our method captures implicit relationships between concepts, can be learned in a fully unsupervised manner, and encodes a broader spectrum of information. Our findings suggest that this representation offers a more flexible and informative way of modeling conceptual relationships in scientific literature.
  arXiv  Detail & Related papers  (2024-11-10T19:45:59Z)
• Good Idea or Not, Representation of LLM Could Tell [86.36317971482755]
  We focus on idea assessment, which aims to leverage the knowledge of large language models to assess the merit of scientific ideas. We release a benchmark dataset from nearly four thousand manuscript papers with full texts, meticulously designed to train and evaluate the performance of different approaches to this task. Our findings suggest that the representations of large language models hold more potential in quantifying the value of ideas than their generative outputs.
  arXiv  Detail & Related papers  (2024-09-07T02:07:22Z)
• Encoding Hierarchical Schema via Concept Flow for Multifaceted Ideology Detection [26.702058189138462]
  Multifaceted ideology detection (MID) aims to detect the ideological leanings of texts towards multiple facets. We develop a novel concept semantics-enhanced framework for the MID task. Our approach achieves state-of-the-art performance in MID, including in the cross-topic scenario.
  arXiv  Detail & Related papers  (2024-05-29T10:37:28Z)
• Learning Structure and Knowledge Aware Representation with Large Language Models for Concept Recommendation [50.31872005772817]
  Concept recommendation aims to suggest the next concept for learners to study based on their knowledge states and the human knowledge system. Previous approaches have not effectively integrated the human knowledge system into the process of designing these educational models. We propose a novel Structure and Knowledge Aware Representation learning framework for concept Recommendation (SKarREC)
  arXiv  Detail & Related papers  (2024-05-21T01:35:36Z)
• SciMON: Scientific Inspiration Machines Optimized for Novelty [68.46036589035539]
  We explore and enhance the ability of neural language models to generate novel scientific directions grounded in literature. We take a dramatic departure with a novel setting in which models use as input background contexts. We present SciMON, a modeling framework that uses retrieval of "inspirations" from past scientific papers.
  arXiv  Detail & Related papers  (2023-05-23T17:12:08Z)
• LEATHER: A Framework for Learning to Generate Human-like Text in Dialogue [15.102346715690755]
  We propose a new theoretical framework for learning to generate text in dialogue. Compared to existing theories of learning, our framework allows for analysis of the multi-faceted goals inherent to text-generation.
  arXiv  Detail & Related papers  (2022-10-14T13:05:11Z)
• Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization [49.00409552570441]
  We study the role of conceptualization in commonsense reasoning, and formulate a framework to replicate human conceptual induction. We apply the framework to ATOMIC, a large-scale human-annotated CKG, aided by the taxonomy Probase.
  arXiv  Detail & Related papers  (2022-06-03T12:24:49Z)
• Formalising Concepts as Grounded Abstractions [68.24080871981869]
  This report shows how representation learning can be used to induce concepts from raw data. The main technical goal of this report is to show how techniques from representation learning can be married with a lattice-theoretic formulation of conceptual spaces.
  arXiv  Detail & Related papers  (2021-01-13T15:22:01Z)
• Revisit Systematic Generalization via Meaningful Learning [15.90288956294373]
  Recent studies argue that neural networks appear inherently ineffective in such cognitive capacity. We reassess the compositional skills of sequence-to-sequence models conditioned on the semantic links between new and old concepts.
  arXiv  Detail & Related papers  (2020-03-14T15:27:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.