TY - GEN
T1 - The Grid
T2 - 1st Workshop on NLP for Science, NLP4Science 2024
AU - Beal Cohen, Allegra A.
AU - Alexeeva, Maria
AU - Alcock, Keith
AU - Surdeanu, Mihai
N1 - Publisher Copyright:
© 2024 Association for Computational Linguistics.
PY - 2024
Y1 - 2024
N2 - When building models of human behavior, we often struggle to find data that capture important factors at the right level of granularity. In these cases, we must rely on expert knowledge to build models. To help partially automate the organization of expert knowledge for modeling, we combine natural language processing (NLP) and machine learning (ML) methods in a tool called the Grid. The Grid helps users organize textual knowledge into clickable cells along two dimensions using iterative, collaborative clustering. We conduct a user study to explore participants’ reactions to the Grid, as well as to investigate whether its clustering feature helps participants organize a corpus of expert knowledge. We find that participants using the Grid’s clustering feature appeared to work more efficiently than those without it, but written feedback about the clustering was critical. We conclude that the general design of the Grid was positively received and that some of the user challenges can likely be mitigated through the use of LLMs.
AB - When building models of human behavior, we often struggle to find data that capture important factors at the right level of granularity. In these cases, we must rely on expert knowledge to build models. To help partially automate the organization of expert knowledge for modeling, we combine natural language processing (NLP) and machine learning (ML) methods in a tool called the Grid. The Grid helps users organize textual knowledge into clickable cells along two dimensions using iterative, collaborative clustering. We conduct a user study to explore participants’ reactions to the Grid, as well as to investigate whether its clustering feature helps participants organize a corpus of expert knowledge. We find that participants using the Grid’s clustering feature appeared to work more efficiently than those without it, but written feedback about the clustering was critical. We conclude that the general design of the Grid was positively received and that some of the user challenges can likely be mitigated through the use of LLMs.
UR - http://www.scopus.com/inward/record.url?scp=85216920253&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85216920253&partnerID=8YFLogxK
U2 - 10.18653/v1/2024.nlp4science-1.19
DO - 10.18653/v1/2024.nlp4science-1.19
M3 - Conference contribution
AN - SCOPUS:85216920253
T3 - NLP4Science 2024 - 1st Workshop on NLP for Science, Proceedings of the Workshop
SP - 219
EP - 229
BT - NLP4Science 2024 - 1st Workshop on NLP for Science, Proceedings of the Workshop
A2 - Peled-Cohen, Lotem
A2 - Calderon, Nitay
A2 - Lissak, Shir
A2 - Reichart, Roi
PB - Association for Computational Linguistics (ACL)
Y2 - 16 November 2024
ER -