SynCRF: Syntax-Based Conditional Random Field for TRIZ Parameter Minings

Guillaume Guarino, Ahmed Samet, Denis Cavallucci

2024

Abstract

Conditional random fields (CRF) are widely used for sequence labeling such as Named Entity Recognition (NER) problems. Most CRFs, in Natural Language Processing (NLP) tasks, model the dependencies between predicted labels without any consideration for the syntactic specificity of the document. Unfortunately, these approaches are not flexible enough to consider grammatically rich documents like patents. Additionally, the position and the grammatical class of the words may influence the text’s understanding. Therefore, in this paper, we introduce SynCRF which considers grammatical information to compute pairwise potentials. Syn-CRF is applied to TRIZ (Theory of Inventive Problem Solving), which offers a comprehensive set of tools to analyze and solve problems. TRIZ aims to provide users with inventive solutions given technical contradiction parameters. SynCRF is applied to mine these parameters from patent documents. Experiments on a labeled real-world dataset of patents show that SynCRF outperforms state-of-the-art and baseline approaches.

Paper Citation

in Harvard Style

Guarino G., Samet A. and Cavallucci D. (2024). SynCRF: Syntax-Based Conditional Random Field for TRIZ Parameter Minings. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 890-897. DOI: 10.5220/0012411300003636

in Bibtex Style

@conference{icaart24,
author={Guillaume Guarino and Ahmed Samet and Denis Cavallucci},
title={SynCRF: Syntax-Based Conditional Random Field for TRIZ Parameter Minings},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={890-897},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012411300003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - SynCRF: Syntax-Based Conditional Random Field for TRIZ Parameter Minings
SN - 978-989-758-680-4
AU - Guarino G.
AU - Samet A.
AU - Cavallucci D.
PY - 2024
SP - 890
EP - 897
DO - 10.5220/0012411300003636
PB - SciTePress