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

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 SynC RF outperforms state-of-the-art and baseline approaches.

BibTeX

@inproceedings{guarino:hal-04652041,
  TITLE = {{SynCRF: Syntax-Based Conditional Random Field for TRIZ Parameter Minings}},
  AUTHOR = {Guarino, Guillaume and Samet, Ahmed and Cavallucci, Denis},
  URL = {https://hal.science/hal-04652041},
  BOOKTITLE = {{Proceedings of the 16th International Conference on Agents and Artificial Intelligence}},
  ADDRESS = {Rome, Italy},
  PUBLISHER = {{Scitepress}},
  VOLUME = {3},
  PAGES = {890-897},
  YEAR = {2024},
  MONTH = Feb,
  DOI = {10.5220/0012411300003636},
  KEYWORDS = {Conditional Random Field, TRIZ, Name Entity Recognition, Text Mining},
  PDF = {https://hal.science/hal-04652041v1/file/islandora_171449.pdf},
  HAL_ID = {hal-04652041},
  HAL_VERSION = {v1},
}