Summary of Regular-pattern-sensitive Crfs For Distant Label Interactions, by Sean Papay et al.
Regular-pattern-sensitive CRFs for Distant Label Interactions
by Sean Papay, Roman Klinger, Sebastian Pado
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new approach to modeling sequence labeling tasks is presented, which enriches standard linear-chain conditional random fields (CRFs) with the ability to learn long-distance label interactions that occur in user-specified patterns. This method, called regular-pattern-sensitive CRFs (RPCRFs), allows users to write concise regular-expression label patterns specifying which types of interactions the model should consider. The approach can be interpreted as a CRF augmented with additional non-local potentials or as a finite-state transducer defined by a set of easily interpretable patterns. Unlike weighted finite-state transducers, exact training and inference are tractable for many pattern sets. This work demonstrates the effectiveness of RPCRFs on synthetic data, showing how different types of patterns can capture various nonlocal dependency structures in label sequences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Regular-pattern-sensitive CRFs (RPCRFs) are a new way to model sequence labeling tasks that lets you learn long-distance label interactions that happen in certain patterns. You can write simple rules to say which types of interactions the model should look for, and it will figure out when these patterns occur in the data. |
Keywords
» Artificial intelligence » Inference » Synthetic data
