Relation Extraction with Sentence Simplification Process and Entity Information
Bibliographic record
Abstract
Graph-based Knowledge Bases (KBs) are composed of relational facts that can be perceived as two entities, called head and tail, linked via a relation. Processes of constructing KBs, i.e., populating them with such facts, as well as revising and updating them are of special interest. These should be performed automatically, especially in the case when the main sources of facts are textual documents. For this reason, a task of Relation Extraction (RE), i.e., predicting a relation that links two entities mentioned in a sentence, is one of the most important activities. Using RE processes, new relational facts can be extracted, and KBs can be built and updated using unstructured information. In this paper, we propose a novel procedure for RE. It is based on a sentence distilling technique that works on dependency trees and removes noisy tokens from sentences while preserving the most relevant and useful ones. In addition, the proposed procedure utilizes information about types of linked entities, it means types of relations’ heads and tails. Our neural network model using processed and new input information is evaluated on the widely used NYT dataset and compared to other state-of-the-art RE methods. Experimental results show the effectiveness of the proposed procedure against other methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".