Integrated-Block: A New Combination Model to Improve Web Page Segmentation
Bibliographic record
Abstract
Context: Web page segmentation methods have been used for different purposes such as web page classification and content analysis. These methods categorize a web page into different blocks, where each block contains similar components. Objective: The goal of this paper is to propose a new segmentation approach that semantically segments web pages into integrated blocks and obtains high segmentation accuracy. Method: In this paper, we propose a new segmentation model that semantically segments web pages into integrated blocks, where (1) it merges web page content into basic-blocks by simulating human perception using Gestalt laws of grouping; and, (2) it utilizes semantic text similarity to identify similar blocks and regroup these similar basic-blocks as integrated blocks. Results: To verify the accuracy of our approach, we (1) applied it to three datasets, (2) compared it with the five existing state-of-the-art algorithms. The results show that our approach outperforms all the five comparison methods in terms of precision, recall, F-1 score, and ARI. Conclusion: In this paper, we propose a new segmentation model and apply it to three datasets to (1) generate basic-blocks by simulating human perception to segment a web page, (2) identify semantically related blocks and regroup them as an integrated block, and (3) address limitations found in existing approaches.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".