Improved Web Page Categorization with Semantic-Aware Focused Crawling Using GloVe and TF-IDF
Bibliographic record
Abstract
Focused crawlers are targeted to search the internet for web pages on specific topics.Its main task is to collect preprocessed and topic related web pages and ignore irrelevant web pages.Traditional focused crawlers have limited success in achieving multi-text categorization of web pages.Due to the large amount of unstructured data present in web pages, the correct classification of web pages based on a given topic is the main practical challenge for focused crawlers.The main objective of this work is to design an improved focused crawling approach using web page classification.In this paper, a text classification model based on the combination of GloVe word vector model and TF IDF weighting technique is proposed to improve the accuracy of web page classification.The GloVe based text classification model is further utilized to guide focused crawlers to classify web pages.The proposed GloVe and TF IDF text categorization models are validated on 10 different datasets and the results are compared with traditional machine learning algorithms as well as different methods based on Naive Bayes, Bag-of-Words and Word2Vec.According to the experimental results, the proposed text classification model is 7-12% better than traditional machine learning algorithms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".