Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Bag-of-Embeddings for Text Classification
Authors: Peng Jin, Yue Zhang, Xingyuan Chen, Yunqing Xia
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on two standard document classification benchmark data show that our model achieve higher accuracies and macro-F1 scores compared than state-ofthe-art models. |
| Researcher Affiliation | Collaboration | 1 School of Computer Science, Leshan Normal University, Leshan China, 614000 2 Singapore University of Technology and Design, Singapore 487372 3 Search Technology Center, Microsoft, Beijing China, 100087 |
| Pseudocode | No | The paper describes methods using mathematical equations and prose, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code of this paper is released at https://github.com/hiccxy/Bag-of-embedding-fortext-classification. |
| Open Datasets | Yes | We choose the twenty newsgroup (20NG)2 test [Lang, 1995] for multi-class classification. We use the bydate data, which consists of 11,314 training instances and 7,532 test instances... For imbalanced classification, Lewis [1995] introduced the Reuters-21578 corpus3. R8 consists of 5,485 documents for training and 2,189 for testing. |
| Dataset Splits | No | The paper specifies training and test instances for 20NG (11,314 training, 7,532 test) and R8 (5,485 training, 2,189 testing), but does not explicitly mention a separate validation split for their proposed model, nor does it detail cross-validation for their model (only for a baseline SVM). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'LIBSVM' for baselines, but does not provide specific version numbers for it or any other software dependencies crucial for reproducing their own model's experiments. |
| Experiment Setup | Yes | For the parameters, we set l = 5, the iteration number to 5, the size of context window to 10 and the dimensions of word vector to 100. |