Neural Bag-of-Ngrams
Authors: Bofang Li, Tao Liu, Zhe Zhao, Puwei Wang, Xiaoyong Du
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform qualitative evaluation on IMDB dataset (Table 2), and quantitative evaluation on text classification task (7 datasets) and semantic relatedness task (2 datasets with 7 categories). |
| Researcher Affiliation | Academia | Bofang Li, Tao Liu, Zhe Zhao, Puwei Wang, Xiaoyong Du School of Information, Renmin University of China, Beijing, China Key laboratory of Data Engineering and Knowledge Engineering, MOE, Beijing, China {libofang, tliu, helloworld, wangpuwei, duyong}@ruc.edu.cn |
| Pseudocode | No | The paper describes methods textually and mathematically but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code of Neural-Bo N is published at https://github. com/libofang/Neural-Bo N. |
| Open Datasets | Yes | For text classification task, hyper-parameters are tuned on 20% of the training data from IMDB dataset (Maas et al. 2011). For semantic relatedness task, hyper-parameters are tuned on the development data from SICK dataset (Marelli et al. 2014). Similar to previous researches, Toronto Books Corpus is used as training data. |
| Dataset Splits | Yes | For text classification task, hyper-parameters are tuned on 20% of the training data from IMDB dataset (Maas et al. 2011). For semantic relatedness task, hyper-parameters are tuned on the development data from SICK dataset (Marelli et al. 2014). |
| Hardware Specification | Yes | Table 3: Approximate training time of models for a single epoch on one million words. CPU: Intel Xeon E5-2670 (32core). GPU: NVIDIA Tesla K40. |
| Software Dependencies | No | The paper mentions techniques like 'Negative Sampling', 'stochastic gradient descent', and 'backpropagation', but does not list any specific software or library names with version numbers used for implementation. |
| Experiment Setup | Yes | Optimal hyper-parameters are actually identical: the vector dimension is 500, the learning rate is fixed to 0.25, the negative sampling size is 5, and models are trained for 10 iteration. |