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 [1].
Learning Word Vectors Efficiently Using Shared Representations and Document Representations
Authors: Qun Luo, Weiran Xu
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our methods on the Stanford Word Analogy dataset and the Stanford Sentiment dataset. The experimental results show that our proposed models significantly outperform the state-of-the-art models in word analogy detection and achieve competitive results in sentiment classification. |
| Researcher Affiliation | Academia | Qun Luo and Weiran Xu Beijing University of Posts and Telecommunications, 10 Xitucheng road, Beijing, China. |
| Pseudocode | No | The provided text does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code, nor does it state that code is released or available. |
| Open Datasets | Yes | We evaluate our methods on the Stanford Word Analogy dataset and the Stanford Sentiment dataset. |
| Dataset Splits | No | The paper mentions using specific datasets but does not provide specific dataset split information (percentages, sample counts, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text. |