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..
Adaptive Multi-Compositionality for Recursive Neural Models with Applications to Sentiment Analysis
Authors: Li Dong, Furu Wei, Ming Zhou, Ke Xu
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We integrate Ada MC into existing recursive neural models and conduct extensive experiments on the Stanford Sentiment Treebank. The results illustrate that Ada MC significantly outperforms state-of-the-art sentiment classification methods. |
| Researcher Affiliation | Collaboration | State Key Lab of Software Development Environment, Beihang University, Beijing, China Microsoft Research, Beijing, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing source code for the described methodology or a direct link to a code repository. |
| Open Datasets | Yes | We evaluate the models on Stanford Sentiment Treebank1. This corpus contains the labels of syntactically plausible phrases... 1http://nlp.stanford.edu/sentiment/treebank.html |
| Dataset Splits | Yes | We use the standard dataset splits (train: 8,544, dev: 1,101, test: 2,210) in all the experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper mentions 'Ada Grad' as an optimization algorithm, but does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We use the mini-batch version Ada Grad in our experiments with the batch size between 20 and 30. We employ f = tanh as the nonlinearity function. To initialize the parameters, we randomly sample values from a uniform distribution U ( , + ), where is a small value. |