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..
Learning Word Representations with Hierarchical Sparse Coding
Authors: Dani Yogatama, Manaal Faruqui, Chris Dyer, Noah Smith
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on various benchmark tasks word similarity ranking, syntactic and semantic analogies, sentence completion, and sentiment analysis demonstrate that the method outperforms or is competitive with state-of-the-art methods. |
| Researcher Affiliation | Academia | Dani Yogatama EMAIL Manaal Faruqui EMAIL Chris Dyer EMAIL Noah A. Smith EMAIL Language Technologies Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA |
| Pseudocode | Yes | Algorithm 1 Fast algorithm for learning word representations with the forest regularizer. |
| Open Source Code | No | Our word representations are available at: http://www.ark.cs.cmu.edu/dyogatam/wordvecs/. This link provides the learned word representations (data), not the source code for the methodology described in the paper. |
| Open Datasets | Yes | We use the WMT-2011 English news corpus as our training data.5 The corpus contains about 15 million sentences and 370 million words. The size of our vocabulary is 180,834. |
| Dataset Splits | Yes | We use the movie reviews dataset from Socher et al. (2013). The dataset consists of 6,920 sentences for training, 872 sentences for development, and 1,821 sentences for testing. |
| Hardware Specification | No | The paper mentions '640 cores' for parts of the training and 'computing resources provided by Google and the Pittsburgh Supercomputing Center', but does not provide specific hardware details like CPU/GPU models, memory, or exact cloud instance specifications. |
| Software Dependencies | No | The paper mentions using implementations for baseline methods like 'http://rnnlm.org/' and 'https://code.google.com/p/word2vec/', and 'http://nlp.stanford.edu/projects/glove/', but does not specify version numbers for these or any other software dependencies crucial for replication. |
| Experiment Setup | Yes | In our experiments, we use forests similar to those in Figure 1 to organize the latent word space. We choose to evaluate performance with M = 52 (4 trees) and M = 520 (40 trees). We set λ = 0.1. |