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..
Verb Pattern: A Probabilistic Semantic Representation on Verbs
Authors: Wanyun Cui, Xiyou Zhou, Hangyu Lin, Yanghua Xiao, Haixun Wang, Seung-won Hwang, Wei Wang
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results prove the high effectiveness of verb patterns. We further apply verb patterns to context-aware conceptualization, to show that verb patterns are helpful in semantic-related tasks. We conducted extensive experiments. The results verify the effectiveness of our model and algorithm. |
| Researcher Affiliation | Collaboration | Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University; Facebook, USA; Yonsei University |
| Pseudocode | No | The paper describes the algorithm steps in paragraph format but does not present a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper mentions "verb patterns (available at http://kw.fudan.edu.cn/verb)", which refers to the patterns themselves, not the source code for the proposed methodology or algorithm. |
| Open Datasets | Yes | We use two public data sets for this purpose: Google Syntactic N-Grams (http://commondatastorage.googleapis.com/books/syntactic -ngrams/index.html) and Probase (Wu et al. 2012). |
| Dataset Splits | No | The paper uses the Google Syntactic N-Grams and Probase datasets, but it does not specify train/validation/test splits for the pattern generation model itself. It describes evaluation on separate test datasets, but not splits for model training. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes the simulated annealing algorithm, including general parameters like 'S is the number of steps performed in SA, and A is a constant to control the speed of cooling process.' However, it does not provide concrete values for these or other hyperparameters (e.g., learning rate, batch size, specific temperature values) or system-level training settings. |