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 | Conference PDF | Archive PDF | Plain Text | 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. |