Inferring Interpersonal Relations in Narrative Summaries
Authors: Shashank Srivastava, Snigdha Chaturvedi, Tom Mitchell
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On a dataset of movie summaries from Wikipedia, our structured models provide more than a 30% error-reduction over a competitive baseline that considers pairs of characters in isolation. |
| Researcher Affiliation | Academia | 1Carnegie Mellon University 2University of Maryland, College Park ssrivastava@cmu.edu, snigdhac@cs.umd.edu, tom.mitchell@cmu.edu |
| Pseudocode | Yes | Algorithm 1 Perceptron Training for Relations and Algorithm 2 Narrative-specific Model |
| Open Source Code | No | The paper states that the dataset is made publicly available, but there is no explicit statement or link indicating that the source code for the described methodology is available. |
| Open Datasets | Yes | We processed the CMU Movie Summary corpus, a collection of movie plot summaries from Wikipedia, along with aligned meta-data (Bamman, O Connor, and Smith 2013); and set up an online annotation task using BRAT (Stenetorp et al. 2012). ... The dataset is made publicly available for research on the first author s webpage. |
| Dataset Splits | Yes | Figure 4 shows the cross-validation performance of major feature families of text features on the training set. and We tune values of hyperparameters... through cross validation on training data. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running the experiments. |
| Software Dependencies | No | The paper mentions software tools such as "Stanford Core-NLP system", "Semafor parser", and "BRAT" but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | We tune values of hyperparameters, i.e. number of training epochs for the structured perceptron (10), the weighting parameter for the clustering model (α=0.8), and the number of clusters (K=2) through cross validation on training data. For the structured models, reported results are averages over 10 initializations. |