Estimating Text Intelligibility via Information Packaging Analysis
Authors: Junyi Li
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | I will also introduce new dataset and methods for analyzing text specificity in its discourse. Using this corpus, I seek to propose techniques that improve the organization of system outputs at multi-sentence level. I designed a system that achieves more than 80% accuracy (Li and Nenkova 2015a). |
| Researcher Affiliation | Academia | Junyi Jessy Li University of Pennsylvania Department of Computer and Information Science Philadelphia, PA 19104 ljunyi@seas.upenn.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a specific repository link or an explicit statement about the release of source code for the methodology described in this paper. |
| Open Datasets | No | The paper mentions 'We are composing a context-informed text specificity corpus' and 'We have collected annotations of 543 sentences (15K words) from 16 politics and business news articles', but it does not provide concrete access information (specific link, DOI, repository name, or formal citation with authors/year for a publicly available dataset). |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | No | The paper does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text. |