Robust Text Classification in the Presence of Confounding Bias
Authors: Virgile Landeiro, Aron Culotta
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On three diverse text classifications tasks, we find that covariate adjustment results in higher accuracy than competing baselines over a range of confounding relationships (e.g., in one setting, accuracy improves from 60% to 81%). |
| Researcher Affiliation | Academia | Virgile Landeiro and Aron Culotta Department of Computer Science Illinois Institute of Technology Chicago, IL 60616 vlandeir@hawk.iit.edu, aculotta@iit.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any specific links or statements indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We evaluate our approach on three diverse classification tasks: predicting the location of a Twitter user (confounded by gender), the political affiliation of a parliament member (confounded by majority party status), and the sentiment of a movie review (confounded by genre). ... IMDb data from Maas et al. (2011). ... data on the 36th and 39th Canadian Parliaments as studied previously (Hirst, Riabinin, and Graham 2010; Dahll of 2012). |
| Dataset Splits | Yes | For each btrain, btest pair, we sample 5 train/test splits and report the average accuracy. For Parliament, we use 5-fold cross-validation on the 39th Parliament; each fold reserves a different 20% of the 39th Parliament for testing. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'L2-regularized logistic regression' but does not specify any software names with version numbers for replication. |
| Experiment Setup | Yes | From an implementation perspective, the approach above is rather straightforward: p(z) is computed using the maximum likelihood estimate above. We compute p(y|x, z) efficiently by simply appending two additional features ci,0 and ci,1 to each instance xi representing z = 0 and z = 1. The first (resp. second) feature is set to v1 if zi = 0 (resp. zi = 1) and the second feature (resp. first) is set to 0. In the default case, we let v1 = 1 but we revisit this decision in the next section. |