Instance-Based Domain Adaptation in NLP via In-Target-Domain Logistic Approximation
Authors: Rui Xia, Jianfei Yu, Feng Xu, Shumei Wang
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The empirical results on two NLP tasks including text categorization and sentiment classification show that our ILA model has advantages over the state-of-the-art instance adaptation methods, in cross-domain classification accuracy, parameter stability and computational efficiency. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Nanjing University of Science and Technology, China 2School of Economics and Management, Nanjing University of Science and Technology, China {rxia.cn, yujianfei1990, xufeng.breeze}@gmail.com, hwasm@njust.edu.cn |
| Pseudocode | No | The paper describes the algorithm steps in paragraph form but does not provide any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | For text categorization, we employ the 20 Newsgroups dataset2 for experiments. ... For sentiment classification, we follow the datasets and experimental settings used by (Xia et al., 2013b). That is, the Movie Review dataset3 is used as the source domain, and each of the Multi-domain sentiment datasets4 (Book, DVD, Electronics, and Kitchen) serves as the target domain. (Footnotes provide URLs: 2 http://qwone.com/~jason/20Newsgroups/ 3 http://www.cs.cornell.edu/people/pabo/movie-review-data/ 4 http://www.cs.jhu.edu/~mdredze/datasets/sentiment/) |
| Dataset Splits | Yes | The datasets are split in such a way that med and guns are used as the source domain data, and space and misc are used as the target domain data. ... We randomly choose 200 labeled data from the target domain, and mix them with 2000 source-domain labeled data5 to construct a domain-mixed training dataset. The remaining data in the target domain is used as the test set. In both of the two tasks, unigrams and bigrams with term frequency no less than 4 are used as features for classification. We randomly repeat the experiments for 10 times, and report the average results in Table 1. |
| Hardware Specification | Yes | We implement all three algorithms with Python, and run the experiments on a server with a 2.2GHz Intel Xeon Processor and 4GB RAM. |
| Software Dependencies | No | The paper states 'We implement all three algorithms with Python' but does not specify the version of Python or any other software dependencies with version numbers. |
| Experiment Setup | Yes | The tradeoff parameter is set to be 0.7 in text categorization and 0.6 in sentiment classification. The percentage in instance adaptation feature selection is set to be 30% and 50% in text categorization and sentiment classification, respectively. To avoid the over-fitting problem mentioned in the MSD criterion, we set the maximum iteration steps in gradient descent optimization as 30. |