Using Graphs of Classifiers to Impose Declarative Constraints on Semi-supervised Learning
Authors: Lidong Bing, William W. Cohen, Bhuwan Dhingra
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experiment with two classes of tasks, link-based text classification and relation extraction. We show modest improvements on well-studied link-based classification benchmarks, and state-of-the-art results on relation-extraction tasks for two realistic domains. |
| Researcher Affiliation | Collaboration | Lidong Bing AI Lab Tencent Inc. lyndonbing@tencent.com William W. Cohen Machine Learning Department Carnegie Mellon University wcohen@cs.cmu.edu Bhuwan Dhingra Language Technologies Institute Carnegie Mellon University bdhingra@cs.cmu.edu |
| Pseudocode | Yes | Figure 1: Declarative specifications of the models for supervised learning, on the left, and for a mutual-exclusivity constraint, on the right. (Contains pseudocode-like rules such as predict(X,Y) pick Label(Y) classify(X,Y).) |
| Open Source Code | No | The paper links to external tools (Spearmint, baseline models) that have open-source code, but it does not state that the authors' own D-Learner implementation or its source code is publicly released or available. |
| Open Datasets | Yes | We use three datasets from [Sen et al., 2008]: Cite Seer, Cora and Pub Med, with their statistical information given in Table 1. |
| Dataset Splits | No | The paper mentions training and testing sets, but does not explicitly describe a separate validation split or its size/proportion. It discusses tuning parameters using Bayesian optimization, which often implies validation, but no explicit split details for a validation set are provided in the text. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., GPU models, CPU types, memory). |
| Software Dependencies | No | The paper mentions the use of 'Pro PPR [Wang et al., 2013]' and 'Spearmint3', but does not provide specific version numbers for Pro PPR or other key software components used in their implementation. |
| Experiment Setup | Yes | After the examples are prepared, we employ Pro PPR to learn multi-class classifiers with α = 0.1. The maximum epoch number is 40, and we find the training usually converges in less than 10 epochs. ... As listed in Table 5, we have 10 parameters: the number of training relation examples (#R)... |