One for All: Neural Joint Modeling of Entities and Events
Authors: Trung Minh Nguyen, Thien Huu Nguyen6851-6858
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to evaluate the effectiveness of the proposed model. The experiments demonstrate the benefits of joint modeling with deep learning for the three subtasks of EE over the traditional baselines, yielding the state-of-the-art performance on the long-standing and widely-used dataset ACE 2005. |
| Researcher Affiliation | Collaboration | Trung Minh Nguyen Alt Inc. 8F, Higashi-Kanda 3-1-2, Chiyoda-ku Tokyo, 101-0031, Japan nguyen.minh.trung@alt.ai [...] Thien Huu Nguyen Department of Computer and Information Science University of Oregon Eugene, Oregon 97403, USA thien@cs.uoregon.edu |
| Pseudocode | No | The paper describes the model architecture and components in text and with a diagram (Figure 1), but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | We evaluate the proposed model on the ACE 2005 dataset. In order to ensure a fair comparison, we use the same data split with the prior work on this dataset (Li, Ji, and Huang 2013; Nguyen, Cho, and Grishman 2016a; Nguyen et al. 2016b; Yang and Mitchell 2016; Sha et al. 2018) in which 40 newswire documents are used for the test set, 30 other documents are reserved for the development set, and the remaining 529 documents form the training set. [...] 1https://www.ldc.upenn.edu/collaborations/past-projects/ace |
| Dataset Splits | Yes | In order to ensure a fair comparison, we use the same data split with the prior work on this dataset (Li, Ji, and Huang 2013; Nguyen, Cho, and Grishman 2016a; Nguyen et al. 2016b; Yang and Mitchell 2016; Sha et al. 2018) in which 40 newswire documents are used for the test set, 30 other documents are reserved for the development set, and the remaining 529 documents form the training set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'Stanford Core NLP' for pre-processing but does not specify its version number or any other software dependencies with version numbers. |
| Experiment Setup | Yes | Regarding the hyper-parameters, the word embeddings have the dimension of 300; the number of hidden units in the encoding RNNs is 300; and the window for local context u is 2. We use the feed-forward neural networks with one hidden layer of 600 hidden units for FF EMD, FF ED and FF ARP . The mini-batch size is 50 while the Frobenius norm for the parameters norms is 3. These values give us best the results on the development set. For the penalty coefficients in the objective function, the best values we obtained from the development data is α = 0.5, β = 1.0, γ = 0.5. We also implement dropouts on the input word embeddings and the hidden vectors of the feed-forward networks with a rate of 0.5 (tuned on the development set). |