Seed-Guided Fine-Grained Entity Typing in Science and Engineering Domains
Authors: Yu Zhang, Yunyi Zhang, Yanzhen Shen, Yu Deng, Lucian Popa, Larisa Shwartz, ChengXiang Zhai, Jiawei Han
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two datasets covering four domains demonstrate the effectiveness of SETYPE in comparison with various baselines. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA 2IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA 3IBM Almaden Research Center, San Jose, CA, USA |
| Pseudocode | No | The paper describes the proposed framework and its phases in text and with a diagram (Figure 1), but it does not contain any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and data are available at: https://github.com/yuzhimanhua/SEType. |
| Open Datasets | Yes | We use two publicly available datasets from software engineering and security domains Stack Overflow NER (Tabassum et al. 2020) and Cybersecurity (Bridges et al. 2013). 1https://github.com/jeniyat/Stack Overflow NER 2https://github.com/stucco/auto-labeled-corpus |
| Dataset Splits | Yes | In the original dataset, Stack Overflow question-answer threads are split into training, validation, and testing sets, while Git Hub issue reports form a testing set only. ... We take the training and validation corpora of Stack Overflow, remove their annotations, and treat them as unlabeled corpora to create pseudo-labeled training and validation sets, respectively. ... For the larger NVD corpus, we take 20% as the annotated testing data, and the remaining 80% are treated as unlabeled text to create pseudo-labeled training and validation data. |
| Hardware Specification | Yes | The model is trained on one NVIDIA RTX A6000 GPU. |
| Software Dependencies | No | The paper mentions using BERTOverflow as the PLM and Adam W optimizer, but does not provide specific version numbers for these or other software dependencies like Python or PyTorch. |
| Experiment Setup | Yes | During model training, the window size of context sentences c = 1; the maximum premise length is 462 tokens; the maximum hypothesis length is 50 tokens; the training batch size is 4; we use the Adam W optimizer (Loshchilov and Hutter 2019), warm up the learning rate for the first 100 steps and then linearly decay it, where the learning rate is 5e-5; the weight decay is 0.01, and ϵ = 1e-8. |