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in Data Studio
Software Entity Recognition
Description
Data collected from our paper "Software Entity Recognition with Noise-robust Learning", ASE 2023.
WikiSER corpus includes 1.7M sentences with named entity labels extracted from 79k Wikipedia articles. Relevant software named entities are labeled under 12 fine-grained categories:
| Type | Examples |
|---|---|
| Algorithm | Auction algorithm, Collaborative filtering |
| Application | Adobe Acrobat, Microsoft Excel |
| Architecture | Graphics processing unit, Wishbone |
| Data_Structure | Array, Hash table, mXOR linked list |
| Device | Samsung Gear S2, iPad, Intel T5300 |
| Error Name | Buffer overflow, Memory leak |
| General_Concept | Memory management, Nouvelle AI |
| Language | C++, Java, Python, Rust |
| Library | Beautiful Soup, FastAPI |
| License | Cryptix General License, MIT License |
| Operating_System | Linux, Ubuntu, Red Hat OS, MorphOS |
| Protocol | TLS, FTPS, HTTP 404 |
WikiSER is organized by the Wiki articles in which the data was scraped from.
|-- Adobe_Flash.txt
|-- Linux.txt
|-- Java_(programming_language).txt
|-- ...
Each sentences are split by <s>...</s> and tokenized with stokenizer.
Structure
In the folder:
wikiser: Full zipped data
wikiser-small: Subset of the data used for training wikiser-bert-base and wikiser-bert-large
wikiser-sample: A few examples
Citation
@inproceedings{nguyen2023software,
title={Software Entity Recognition with Noise-Robust Learning},
author={Nguyen, Tai and Di, Yifeng and Lee, Joohan and Chen, Muhao and Zhang, Tianyi},
booktitle={Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering (ASE'23)},
year={2023},
organization={IEEE/ACM}
}
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