metadata

license: apache-2.0

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}
}