Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
text
Languages:
English
Size:
10K - 100K
DOI:
License:
| license: apache-2.0 | |
| task_categories: | |
| - token-classification | |
| language: | |
| - en | |
| tags: | |
| - NER | |
| - Aerospace | |
| - ORG | |
| - SYS | |
| - DATETIME | |
| - RESOURCE | |
| - VALUE | |
| pretty_name: all_text_annotation_NER.txt | |
| size_categories: | |
| - n<1K | |
| # Dataset Card for aeroBERT-NER | |
| ## Dataset Description | |
| - **Paper:** aeroBERT-NER: Named-Entity Recognition for Aerospace Requirements Engineering using BERT | |
| - **Point of Contact:** [email protected] | |
| ### Dataset Summary | |
| This dataset contains sentences from the aerospace requirements domain. The sentences are tagged for five NER categories (SYS, VAL, ORG, DATETIME, and RES) using the BIO tagging scheme. | |
| There are a total of 1432 sentences. The creation of this dataset is aimed at - <br> | |
| (1) Making available an **open-source** dataset for aerospace requirements which are often proprietary <br> | |
| (2) Fine-tuning language models for **token identification** (NER) specific to the aerospace domain <br> | |
| This dataset can be used for training or fine-tuning language models for the identification of mentioned Named-Entities in aerospace texts. | |
| ## Dataset Structure | |
| The dataset is of the format: ``Sentence-Number * WordPiece-Token * NER-tag`` <br> | |
| "*" is used as a delimiter to avoid confusion with commas (",") that occur in the text. The following example shows the dataset structure for Sentence #1431. <br> | |
| 1431\*the\*O <br> | |
| 1431\*airplane\*B-SYS <br> | |
| 1431\*takeoff\*O <br> | |
| 1431\*performance\*O <br> | |
| 1431\*must\*O <br> | |
| 1431\*be\*O <br> | |
| 1431\*determined\*O <br> | |
| 1431\*for\*O <br> | |
| 1431\*climb\*O <br> | |
| 1431\*gradients\*O <br> | |
| 1431\*.\*O <br> | |
| ## Dataset Creation | |
| ### Source Data | |
| Two types of aerospace texts are used to create the aerospace corpus for fine-tuning BERT: <br> | |
| (1) general aerospace texts such as publications by the National Academy of Space Studies Board, and <br> | |
| (2) certification requirements from Title 14 CFR. A total of 1432 sentences from the aerospace domain were included in the corpus. <br> | |
| ### Importing dataset into Python environment | |
| Use the following code chunk to import the dataset into Python environment as a DataFrame. | |
| ``` | |
| from datasets import load_dataset | |
| import pandas as pd | |
| dataset = load_dataset("archanatikayatray/aeroBERT-NER") | |
| #Converting the dataset into a pandas DataFrame | |
| dataset = pd.DataFrame(dataset["train"]["text"]) | |
| dataset = dataset[0].str.split('*', expand = True) | |
| #Getting the headers from the first row | |
| header = dataset.iloc[0] | |
| #Excluding the first row since it contains the headers | |
| dataset = dataset[1:] | |
| #Assigning the header to the DataFrame | |
| dataset.columns = header | |
| #Viewing the last 10 rows of the annotated dataset | |
| dataset.tail(10) | |
| ``` | |
| ### Annotations | |
| #### Annotation process | |
| A Subject Matter Expert (SME) was consulted for deciding on the annotation categories. The BIO Tagging scheme was used for annotating the dataset. | |
| **B** - Beginning of entity <br> | |
| **I** - Inside an entity <br> | |
| **O** - Outside an entity <br> | |
| | Category | NER Tags | Example | | |
| | :----: | :----: | :----: | | |
| | System | B-SYS, I-SYS | exhaust heat exchangers, powerplant, auxiliary power unit | | |
| | Value | B-VAL, I-VAL | 1.2 percent, 400 feet, 10 to 19 passengers | | |
| | Date time | B-DATETIME, I-DATETIME | 2013, 2019, May 11,1991 | | |
| | Organization | B-ORG, I-ORG | DOD, Ames Research Center, NOAA | | |
| | Resource | B-RES, I-RES | Section 25-341, Sections 25-173 through 25-177, Part 23 subpart B | | |
| The distribution of the various entities in the corpus is shown below - <br> | |
| |NER Tag|Description|Count| | |
| | :----: | :----: | :----: | | |
| O | Tokens that are not identified as any NE | 37686 | | |
| B-SYS | Beginning of a system NE | 1915 | | |
| I-SYS | Inside a system NE | 1104 | | |
| B-VAL | Beginning of a value NE | 659 | | |
| I-VAL | Inside a value NE | 507 | | |
| B-DATETIME| Beginning of a date time NE | 147 | | |
| I-DATETIME | Inside a date time NE | 63 | | |
| B-ORG | Beginning of an organization NE | 302 | | |
| I-ORG | Inside a organization NE | 227 | | |
| B-RES | Beginning of a resource NE |390 | | |
| I-RES | Inside a resource NE | 1033 | | |
| ### Limitations | |
| (1)The dataset is an imbalanced dataset, given that's how language is (not every word is a Named-Entity). Hence, using ``Accuracy`` as a metric for the model performance is | |
| NOT a good idea. The use of Precision, Recall, and F1 scores are suggested for model performance evaluation. | |
| (2)This dataset does not contain a test set. Hence, it is suggested that the user split the dataset into training/validation/testing after importing the data into a Python environment. | |
| Please refer to the Appendix of the paper for information on the test set. | |
| ### Citation Information | |
| ``` | |
| @Article{aeroBERT-NER, | |
| AUTHOR = {Tikayat Ray, Archana and Pinon Fischer, Olivia J. and Mavris, Dimitri N. and White, Ryan T. and Cole, Bjorn F.}, | |
| TITLE = {aeroBERT-NER: Named-Entity Recognition for Aerospace Requirements Engineering using BERT}, | |
| JOURNAL = {AIAA SCITECH 2023 Forum}, | |
| YEAR = {2023}, | |
| URL = {https://arc.aiaa.org/doi/10.2514/6.2023-2583}, | |
| DOI = {10.2514/6.2023-2583} | |
| } | |
| @phdthesis{tikayatray_thesis, | |
| author = {Tikayat Ray, Archana}, | |
| title = {Standardization of Engineering Requirements Using Large Language Models}, | |
| school = {Georgia Institute of Technology}, | |
| year = {2023}, | |
| doi = {10.13140/RG.2.2.17792.40961}, | |
| URL = {https://repository.gatech.edu/items/964c73e3-f0a8-487d-a3fa-a0988c840d04} | |
| } | |
| ``` | |