DFKI-SLT/dfki-product-corpus

Dataset Card for DFKI Product Corpus

The DFKI Product Corpus is a dataset of 174 English web pages and social media posts annotated for product and company named entities, and the relation CompanyProvidesProduct.

Dataset Details

Dataset Description

This repository contains only those sentences from the original full-text documents which contain at least one ORGANIZATION and one PRODUCT entity. For each ORG-PROD combination in a sentence, the dataset contains an instance, labeled with either "CompanyProvidesProduct" or "no_relation". The dataset is hence more suited for Relation Extraction, similar to the TACRED dataset, and not for Named Entity Recognition.

• Curated by: DFKI-SLT
• Shared by: DFKI-SLT
• Language(s) (NLP): en-us, en-gb
• License: CC-BY-4.0

Dataset Sources

• Repository: https://github.com/DFKI-NLP/product-corpus
• Paper: https://aclanthology.org/L18-1704.pdf

Uses

Direct Use

The dataset can be used for binary relation classification.

Dataset Structure

The original dataset was annotated at the document level. We used a random 80/10/10 split to create train, dev and test sets. The documents in each split were then converted to sentence-level JSONL files. The test set documents contained a larger number of ORG and PROD annotations than the dev split, which resulted in a larger test split in the JSONL version. Sentence splitting and tokenization were performed with Spacy 3.2.0, using the `en_core_web_sm' model. The JSON field structure follows the structure used in the TACRED dataset and includes fields for 'grammar' and 'type', which are somewhat redundant in our case. of the JSON follows

Each instance of the dataset contains the following fields:

• id: the instance id of this sentence, a string feature
• tokens: the list of tokens of this sentence, a list feature
• label: the relation label, either "CompanyProvidesProduct" or "no_relation"
• entities: the token-based start and end offsets of the head (ORG) and tail (PROD) entities, a list feature
• grammar: a fixed ["SUBJ", "OBJ"] list, since we do not include inverted relations
• type: a fixed ["ORGANIZATION", "PRODUCT"] list, denoting the entity types of the head and tail entities

Data Splits

Train	Validation	Test
2437	330	510

Dataset Creation

Curation Rationale

The dataset was created as part of the PLASS project in the Speech & Language Technology Group of DFKI between 2019-2022. The goal was to create training and evaluation data for company-product relations expressed in web text, as a means of building a knowledge graph.

Source Data

Source data is web text, mainly news, but also product pages, special interest web pages, etc.

Data Collection and Processing

[More Information Needed]

Who are the source data producers?

[More Information Needed]

Annotations

The data contains annotations for Named Entities (ORGANIZATION, PRODUCT) and a single relation type (CompanyProvidesProduct), as well as "no_relation".

Annotation process

See the paper https://aclanthology.org/L18-1704.pdf

Who are the annotators?

Trained student assistants and researchers at DFKI SLT.

Personal and Sensitive Information

This dataset contains no personal or sensitive information.

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

Citation

BibTeX:

@InProceedings{SCHÖN18.88,
  author = {Saskia Schön and Veselina Mironova and Aleksandra Gabryszak and Leonhard Hennig},
  title = "{A Corpus Study and Annotation Schema for Named Entity Recognition and Relation Extraction of Business Products}",
  booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
  year = {2018},
  month = {May 7-12, 2018},
  address = {Miyazaki, Japan},
  editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {979-10-95546-00-9},
  language = {english}
  }

APA:

Schön, S., Mironova, V., Gabryszak, A., & Hennig, L. (2018). A Corpus Study and Annotation Schema for Named Entity Recognition and Relation Extraction of Business Products. ArXiv, abs/2004.03287.

Contributions

Thanks to @leonhardhennig for adding this dataset.