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Datasets:
boschresearch
/
sofc_materials_articles

Tasks:
Text Generation
Fill-Mask
Token Classification
Sub-tasks:
named-entity-recognition
slot-filling
topic-classification
Languages:
English
Size:
n<1K
ArXiv:
License:
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sofc_materials_articles / README.md
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metadata
annotations_creators:
  - expert-generated
language_creators:
  - found
languages:
  - en
licenses:
  - cc-by-4-0
multilinguality:
  - monolingual
size_categories:
  - n<1K
source_datasets:
  - original
task_categories:
  - sequence-modeling
  - structure-prediction
  - text-classification
task_ids:
  - named-entity-recognition
  - slot-filling
  - topic-classification

Dataset Card Creation Guide

Table of Contents

Dataset Description

Dataset Summary

The SOFC-Exp corpus contains 45 scientific publications about solid oxide fuel cells (SOFCs), published between 2013 and 2019 as open-access articles all with a CC-BY license. The dataset was manually annotated by domain experts with the following information:

  • Mentions of relevant experiments have been marked using a graph structure corresponding to instances of an Experiment frame (similar to the ones used in FrameNet.) We assume that an Experiment frame is introduced to the discourse by mentions of words such as report, test or measure (also called the frame-evoking elements). The nodes corresponding to the respective tokens are the heads of the graphs representing the Experiment frame.
  • The Experiment frame related to SOFC-Experiments defines a set of 16 possible participant slots. Participants are annotated as dependents of links between the frame-evoking element and the participant node.
  • In addition, we provide coarse-grained entity/concept types for all frame participants, i.e, MATERIAL, VALUE or DEVICE. Note that this annotation has not been performed on the full texts but only on sentences containing information about relevant experiments, and a few sentences in addition. In the paper, we run experiments for both tasks only on the set of sentences marked as experiment-describing in the gold standard, which is admittedly a slightly simplified setting. Entity types are only partially annotated on other sentences. Slot filling could of course also be evaluated in a fully automatic setting with automatic experiment sentence detection as a first step.

Supported Tasks and Leaderboards

  • topic-classification: The dataset can be used to train a model for topic-classification, to identify sentences that mention SOFC-related experiments.
  • named-entity-recognition: The dataset can be used to train a named entity recognition model to detect MATERIAL, VALUE, DEVICE, and EXPERIMENT entities.
  • slot-filling: The slot-filling task is approached as fine-grained entity-typing-in-context, assuming that each sentence represents a single experiment frame. Sequence tagging architectures are utilized for tagging the tokens of each experiment-describing sentence with the set of slot types.

The paper experiments with BiLSTM architectures with BERT- and SciBERT- generated token embeddings, as well as with BERT and SciBERT directly for the modeling task. A simple CRF architecture is used as a baseline for sequence-tagging tasks. Implementations of the transformer-based architectures can be found in the huggingface/transformers library: BERT, SciBERT

Languages

This corpus is in English.

Dataset Structure

Data Instances

As each example is a full text of an academic paper, plus annotations, a json formatted example is space-prohibitive for this README.

Data Fields

  • text: The full text of the paper
  • sentence_offsets: Start and end character offsets for each sentence in the text.
    • begin_char_offset: a int64 feature.
    • end_char_offset: a int64 feature.
  • sentences: A sequence of the sentences in the text (using sentence_offsets)
  • sentence_labels: Sequence of binary labels for whether a sentence contains information of interest.
  • token_offsets: Sequence of sequences containing start and end character offsets for each token in each sentence in the text.
    • offsets: a dictionary feature containing:
      • begin_char_offset: a int64 feature.
      • end_char_offset: a int64 feature.
  • tokens: Sequence of sequences containing the tokens for each sentence in the text.
    • feature: a string feature.
  • entity_labels: a dictionary feature containing:
    • feature: a classification label, with possible values including B-DEVICE, B-EXPERIMENT, B-MATERIAL, B-VALUE, I-DEVICE.
  • slot_labels: a dictionary feature containing:
    • feature: a classification label, with possible values including B-anode_material, B-cathode_material, B-conductivity, B-current_density, B-degradation_rate.
  • links: a dictionary feature containing:
    • relation_label: a classification label, with possible values including coreference, experiment_variation, same_experiment, thickness.
    • start_span_id: a int64 feature.
    • end_span_id: a int64 feature.
  • slots: a dictionary feature containing:
    • frame_participant_label: a classification label, with possible values including anode_material, cathode_material, current_density, degradation_rate, device.
    • slot_id: a int64 feature.
  • spans: a dictionary feature containing:
    • span_id: a int64 feature.
    • entity_label: a classification label, with possible values including ``, DEVICE, MATERIAL, VALUE.
    • sentence_id: a int64 feature.
    • experiment_mention_type: a classification label, with possible values including ``, current_exp, future_work, general_info, previous_work.
    • begin_char_offset: a int64 feature.
    • end_char_offset: a int64 feature.
  • experiments: a dictionary feature containing:
    • experiment_id: a int64 feature.
    • span_id: a int64 feature.
    • slots: a dictionary feature containing:
      • frame_participant_label: a classification label, with possible values including anode_material, cathode_material, current_density, degradation_rate, conductivity.
      • slot_id: a int64 feature.

Very detailed information for each of the fields can be found in the corpus file formats section of the associated dataset repo

Data Splits

This dataset consists of three splits:

Train Valid Test
Input Examples 26 8 11

The authors propose the experimental setting of using the training data in a 5-fold cross validation setting for development and tuning, and finally applying tte model(s) to the independent test set.

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

The corpus consists of 45 open-access scientific publications about SOFCs and related research, annotated by domain experts.

Annotations

Annotation process

For manual annotation, the authors use the InCeption annotation tool (Klie et al., 2018).

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

The manual annotations created for the SOFC-Exp corpus are licensed under a Creative Commons Attribution 4.0 International License (CC-BY-4.0).

Citation Information 

@misc{friedrich2020sofcexp,
      title={The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain},
      author={Annemarie Friedrich and Heike Adel and Federico Tomazic and Johannes Hingerl and Renou Benteau and Anika Maruscyk and Lukas Lange},
      year={2020},
      eprint={2006.03039},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}