README.md

---
language:
- de
- en
license: gpl-3.0
size_categories:
- 10k<n<100k
tasks:
- named-entity-recognition
- token-classification
pretty_name: BotanicalNER
tags:
- named-entity-recognition
- botany
- multilingual
configs:
- config_name: botlit_de
  data_files:
  - split: train
    path: botlit_de/*
  default: true
- config_name: botlit_en
  data_files:
  - split: train
    path: botlit_en/*
- config_name: plantblog_de
  data_files:
  - split: train
    path: plantblog_de/*
- config_name: plantblog_en
  data_files:
  - split: train
    path: plantblog_en/*
- config_name: textberg_de
  data_files:
  - split: train
    path: textberg_de/*
- config_name: textberg_en
  data_files:
  - split: train
    path: textberg_en/*
- config_name: wiki_de
  data_files:
  - split: train
    path: wiki_de/*
- config_name: wiki_en
  data_files:
  - split: train
    path: wiki_en/*
- config_name: gold_de
  data_files:
  - split: test
    path: gold_de/*
- config_name: gold_en
  data_files:
  - split: test
    path: gold_en/*
- config_name: fungi_de
  data_files:
  - split: test
    path: fungi_de/*
- config_name: fungi_en
  data_files:
  - split: test
    path: fungi_en/*
dataset_info:
- config_name: botlit_de
  features:
  - name: id
    dtype: string
  - name: tokens
    sequence: string
  - name: pos_tags
    sequence: string
  - name: ner_tags
    sequence:
      class_label:
        names:
          '0': O
          '1': B-Scientific
          '2': I-Scientific
          '3': B-Vernacular
          '4': I-Vernacular
  splits:
  - name: train
    num_bytes: 100000 # Placeholder
    num_examples: 1000 # Placeholder
# Note: The Hub will auto-populate dataset_info for all other configs.
---

# Dataset Card for BotanicalNER

## Table of Contents
- [Dataset Card for BotanicalNER](#dataset-card-for-botanicalner)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)

## Dataset Description

- **Homepage:** [https://github.com/IsabelMeraner/BotanicalNER](https://github.com/IsabelMeraner/BotanicalNER)
- **Paper:** Meraner, I. 2019. *Grasping the Nettle: Neural Entity Recognition for Scientific and Vernacular Plant Names*. Master Thesis, Institute of Computational Linguistics, University of Zurich.
- **Point of Contact:** Isabel Meraner

### Dataset Summary

**BotanicalNER** is a German-English parallel dataset for Named Entity Recognition (NER) of scientific and vernacular plant names. The resources were created for the master thesis project "Grasping the Nettle" at the University of Zurich in 2019.

The main focus of the project was to identify and disambiguate scientific and vernacular plant names across multiple text genres to provide a valuable tool for extracting and preserving (ethno-)botanical knowledge. The dataset is structured into several sub-corpora from different domains, which are available as separate configurations:

-   `botlit`: Botanical literature
-   `plantblog`: Plant-themed blog posts
-   `wiki`: Wikipedia abstracts
-   `textberg`: The TextBerg corpus of Alpine Club yearbooks
-   `gold`: Gold-standard test sets
-   `fungi`: A specialized test set for fungi names

### Supported Tasks and Leaderboards

-   **Tasks**: Named Entity Recognition, Token Classification
-   **Leaderboards**: N/A

### Languages

The dataset contains texts in German (`de`) and English (`en`).

## Dataset Structure

The dataset is composed of multiple configurations, one for each sub-corpus and language (e.g., `botlit_de`, `wiki_en`, `gold_de`).

### Data Instances

An example from the `gold_de` configuration:

```json
{
  "id": "0",
  "tokens": ["Die", "Brennnessel", "(", "Urtica", "dioica", ")", "ist", "eine", "wichtige", "Heilpflanze", "."],
  "pos_tags": ["ART", "NN", "$(", "NE", "NE", "$)", "VAFIN", "ART", "ADJA", "NN", "$."],
  "ner_tags": [0, 3, 0, 1, 2, 0, 0, 0, 0, 0, 0]
}
```

### Data Fields

All configurations share the same data fields:
-   `id`: A unique identifier for the example, a `string` feature.
-   `tokens`: The list of tokens in the sentence, a `Sequence` of `string` features.
-   `pos_tags`: The list of part-of-speech tags, a `Sequence` of `string` features.
-   `ner_tags`: The list of NER tags, a `Sequence` of `ClassLabel` features. The mapping from ID to tag is as follows:

```json
{
    "0": "O",
    "1": "B-Scientific",
    "2": "I-Scientific",
    "3": "B-Vernacular",
    "4": "I-Vernacular"
}
```

### Data Splits

The data is provided as several distinct corpora, loaded via configurations.
-   The `botlit`, `plantblog`, `textberg`, and `wiki` configurations each contain a **train** split composed of silver-standard data.
-   The `gold` and `fungi` configurations each contain a **test** split composed of gold-standard data.

## Dataset Creation

### Curation Rationale

The project aimed to create a robust tool for extracting plant names from diverse texts, which is a crucial step for digitizing and preserving botanical and ethno-botanical knowledge.

### Source Data

The data was collected from various sources, tokenized, and POS-tagged using TreeTagger.

### Annotations

The dataset was annotated using a dictionary-based method (silver standard) and manual correction (gold standard). The annotation scheme is IOB (Inside, Outside, Beginning) for two entity types: `Scientific` and `Vernacular` plant names.

### Personal and Sensitive Information

The dataset does not contain personal or sensitive information as it focuses on botanical and scientific content.

## Considerations for Using the Data

### Social Impact of Dataset

This dataset can have a positive social impact by enabling better extraction of botanical knowledge, supporting biodiversity research, and assisting in the preservation of ethnobotanical knowledge.

### Discussion of Biases

Potential biases include geographic bias in plant names, source bias from different text genres, and domain bias from the specific sources used. Most training data also uses automatic annotation which may contain errors.

## Additional Information

### Dataset Curators

Isabel Meraner, Institute of Computational Linguistics, University of Zurich (2019).

### Licensing Information

The dataset is licensed under the GNU General Public License v3.0 (GPL-3.0).

### Citation Information

```bibtex
@mastersthesis{meraner2019grasping,
    title={Grasping the Nettle: Neural Entity Recognition for Scientific and Vernacular Plant Names},
    author={Meraner, Isabel},
    year={2019},
    school={Institute of Computational Linguistics, University of Zurich}
}
```