ACTER is a collection of manually annotated corpora for term extraction whose terms and/or named entities were labelled from the following domains: corruption, wind energy, equitation, and heart failure and are available in English, French, and Dutch.
- The original corpora (v.1.1-v1.5) are available at AylaRT/ACTER.
Meanwhile, RSDO5 is a manually annotated corpus for Slovenian language in the domain of biomechanics, linguistics, chemistry, or veterinary science.
- The original version of RSDO (v1.0, v1.1) corpus is available at Corpus of term-annotated texts RSDO5.
In this repo, we provide the annotated corpora in NOBI annotation format with additional information about term position and extracted term list per sentence for both datasets.
- Creator: @honghanhh
- Association: Jozef Stefan Institute, Slovenia and La Rochelle University, France
- Version: ACTER v1.6, RSDO5 v1.2
- Date of creation version 1.6: 10/10/2022
- Date of last update: 27/01/2023
- Contact: @honghanhh
- Source: https://github.com/honghanhh/NOBI_ACTER
- License: CC BY-NC-SA 4.0
| Abbreviation | Meaning |
|---|---|
| ACTER | Annotated Corpora for Term Extraction Research |
| RSDO | Corpus of term-annotated texts RSDO5 1.0 |
| BIO | Begin, Inside, Outside annotation |
| NOBI | Nested BIO annotation |
| en | English |
| fr | French |
| nl | Dutch |
| corp | Corruption |
| equi | Equitation (dressage) |
| htfl | Heart failure |
| wind | Wind energy |
.
├── ACTER
│ ├── en
│ │ ├── with_named_entities
│ │ │ ├── en_corp_nes.csv
│ │ │ ├── en_equi_nes.csv
│ │ │ ├── en_htfl_nes.csv
│ │ │ └── en_wind_nes.csv
│ │ └── without_named_entities
│ │ ├── en_corp_ann.csv
│ │ ├── en_equi_ann.csv
│ │ ├── en_htfl_ann.csv
│ │ └── en_wind_ann.csv
│ ├── fr
│ │ ├── with_named_entities
│ │ │ ├── fr_corp_nes.csv
│ │ │ ├── fr_equi_nes.csv
│ │ │ ├── fr_htfl_nes.csv
│ │ │ └── fr_wind_nes.csv
│ │ └── without_named_entities
│ │ ├── fr_corp_ann.csv
│ │ ├── fr_equi_ann.csv
│ │ ├── fr_htfl_ann.csv
│ │ └── fr_wind_ann.csv
│ └── nl
│ ├── with_named_entities
│ │ ├── nl_corp_nes.csv
│ │ ├── nl_equi_nes.csv
│ │ ├── nl_htfl_nes.csv
│ │ └── nl_wind_nes.csv
│ └── without_named_entities
│ ├── nl_corp_ann.csv
│ ├── nl_equi_ann.csv
│ ├── nl_htfl_ann.csv
│ └── nl_wind_ann.csv
└── RSDO5
└── sl
├── bim.csv
├── kem.csv
├── ling.csv
└── vet.csv
13 directories, 28 files
The annotations are provided in simple UTF-8 encoded plain text files. No lemmatisation was performed.
Each annotated file (e.g., en_corp_nes.csv) contains the following columns: words, labels, entities, term_list. The words column contains the original text, the labels column contains the NOBI annotation, the entities column contains the position of the terms in the sentence, the term_list column contains the terms extracted from the text. For example, the first 5 row of the en_corp_nes.csv file is as follows:
| words | labels | entities | term_list |
|---|---|---|---|
| ['Protecting', 'the', 'EU', ""'s"", 'financial', 'interests', '-', 'fight', 'against', 'fraud'] | ['O', 'O', 'B-Term', 'O', 'BN-Term', 'I-Term', 'O', 'B-Term', 'I-Term', 'IN-Term'] | {('Term', 2, 2), ('Term', 4, 5), ('Term', 7, 9)} | ['EU', 'financial interests', 'fight against fraud'] |
| ['Since', '1995', ',', 'a', 'convention', 'has', 'been', 'in', 'place', 'which', 'seeks', 'to', 'protect', ',', 'under', 'criminal', 'law', ',', 'the', 'financial', 'interests', 'of', 'the', 'EU', 'and', 'its', 'taxpayers', '.'] | ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'BN-Term', 'IN-Term', 'O', 'O', 'BN-Term', 'I-Term', 'O', 'O', 'B-Term', 'O', 'O', 'B-Term', 'O'] | {('Term', 26, 26), ('Term', 19, 20), ('Term', 15, 16), ('Term', 23, 23)} | ['taxpayers', 'financial interests', 'criminal law', 'EU'] |
| ['Over', 'the', 'years', ',', 'the', 'Convention', 'on', 'the', 'Protection', 'of', 'the', 'European', 'Communities', ""'"", 'Financial', 'Interests', 'has', 'been', 'supplemented', 'by', 'a', 'series', 'of', 'protocols', '.'] | ['O', 'O', 'O', 'O', 'O', 'B-Term', 'I-Term', 'I-Term', 'I-Term', 'I-Term', 'I-Term', 'I-Term', 'I-Term', 'I-Term', 'I-Term', 'I-Term', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O'] | {('Term', 5, 15)} | ["Convention on the Protection of the European Communities ' Financial Interests"] |
| ['ACT'] | ['O'] | {} | [] |
| ['Council', 'Act', 'of', '26', 'July', '1995', 'drawing', 'up', 'the', 'Convention', 'on', 'the', 'protection', 'of', 'the', 'European', 'Communities', ""'"", 'financial', 'interests'] | ['B-Term', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-Term', 'I-Term', 'I-Term', 'I-Term', 'I-Term', 'I-Term', 'I-Term', 'I-Term', 'I-Term', 'IN-Term', 'IN-Term'] | {('Term', 9, 19), ('Term', 0, 0)} | ["Convention on the protection of the European Communities ' financial interests", 'Council'] |
The NOBI annotation is an extension of BIO annotation scheme where we have 5 labels, including 3 existing labels in BIO (B-Term, I-Term, O) with 2 additional labels for single nested term (BN-Term, IN-Term). The B-Term and I-Term labels are used for the beginning and inside of a term, respectively. The BN-Term and IN-Term labels are used for the beginning and inside of a single nested term, respectively. The O label is used for non-term tokens. For example, the following sentence is annotated as follows:
In these examples, the gold standard contains the following terms: stent, bottleneck stent, myocardial, infarction, myocardial infarction, etc. In the NOBI regime, we consider bottleneck stent and stent as two different terms where stent is the nested term of bottleneck stent, in contrast to the BIO scheme, where the model extracts just the bottleneck stent as a term. Similarly, myocardial and infarction are two separate terms that are nested from myocardial infarction. Therefore, an additional label N is added to the label of stent, myocardial, and infarction.
We further provide the position of the terms in the sentence in the entities column and the term_list column contains the terms extracted from the each sentence in each row for futher usage.
- Rigouts Terryn, A., Hoste, V., & Lefever, E. (2022). Tagging Terms in Text: A Supervised Sequential Labelling Approach to Automatic Term Extraction. Terminology. International Journal of Theoretical and Applied Issues in Specialized Communication, 28(1). https://doi.org/10.1075/term.21010.rig.
- ACTER Annotated Corpora for Term Extraction Research, version 1.5
- Jemec Tomazin, M., Trojar, M., Atelšek, S., Fajfar, T., Erjavec, T., Žagar Karer, M.: Corpus of term-annotated texts RSDO5 1.1 (2021), http://hdl.handle.net/11356/1470, slovenian language resource repository CLARIN.SI