Experiments from NER task in Spanish language using two corpora: CoNLL-2002 and Mexican news
The datasets used are described as follows:
The CoNLL-2002 dataset is in the language Spanish and it has four classes under tagging schema IOB (Inside/Outside/Beginning). A Spanish sentence example with IOB schema is:
El presidente Andrés Manuel López Obrador estará viajando a la ciudad de Puebla de Zaragoza este Miércoles .
PERSON: Andrés-PER-B Manuel-PER-I López-PER-I Obrador-PER-I
LOCATION: Puebla-LOC-B de-LOC-I Zaragoza-LOC-I
| No. | Class | IOB Schema | Description |
|---|---|---|---|
| 1 | ORG | B-ORG, I-ORG | Organizations names |
| 2 | PER | B-PER, I-PER | People names |
| 3 | LOC | B-LOC, I-LOC | Location names |
| 4 | MISC | B-MISC, I-MISC | Miscellaneous |
The CoNLL-2002 dataset was split into four partitions.
| No. | Partition | Original | Split | Tokens | Tags |
|---|---|---|---|---|---|
| 1 | Test A | 1915 | 2177 | 9646 | 8 |
| 2 | Test B | 1517 | 1848 | 9086 | 8 |
| 3 | Train | 8323 | 9947 | 26099 | 8 |
| 4 | Ensemble | 11755 | 13972 | 31405 | 8 |
In headings, Original are the original sentences length. Split were splitted to length 50 (tokens).
The plot shows the tags/classes distribution from dataset under IOB schema. In the chart, the x-axis shows the four classes and the y-axis describe the number of entities per class.
The histogram is used to identify the sentences-length from the dataset.
The Mx-news dataset was built on the political news domain using 250 documents, it is in the language Spanish and it has seventeen classes. One annotator used tagging schema IOBES (Inside/Outside/Beginning/End/Single) to labeled this corpus. Using the previous Spanish sentence example with IOBES schema:
El presidente Andrés Manuel López Obrador estará viajando a la ciudad de Puebla de Zaragoza este Miércoles .
PERSON: Andrés-PER-B Manuel-PER-I López-PER-I Obrador-PER-E
LOCATION: Puebla-LOC-B de-LOC-I Zaragoza-LOC-E
DATE: Miércoles-DAT-S
| No. | Class | IOB Schema | Description |
|---|---|---|---|
| 1 | PER | B-PER, I-PER, E-PER, S-PER | People names, aliases and abbreviations |
| 2 | ORG | B-ORG, I-ORG, E-ORG, S-ORG | Organizations, institutions |
| 3 | DAT | B-DAT, I-DAT, E-DAT, S-DAT | Dates on different formats |
| 4 | TIT | B-TIT, I-TIT, E-TIT, S-TIT | Title or position of persons |
| 5 | GPE | B-GPE, I-GPE, E-GPE, S-GPE | Country names, states, cities, municipalities |
| 6 | PEX | B-PEX, I-PEX, E-PEX, S-PEX | Political party names, aliases and abbreviations |
| 7 | TIM | B-TIM, I-TIM, E-TIM, S-TIM | Time expresions |
| 8 | FAC | B-FAC, I-FAC, E-FAC, S-FAC | Facility names |
| 9 | EVT | B-EVT, I-EVT, E-EVT, S-EVT | Event names |
| 10 | ADD | B-ADD, I-ADD, E-ADD, S-ADD | Addresses expressions, URLs and Twitter users |
| 11 | MNY | B-MNY, I-MNY, E-MNY, ----- | Monetary amounts |
| 12 | DOC | B-DOC, I-DOC, E-DOC, S-DOC | Documents, laws, rules |
| 13 | PRO | B-PRO, I-PRO, E-PRO, S-PRO | Product names, brands, application names |
| 14 | PRC | B-PRC, I-PRC, E-PRC, ----- | Percentage expressions |
| 15 | DEM | B-DEM, -----, E-DEM, S-DEM | Geographical or racial origin of people |
| 16 | AGE | B-AGE, I-AGE, E-AGE, ----- | People age |
| 17 | LOC | B-LOC, I-LOC, E-LOC, S-LOC | Locations about regions, rivers, lakes |
The Mx-news dataset was split into four partitions.
| No. | Partition | Original | Split | Tokens | Tags |
|---|---|---|---|---|---|
| 1 | Split I | 1295 | 1666 | 7628 | 63 |
| 2 | Split II | 1295 | 1677 | 7726 | 63 |
| 3 | Split III | 1297 | 1661 | 7664 | 63 |
| 4 | Ensemble | 3888 | 5004 | 13273 | 65 |
In headings, Original are the original sentences length. Split were splitted to length 50 (tokens).
The plot shows the tags/classes distribution from dataset under IOBES schema. In the chart, the x-axis shows the seventeen classes and the y-axis describe the number of entities per class.
Mx-news dataset was reduced to four classes: ORG, PER, LOC, MISC (the fourteen left) is labeled under IOB (Inside/Beginning/Out) schema. The plot shows the tags/classes distribution from dataset under IOB schema. In the chart, the x-axis shows the four classes and the y-axis describe the number of entities per class.
The histogram is used to identify the sentences-length from the dataset. The Natural Language Toolkit was used to split sentences.
The ELMo word embeddings were trained using elmoformanylangs Python library.
Download CoNLL-2002 ELmo embeddings
Download Mx-news ELMo embeddings
The Python libraries used:
- elmoformanylangs==0.0.2
- graphviz==0.10.1
- Keras==2.2.4
- Keras-Applications==1.0.7
- keras-contrib==2.0.8
- Keras-Preprocessing==1.0.9
- matplotlib==3.0.3
- numpy==1.16.2
- seqeval==0.0.10
- scikit-learn==0.20.3
- sklearn-crfsuite==0.3.6
- tensorflow==1.13.1