---
license: cc-by-4.0
language:
- en
datasets:
- CoNLL2003/AIDA
- Wikipedia
- sshavara/AIDA_testc
tags:
- SpEL
- Entity Linking
- Structured Prediction
widget:
- text: "Leicestershire beat Somerset by an innings and 39 runs in two days."
---
## SpEL (Structured prediction for Entity Linking)
SpEL model finetuned on English Wikipedia as well as the training portion of CoNLL2003/AIDA.
It is introduced in the paper [SPEL: Structured Prediction for Entity Linking (EMNLP 2023)](https://arxiv.org/abs/2310.14684).
The code and data are available in [this repository](https://github.com/shavarani/SpEL).
### Usage
The following snippet demonstrates a quick way that SpEL can be used to generate subword-level, word-level, and phrase-level annotations for a sentence.
```python
# download SpEL from https://github.com/shavarani/SpEL
from transformers import AutoTokenizer
from spel.model import SpELAnnotator
from spel.configuration import device
from spel.utils import get_subword_to_word_mapping
from spel.span_annotation import WordAnnotation, PhraseAnnotation
finetuned_after_step = 4
sentence = "Grace Kelly by Mika reached the top of the UK Singles Chart in 2007."
tokenizer = AutoTokenizer.from_pretrained("roberta-base")
# ############################################# LOAD SpEL #############################################################
spel = SpELAnnotator()
spel.init_model_from_scratch(device=device)
if finetuned_after_step == 3:
spel.shrink_classification_head_to_aida(device)
spel.load_checkpoint(None, device=device, load_from_torch_hub=True, finetuned_after_step=finetuned_after_step)
# ############################################# RUN SpEL ##############################################################
inputs = tokenizer(sentence, return_tensors="pt")
token_offsets = list(zip(inputs.encodings[0].tokens,inputs.encodings[0].offsets))
subword_annotations = spel.annotate_subword_ids(inputs.input_ids, k_for_top_k_to_keep=10, token_offsets=token_offsets)
# #################################### CREATE WORD-LEVEL ANNOTATIONS ##################################################
tokens_offsets = token_offsets[1:-1]
subword_annotations = subword_annotations[1:]
word_annotations = [WordAnnotation(subword_annotations[m[0]:m[1]], tokens_offsets[m[0]:m[1]])
for m in get_subword_to_word_mapping(inputs.tokens(), sentence)]
# ################################## CREATE PHRASE-LEVEL ANNOTATIONS ##################################################
phrase_annotations = []
for w in word_annotations:
if not w.annotations:
continue
if phrase_annotations and phrase_annotations[-1].resolved_annotation == w.resolved_annotation:
phrase_annotations[-1].add(w)
else:
phrase_annotations.append(PhraseAnnotation(w))
```
## Evaluation Results
Entity Linking evaluation results of *SpEL* compared to that of the literature over AIDA test sets:
| Approach | EL Micro-F1
test-a | EL Micro-F1
test-b | #params
on GPU | speed
sec/doc |
|-----------------------------------------------------------------|:----------------------:|:----------------------:|:----------------------------------------:|:-----------------:|
| Hoffart et al. (2011) | 72.4 | 72.8 | - | - |
| Kolitsas et al. (2018) | 89.4 | 82.4 | 330.7M | 0.097 |
| Broscheit (2019) | 86.0 | 79.3 | 495.1M | 0.613 |
| Peters et al. (2019) | 82.1 | 73.1 | - | - |
| Martins et al. (2019) | 85.2 | 81.9 | - | - |
| van Hulst et al. (2020) | 83.3 | 82.4 | 19.0M | 0.337 |
| Févry et al. (2020) | 79.7 | 76.7 | - | - |
| Poerner et al. (2020) | 90.8 | 85.0 | 131.1M | - |
| Kannan Ravi et al. (2021) | - | 83.1 | - | - |
| De Cao et al. (2021b) | - | 83.7 | 406.3M | 40.969 |
| De Cao et al. (2021a)
(no mention-specific candidate set) | 61.9 | 49.4 | 124.8M | 0.268 |
| De Cao et al. (2021a)
(using PPRforNED candidate set) | 90.1 | 85.5 | 124.8M | 0.194 |
| Mrini et al. (2022) | - | 85.7 | (train) 811.5M
(test) 406.2M | - |
| Zhang et al. (2022) | - | 85.8 | 1004.3M | - |
| Feng et al. (2022) | - | 86.3 | 157.3M | - |
|
|
|
|
|
|
| **SpEL-base** (no mention-specific candidate set) | 91.3 | 85.5 | 128.9M | 0.084 |
| **SpEL-base** (KB+Yago candidate set) | 90.6 | 85.7 | 128.9M | 0.158 |
| **SpEL-base** (PPRforNED candidate set)
(context-agnostic) | 91.7 | 86.8 | 128.9M | 0.153 |
| **SpEL-base** (PPRforNED candidate set)
(context-aware) | 92.7 | 88.1 | 128.9M | 0.156 |
| **SpEL-large** (no mention-specific candidate set) | 91.6 | 85.8 | 361.1M | 0.273 |
| **SpEL-large** (KB+Yago candidate set) | 90.8 | 85.7 | 361.1M | 0.267 |
| **SpEL-large** (PPRforNED candidate set)
(context-agnostic) | 92.0 | 87.3 | 361.1M | 0.268 |
| **SpEL-large** (PPRforNED candidate set)
(context-aware) | 92.9 | 88.6 | 361.1M | 0.267 |
----
## Citation
If you use SpEL finetuned models or data, please cite our paper:
```
@inproceedings{shavarani2023spel,
title={Sp{EL}: Structured Prediction for Entity Linking},
author={Shavarani, Hassan S. and Sarkar, Anoop},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://arxiv.org/abs/2310.14684}
}
```