| Challenge: | Entity Disambiguation (ED) is a crucial problem in Natural Language Processing (NLP). |
| Approach: | They propose to use Wikipedia titles as the textual representation of each candidate to improve the generalization capability over unseen patterns. |
| Outcome: | The proposed model improves on 2 out of 6 benchmarks and is generalized over unseen patterns. |
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ExtEnD: Extractive Entity Disambiguation (2022.acl-long)
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| Challenge: | Entity disambiguation (ED) is a task in natural language processing that requires a large pre-trained language model to perform. |
| Approach: | They propose a local formulation for Entity Disambiguation (ED) that frames this task as a text extraction problem and propose two Transformer-based architectures that implement it. |
| Outcome: | The proposed model outperforms all its competitors in terms of data efficiency and raw performance on 4 out of 4 benchmarks. |
Entity Disambiguation via Fusion Entity Decoding (2024.naacl-long)
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Junxiong Wang, Ali Mousavi, Omar Attia, Ronak Pradeep, Saloni Potdar, Alexander Rush, Umar Farooq Minhas, Yunyao Li
| Challenge: | Existing generative approaches demonstrate improved accuracy compared to classification approaches under the standardized ZELDA benchmark. |
| Approach: | They propose an encoder-decoder model to disambiguate entities with more detailed entity descriptions. |
| Outcome: | The proposed model outperforms existing classification models on the ZELDA benchmark and on retrieval/reader frameworks. |
ZELDA: A Comprehensive Benchmark for Supervised Entity Disambiguation (2023.eacl-main)
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| Challenge: | Entity disambiguation (ED) is the task of disambiguating named entity mentions in text to unique entries in a knowledge base. |
| Approach: | They propose a benchmark for entity disambiguation that includes a unified training data set, entity vocabulary, candidate lists and challenging evaluation splits covering 8 different domains. |
| Outcome: | The proposed benchmark is based on a unified training data set, entity vocabulary, candidate lists and evaluation splits covering 8 different domains. |
Improving Entity Disambiguation by Reasoning over a Knowledge Base (2022.naacl-main)
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| Challenge: | Recent work in entity disambiguation relies on a limited subset of KB facts to link entities . less common entities are prone to missing or inconsistent KB information, which is problematic for models which rely on 'one source' |
| Approach: | They propose an ED model which links entities by reasoning over a symbolic knowledge base in a fully differentiable fashion. |
| Outcome: | The proposed model outperforms state-of-the-art models on six well-established datasets by 1.3 F1 on average. |
Entity Embedding Completion for Wide-Coverage Entity Disambiguation (2022.findings-emnlp)
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| Challenge: | Existing state-of-the-art ED models do not address out-of vocabulary entities that are absent from training data. |
| Approach: | They propose to extend a state-of-the-art ED model by dynamically computing embeddings of out-ofvocabulary entities by using entity descriptions and mention contexts. |
| Outcome: | The proposed model performs comparable to existing models whose embeddings are trained for all candidate entities as well as embedd-free models. |
Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extraction (2024.findings-emnlp)
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| Challenge: | Existing approaches to relation extraction use concatenating embeddings of head and tail entities . however, such representations capture the types of the entities involved, leading to false positives and confusion between relations involving entities of the same type. |
| Approach: | They propose a model which combines [MASK] embeddings with entity embedds to learn relation embeddations. |
| Outcome: | The proposed model outperforms the state-of-the-art on several benchmarks . it uses a self-supervised pre-training strategy which further improves the results. |
Global Entity Disambiguation with BERT (2022.naacl-main)
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| Challenge: | Entity disambiguation (ED) is a task of assigning mentions to referent entities in a knowledge base. |
| Approach: | They propose a global entity disambiguation (ED) model based on BERT . they train the model using a large entity-annotated corpus obtained from Wikipedia . |
| Outcome: | The proposed model can disambiguate masked entities based on words and non-masked ones at the inference time. |
Improving Neural Entity Disambiguation with Graph Embeddings (P19-2)
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| Challenge: | Entity Disambiguation (ED) is the task of linking an ambiguous entity mention to a corresponding entry in a knowledge base. |
| Approach: | They propose a method that integrates structured information from the knowledge base with unstructured information from text-based representations. |
| Outcome: | The proposed method improves on a graph of hyperlinks between Wikipedia articles and a state-of-the-art neural ED model. |
Towards Entity Spaces (2020.lrec-1)
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| Challenge: | Entities are a central element of knowledge bases and are used in many knowledge-centric tasks including text analysis. |
| Approach: | They propose to use entity spaces to represent a set of associated entities with near-identity to provide a handle to an amorphous grouping of entities. |
| Outcome: | The proposed representations improve recall of entity linking in English by using disambiguation pages. |
Recognizing Complex Entity Mentions: A Review and Future Directions (P18-3)
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| Challenge: | Named entity recognition (NER) is a task of identifying and classifying named entities (NE) within text. |
| Approach: | They review existing methods for identifying and classifying named entities within text . they identify the research gap and propose a new approach to tackle these problems . |
| Outcome: | The proposed methods address the identified identified gaps in the literature and provide recommendations for future work. |