| Challenge: | a lack of research on the ability to search through databases of personal and organization name is hindering this area . specialized indexing methods which understand the structure of names are essential to efficient performance. |
| Approach: | They propose a neural solution which provides a 12% performance gain over baselines . they propose specialized indexing methods which understand the structure of names . |
| Outcome: | The proposed solution shows up to 12% performance gain over baselines . the proposed solution is compared against a similar dataset from a different dataset . |
Similar Papers
Recognizing Complex Entity Mentions: A Review and Future Directions (P18-3)
Copied to clipboard
| 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. |
DOCENT: Learning Self-Supervised Entity Representations from Large Document Collections (2021.eacl-main)
Copied to clipboard
Yury Zemlyanskiy, Sudeep Gandhe, Ruining He, Bhargav Kanagal, Anirudh Ravula, Juraj Gottweis, Fei Sha, Ilya Eckstein
| Challenge: | Using pre-trained models, we learn to jointly predict words and entities from multiple text sources without any human supervision. |
| Approach: | They propose to learn rich self-supervised entity representations from large amounts of associated text. |
| Outcome: | The proposed models outperform baseline models on downstream tasks in the TV-Movies domain, and scale to very large corpora. |
Comparing Annotated Datasets for Named Entity Recognition in English Literature (2022.lrec-1)
Copied to clipboard
| Challenge: | Generally speaking, the majority of NER tools struggle to perform well when the entities in the text contain specific characteristics. |
| Approach: | They analysed two existing annotated datasets and two additional gold standard datasets to evaluate the performance of two NER tools. |
| Outcome: | The results show that the performance of two NER tools varies significantly depending on the gold standard used for the individual evaluations. |
Multi-lingual Entity Discovery and Linking (P18-5)
Copied to clipboard
| Challenge: | This tutorial reviews the framework of cross-lingual EL and motivates it as a broad paradigm for the Information Extraction task. |
| Approach: | This tutorial will review the framework of cross-lingual EL and motivate it as a broad paradigm for the Information Extraction task. |
| Outcome: | The aim of this tutorial is to review the framework of cross-lingual EL and motivate it as a broad paradigm for the Information Extraction task. |
A Short Survey on Sense-Annotated Corpora (2020.lrec-1)
Copied to clipboard
| Challenge: | Word Sense Disambiguation (WSD) is a key task in Natural Language Understanding. |
| Approach: | They propose to use sense-annotated corpora for supervised Word Sense Disambiguation. |
| Outcome: | The proposed methods have been compared with knowledge-based approaches and have shown to be more efficient when they are available. |
An Analysis of Simple Data Augmentation for Named Entity Recognition (2020.coling-main)
Copied to clipboard
| Challenge: | Recent studies have focused on using data augmentation techniques on sentence-level and sentence-pair natural language processing tasks such as text classification. |
| Approach: | They propose to use data augmentation techniques for named entity recognition to increase model performance. |
| Outcome: | The proposed techniques boost performance for both recurrent and transformer-based models, especially for small training sets. |
Learning from Context or Names? An Empirical Study on Neural Relation Extraction (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing datasets may leak shallow heuristics via entity mentions, thus contributing to the high performance on RE benchmarks. |
| Approach: | They propose an entity-masked contrastive framework for relation extraction to gain a deeper understanding on textual context and type information while avoiding rote memorization of entities. |
| Outcome: | The proposed framework improves the effectiveness and robustness of neural models in different RE scenarios. |
Investigation on Data Adaptation Techniques for Neural Named Entity Recognition (2021.acl-srw)
Copied to clipboard
| Challenge: | Existing methods for named entity recognition use only a limited number of samples . data augmentation and selftraining are popular methods to generate additional synthetic data . |
| Approach: | They investigate the impact of data augmentation and data augmented on named entity recognition tasks. |
| Outcome: | The proposed methods improve the performance of three named entity recognition tasks. |
NERetrieve: Dataset for Next Generation Named Entity Recognition and Retrieval (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) is a widely adopted NLP task . authors present three variants of NER task, with dataset to support them . |
| Approach: | They propose three variants of the NER task, together with a dataset to support them . they propose a move towards more fine-grained entities and zero-shot recognition . |
| Outcome: | The proposed model matches or surpasses existing models in NER tasks . the proposed model is based on a large, silver-annotated corpus of 4 million paragraphs . |
Joint Neural Entity Disambiguation with Output Space Search (C18-1)
Copied to clipboard
| Challenge: | Existing models for entity disambiguation combine local contextual information and global evidences. |
| Approach: | They propose a limited discrepancy search model that combines local contextual information and global evidences to improve a local solution from a global view point. |
| Outcome: | The proposed model improves local and global solutions on CoNLL 2003 and TAC 2010 benchmarks. |