Papers with Named-Entity Recognition
NepBERTa: Nepali Language Model Trained in a Large Corpus (2022.aacl-short)
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| Challenge: | Nepali is a low-resource language with more than 40 million speakers worldwide. |
| Approach: | They present a BERT-based natural language understanding model trained on the most extensive monolingual Nepali corpus ever. |
| Outcome: | The proposed model performs well in Nepali-specific NLP tasks including Named-Entity Recognition, Content Classification, POS Tagging, and Sequence Pair Similarity. |
Robust Lexical Features for Improved Neural Network Named-Entity Recognition (C18-1)
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| Challenge: | Named-Entity Recognition (NER) uses word embeddings to extend, rather than replace, hand-crafted features. |
| Approach: | They propose to embed words and entity types into a low-dimensional vector space and compute a feature vector representing each word offline. |
| Outcome: | The proposed representations outperform existing models and achieve state-of-the-art performance. |
Extracting Person Names from User Generated Text: Named-Entity Recognition for Combating Human Trafficking (2022.findings-acl)
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| Challenge: | Existing methods for Named-Entity Recognition (NER) on escort ads are not sufficient to extract person names from the text of the ad. |
| Approach: | They propose to use a model to extract person names from escort ads to capture ambiguous names and adapt to adversarial changes in the text. |
| Outcome: | The proposed model shows 19% improvement on average in the F1 classification score compared to previous state-of-the-art in two domain-specific datasets. |
Character-Level Feature Extraction with Densely Connected Networks (C18-1)
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| Challenge: | Existing methods to generate character-level features with neural architectures such as CNN or Recurrent Neural Network (RNN) are slow and generate position-independent features. |
| Approach: | They propose a method that uses a densely connected network to extract character-level features from words using CNN and RNN. |
| Outcome: | The proposed method shows robustness and effectiveness while being faster than CNN- or RNN-based methods. |
Identifying Motion Entities in Natural Language and A Case Study for Named Entity Recognition (2020.coling-main)
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| Challenge: | Identifying motion entities in text is not only challenging but beneficial for a better natural language understanding. |
| Approach: | They propose a Motion Entity Tagging model to identify entities in motion in a text using the Literal-Motion-in-Text dataset for training and evaluating the model. |
| Outcome: | The proposed method improves the Named-Entity Recognition task by splitting clauses and phrases from complex and long motion sentences. |
Separating Retention from Extraction in the Evaluation of End-to-end Relation Extraction (2021.emnlp-main)
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| Challenge: | State-of-the-art NLP models adopt shallow heuristics that limit their generalization capability. |
| Approach: | They propose to use heuristics that limit their generalization capability to model lexical overlap with the training set in Named-Entity Recognition and Event or Type heuristic in Relation Extraction to test their models. |
| Outcome: | The proposed model can perform better on the two key tasks, while the retention of training relation triples. |