Papers by Felix Hamborg
Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognition (2024.eacl-long)
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| Challenge: | Few-shot named entity recognition (NER) uses only a few annotated examples to identify named entities within text. |
| Approach: | They propose to leverage natural language descriptions of each entity type to perform few-shot named entity recognition. |
| Outcome: | The proposed model learns to interpret verbalized descriptions of entities using natural language descriptions of their types and their verbalizations. |
Towards Evaluation of Cross-document Coreference Resolution Models Using Datasets with Diverse Annotation Schemes (2022.lrec-1)
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| Challenge: | Existing cross-document coreference resolution (CDCR) datasets contain event-centric coreference chains of events and entities with identity relations. |
| Approach: | They propose to use a phrasing diversity metric to evaluate lexical diversity of CDCR datasets . they propose to combine CDCR annotation schemes with multiple properties of the coreference chains . |
| Outcome: | The proposed phrasing diversity metric evaluates the CDCR datasets with higher precision. |
NewsMTSC: A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles (2021.eacl-main)
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| Challenge: | Previous work on target-dependent sentiment classification (TSC) has focused on reviews, social media, and other domains where authors tend to express their opinions explicitly. |
| Approach: | They propose a high-quality dataset for TSC on news articles with key differences compared to established datasets. |
| Outcome: | The proposed model improves the state-of-the-art from 81.7 to 83.1 (real-world sentiment distribution) and 82.5 (multi-target sentences) compared to established datasets. |
Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMs (2023.emnlp-demo)
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| Challenge: | Recent research addresses the bottleneck of producing labeled training data for NLP tasks. |
| Approach: | They propose a method that generates labeled data that can be used to train a downstream NLP model. |
| Outcome: | The proposed model enables an LLM to generate labeled data that can be used to train a downstream NLP model. |
Media Bias, the Social Sciences, and NLP: Automating Frame Analyses to Identify Bias by Word Choice and Labeling (2020.acl-srw)
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| Challenge: | slanted news coverage can have negative effects on individuals and society . a system that helps readers to become aware of the differences in media coverage caused by bias is being developed. |
| Approach: | They propose to use natural language processing and deep learning to identify instances of WCL bias and estimate the frames they induce. |
| Outcome: | The proposed system can identify instances of WCL bias and estimate the frames they induce. |
Less is More: Parameter-Efficient Selection of Intermediate Tasks for Transfer Learning (2024.emnlp-main)
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| Challenge: | Prior methods producing useful task rankings are infeasible for large source pools . Embedding space maps (ESMs) reduce execution time and disk space usage . |
| Approach: | They introduce Embedded Space Maps (ESMs) that approximate the effect of fine-tuning a language model. |
| Outcome: | The proposed method reduces execution time and disk space usage by 10 and 278, respectively, while retaining high selection performance. |