| Challenge: | Part names are often multi-word terms longer than two words, and there is little consistency in how terms are described in noisy free text. |
| Approach: | They propose an algorithm that exploits statistical, linguistic and machine learning techniques to discover part names in noisy text. |
| Outcome: | The proposed method outperforms existing methods significantly in part name extraction. |
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| Challenge: | Existing approaches to learning from examples are limited due to the vast number of languages, domains and tasks. |
| Approach: | They propose a semi-supervised training procedure that reformulates input examples as cloze-style phrases to help language models understand a given task. |
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Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification (2020.coling-main)
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| Challenge: | Existing approaches to few-shot text classification require domain expertise and an understanding of the language model's abilities to define the mapping between words and labels. |
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Domain adaptation for part-of-speech tagging of noisy user-generated text (N19-1)
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Multi-modal Information Extraction from Text, Semi-structured, and Tabular Data on the Web (2020.acl-tutorials)
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| Challenge: | a tutorial explores the commonalities in the challenges and solutions developed to address information extraction from the World Wide Web. |
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Financial Event Extraction Using Wikipedia-Based Weak Supervision (D19-51)
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Liat Ein-Dor, Ariel Gera, Orith Toledo-Ronen, Alon Halfon, Benjamin Sznajder, Lena Dankin, Yonatan Bilu, Yoav Katz, Noam Slonim
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Handling Normalization Issues for Part-of-Speech Tagging of Online Conversational Text (L18-1)
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Géraldine Damnati, Jeremy Auguste, Alexis Nasr, Delphine Charlet, Johannes Heinecke, Frédéric Béchet
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SpanPredict: Extraction of Predictive Document Spans with Neural Attention (2021.naacl-main)
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| Challenge: | identifying predictive text in clinical notes can be as important as the predictions themselves . identifying specific content in clinical note descriptions may illuminate previously unknown risk factors . |
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Global Relation Embedding for Relation Extraction (N18-1)
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| Challenge: | Existing methods to extract textual relations with distant supervision are limited by their reliance on supervised training data. |
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