Papers by Gus Hahn-Powell
Active Learning Design Choices for NER with Transformers (2024.lrec-main)
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Robert Vacareanu, Enrique Noriega-Atala, Gus Hahn-Powell, Marco A. Valenzuela-Escarcega, Mihai Surdeanu
| Challenge: | In the field of natural language processing, active learning is a technique that is used to decide which examples are worth annotating . a number of studies have focused on sequence classification, text classification, question answering, and question answering. |
| Approach: | They propose two different approaches to deal with partially-annotated sentences . they propose an annotation scheme that can be used to train with tokens . |
| Outcome: | The proposed approaches achieve comparable or better performance than sentence-level annotations with a smaller number of annotated tokens. |
Text Annotation Graphs: Annotating Complex Natural Language Phenomena (L18-1)
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| Challenge: | Text Annotation Graphs is a web-based tool for annotating text . it provides functionality for representing complex relationships between words and word phrases . |
| Approach: | They introduce a web-based tool for annotating text, Text Annotation Graphs, or TAG . it provides functionality for representing complex relationships between words and word phrases . |
| Outcome: | The proposed software can represent complex relationships between words and words . it can also be used to find similar structures within the current document or external annotated documents. |
Enabling Search and Collaborative Assembly of Causal Interactions Extracted from Multilingual and Multi-domain Free Text (N19-4)
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George C. G. Barbosa, Zechy Wong, Gus Hahn-Powell, Dane Bell, Rebecca Sharp, Marco A. Valenzuela-Escárcega, Mihai Surdeanu
| Challenge: | a new searchable knowledge graph allows users to search for causal interactions in multiple languages . a recent study shows that search tools are shallow and do not support multilingual research . |
| Approach: | They propose a system that integrates causal interactions into a single searchable knowledge graph. |
| Outcome: | The proposed system extracts over 600 thousand causal statements from 120 thousand Portuguese publications with a precision of 62%. |
A Human-machine Interface for Few-shot Rule Synthesis for Information Extraction (2022.naacl-demo)
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Robert Vacareanu, George C.G. Barbosa, Enrique Noriega-Atala, Gus Hahn-Powell, Rebecca Sharp, Marco A. Valenzuela-Escárcega, Mihai Surdeanu
| Challenge: | Vacareanu et al., 2021) proposes a system that helps users build transparent information extraction models . rule-based methods address the opacity of neural architectures by producing models that are transparent . |
| Approach: | They propose a system that assists a user in constructing transparent information extraction models . the system generates high-precision rules even in a 1-shot setting, they show . |
| Outcome: | The proposed system generates high-precision rules even in a 1-shot setting . it outperforms manually written patterns on a widely-used relation extraction dataset . |
Exploring Interpretability in Event Extraction: Multitask Learning of a Neural Event Classifier and an Explanation Decoder (2020.acl-srw)
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| Challenge: | EE is a key requirement for machine learning in many domains, e.g., legal, medical, finance. |
| Approach: | They propose an interpretable approach for event extraction that jointly trains a classifier and a rule decoder for event processing. |
| Outcome: | The proposed approach can be used for semi-supervised learning and its performance improves when trained on automatically-labeled data generated by a rule-based system. |
Odinson: A Fast Rule-based Information Extraction Framework (2020.lrec-1)
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| Challenge: | Odinson is a rule-based information extraction framework that matches over multiple representations of text in near real time. |
| Approach: | They propose a rule-based information extraction framework that matches patterns over multiple representations of text with a runtime system that operates in near real time. |
| Outcome: | The proposed framework matches a graph traversal in 2.8 seconds in a corpus of over 134 million sentences, nearly 150,000 times faster than its predecessor. |