Papers by Dane Bell
Grounding Gradable Adjectives through Crowdsourcing (L18-1)
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| Challenge: | Often, texts describe interactions using vague, high-level language . crowdsourcing is expensive and requires extensive literature review and time . |
| Approach: | They propose a method for estimating concrete groundings for a set of gradable adjectives by crowdsourcing human intuitions and fitting a mixed effects model to the text. |
| Outcome: | The proposed model can generalize to unseen data and has a predictive R 2 of 0.632 in general and 0.677 on a subset of high-frequency adjectives. |
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%. |
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. |