William de Beaumont

Also published as: Will de Beaumont


From Stance to Concern: Adaptation of Propositional Analysis to New Tasks and Domains
Brodie Mather | Bonnie Dorr | Adam Dalton | William de Beaumont | Owen Rambow | Sonja Schmer-Galunder
Findings of the Association for Computational Linguistics: ACL 2022

We present a generalized paradigm for adaptation of propositional analysis (predicate-argument pairs) to new tasks and domains. We leverage an analogy between stances (belief-driven sentiment) and concerns (topical issues with moral dimensions/endorsements) to produce an explanatory representation. A key contribution is the combination of semi-automatic resource building for extraction of domain-dependent concern types (with 2-4 hours of human labor per domain) and an entirely automatic procedure for extraction of domain-independent moral dimensions and endorsement values. Prudent (automatic) selection of terms from propositional structures for lexical expansion (via semantic similarity) produces new moral dimension lexicons at three levels of granularity beyond a strong baseline lexicon. We develop a ground truth (GT) based on expert annotators and compare our concern detection output to GT, to yield 231% improvement in recall over baseline, with only a 10% loss in precision. F1 yields 66% improvement over baseline and 97.8% of human performance. Our lexically based approach yields large savings over approaches that employ costly human labor and model building. We provide to the community a newly expanded moral dimension/value lexicon, annotation guidelines, and GT.


A Broad-Coverage Deep Semantic Lexicon for Verbs
James Allen | Hannah An | Ritwik Bose | Will de Beaumont | Choh Man Teng
Proceedings of the Twelfth Language Resources and Evaluation Conference

Progress on deep language understanding is inhibited by the lack of a broad coverage lexicon that connects linguistic behavior to ontological concepts and axioms. We have developed COLLIE-V, a deep lexical resource for verbs, with the coverage of WordNet and syntactic and semantic details that meet or exceed existing resources. Bootstrapping from a hand-built lexicon and ontology, new ontological concepts and lexical entries, together with semantic role preferences and entailment axioms, are automatically derived by combining multiple constraints from parsing dictionary definitions and examples. We evaluated the accuracy of the technique along a number of different dimensions and were able to obtain high accuracy in deriving new concepts and lexical entries. COLLIE-V is publicly available.


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Complex Event Extraction using DRUM
James Allen | Will de Beaumont | Lucian Galescu | Choh Man Teng
Proceedings of BioNLP 15


Automatically Deriving Event Ontologies for a CommonSense Knowledge Base
James Allen | Will de Beaumont | Lucian Galescu | Jansen Orfan | Mary Swift | Choh Man Teng
Proceedings of the 10th International Conference on Computational Semantics (IWCS 2013) – Long Papers


Building Timelines from Narrative Clinical Records: Initial Results Based-on Deep Natural Language Understanding
Hyuckchul Jung | James Allen | Nate Blaylock | William de Beaumont | Lucian Galescu | Mary Swift
Proceedings of BioNLP 2011 Workshop


Deep Semantic Analysis of Text
James F. Allen | Mary Swift | Will de Beaumont
Semantics in Text Processing. STEP 2008 Conference Proceedings


Increasing the coverage of a domain independent dialogue lexicon with VERBNET
Benoit Crabbé | Myroslava O. Dzikovska | William de Beaumont | Mary Swift
Proceedings of the Third Workshop on Scalable Natural Language Understanding


Generic Parsing for Multi-Domain Semantic Interpretation
Myroslava Dzikovska | Mary Swift | James Allen | William de Beaumont
Proceedings of the Ninth International Workshop on Parsing Technology