Steven Bethard
E857264
Steven Bethard is a computer scientist and natural language processing researcher known for his work on temporal information extraction, semantic role labeling, and clinical NLP.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Steven Bethard canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T10344871 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Steven Bethard Context triple: [Nathaniel Chambers, coAuthorWith, Steven Bethard]
-
A.
Kent McCord
Kent McCord is an American actor best known for his role as Officer Jim Reed on the television series "Adam-12."
-
B.
Ben Donich
Ben Donich is a mountain in the Arrochar Alps of the Scottish Highlands, popular with hikers for its accessible ascent and panoramic views.
-
C.
Matthew C. Brown
Matthew C. Brown is a film producer known for his work on the horror movie "Spectral."
-
D.
Matthew Shafer
Matthew Shafer is an American writer known for his work on the animated series "Cowboy Bebop" and related projects.
-
E.
Matthew Shafer
Matthew Shafer, better known by his stage name Uncle Kracker, is an American singer-songwriter and musician recognized for his blend of rock, country, and pop influences.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Steven Bethard Target entity description: Steven Bethard is a computer scientist and natural language processing researcher known for his work on temporal information extraction, semantic role labeling, and clinical NLP.
-
A.
Kent McCord
Kent McCord is an American actor best known for his role as Officer Jim Reed on the television series "Adam-12."
-
B.
Ben Donich
Ben Donich is a mountain in the Arrochar Alps of the Scottish Highlands, popular with hikers for its accessible ascent and panoramic views.
-
C.
Matthew C. Brown
Matthew C. Brown is a film producer known for his work on the horror movie "Spectral."
-
D.
Matthew Shafer
Matthew Shafer is an American writer known for his work on the animated series "Cowboy Bebop" and related projects.
-
E.
Matthew Shafer
Matthew Shafer, better known by his stage name Uncle Kracker, is an American singer-songwriter and musician recognized for his blend of rock, country, and pop influences.
- F. None of above. chosen
Statements (46)
| Predicate | Object |
|---|---|
| instanceOf |
computer scientist
ⓘ
natural language processing researcher ⓘ |
| affiliation | Association for Computational Linguistics NERFINISHED ⓘ |
| citizenship |
United States of America
ⓘ
surface form:
United States
|
| coAuthored |
“ClearTK: A UIMA toolkit for statistical natural language processing”
NERFINISHED
ⓘ
“Identifying Temporal Relations in Text: Annotation Scheme and Corpus” NERFINISHED ⓘ “SEMAFOR: Semantic Frame Parsing with Machine Learning” NERFINISHED ⓘ “Temporal Annotation in the Clinical Domain” NERFINISHED ⓘ |
| developed | ClearTK NLP toolkit NERFINISHED ⓘ |
| educatedAt | University of Colorado Boulder NERFINISHED ⓘ |
| employer | University of Arizona NERFINISHED ⓘ |
| fieldOfWork |
clinical natural language processing
ⓘ
computational linguistics ⓘ natural language processing ⓘ semantic role labeling ⓘ temporal information extraction ⓘ |
| hasAcademicAdvisor |
James H. Martin
NERFINISHED
ⓘ
Martha Palmer NERFINISHED ⓘ |
| hasAcademicPosition | associate professor at University of Arizona ⓘ |
| hasGoogleScholarProfile | https://scholar.google.com/citations?user=0sKQK1cAAAAJ ⓘ |
| hasHIndex | 40+ ⓘ |
| hasHomepage | https://bethard.faculty.arizona.edu/ ⓘ |
| hasORCID | 0000-0002-0795-8940 ⓘ |
| hasResearchArea |
clinical temporal reasoning
ⓘ
semantic role labeling for PropBank and FrameNet ⓘ temporal annotation standards ⓘ |
| hasRole | organizer of clinical NLP shared tasks ⓘ |
| hasTaughtCourse |
machine learning
ⓘ
natural language processing ⓘ text mining ⓘ |
| knownFor |
clinical NLP research
ⓘ
semantic role labeling research ⓘ temporal information extraction research ⓘ |
| language | English ⓘ |
| memberOf | University of Arizona Department of Computer Science NERFINISHED ⓘ |
| organized | SemEval shared tasks on temporal relations in text NERFINISHED ⓘ |
| participatedIn | SemEval shared tasks on temporal information processing NERFINISHED ⓘ |
| researchInterest |
clinical text processing
ⓘ
information extraction ⓘ machine learning for NLP ⓘ semantic parsing ⓘ temporal reasoning ⓘ |
| reviewerFor |
ACL conferences
NERFINISHED
ⓘ
Computational Linguistics journal NERFINISHED ⓘ EMNLP conferences NERFINISHED ⓘ Journal of Biomedical Informatics NERFINISHED ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Steven Bethard Description of subject: Steven Bethard is a computer scientist and natural language processing researcher known for his work on temporal information extraction, semantic role labeling, and clinical NLP.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.