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Chris Callison-Burch

Associate Professor of Computer and Information Science at University of Pennsylvania
Person human Q93953917

Facts

10 properties
Instance of
human
Sex or gender
male[8]
Occupation
computational linguist, university teacher, academic
Employer
Johns Hopkins University (2007–2013), University of Pennsylvania (2013–present)[7]
Field of work
computational linguistics, natural language processing, artificial intelligence[4][5][6]
Doctoral student
Anne Cocos[9]
Languages spoken, written or signed
English[13]
Family name
Callison, Burch[10][11]
Github username
callison-burch
Given name
Chris[12]
🏷️ Also known as
en Chris Callison-Burch
🔖 Identifiers · 8
ORCID iD
0000-0001-8196-1943
OpenAlex ID
A5068508539, A5140683410as of 2026-09-23
where from?
Source
OpenAlex
Edition
20260923 · as of 2026-09-23
Bronze
not in the catalog
Transform
4ort-mint · openalex:20260923 · version not recorded
DBLP author ID
78/1408
GitHub account
callison-burch
Google Knowledge Graph ID
/g/11gbhm49_f
Google Scholar author ID
nv-MV58AAAAJ
LinkedIn personal profile ID
chris-callison-burch-40bb87b7
OpenReview.net profile ID
Chris_Callison-Burch1

Report an error or request removal Lineage (PROV-JSON)

📜 Data licence
Source: OpenAlex (openalex.org), snapshot of 2026-09-23. CC0 1.0 Universal. CC0 1.0 Universal · OpenAlex
🔗 Connections
Author of 49
A Comparison of Context-sensitive Models for Lexical Substitution, A Comprehensive Analysis of Bilingual Lexicon Induction, Adding Semantics to Data-Driven Paraphrasing, A deep learning method to detect opioid prescription and opioid use disorder from electronic health records, A Feasibility Study of Answer-Unaware Question Generation for Education, Automatically Generated Summaries of Video Lectures May Enhance Students’ Learning Experience, BiSECT: Learning to Split and Rephrase Sentences with Bitexts, Calibrating Large Language Models with Sample Consistency +41 more
← Authored by 49
A Comparison of Context-sensitive Models for Lexical Substitution, A Comprehensive Analysis of Bilingual Lexicon Induction, Adding Semantics to Data-Driven Paraphrasing, A deep learning method to detect opioid prescription and opioid use disorder from electronic health records, A Feasibility Study of Answer-Unaware Question Generation for Education, Automatically Generated Summaries of Video Lectures May Enhance Students’ Learning Experience, BiSECT: Learning to Split and Rephrase Sentences with Bitexts, Calibrating Large Language Models with Sample Consistency +41 more
Affiliated with 10
Google, University of Pennsylvania, Paris-Saclay University, University of Illinois Urbana-Champaign, National Center for Scientific Research, Paris Cité University, Children's Hospital of Philadelphia, PennWest California +2 more
Field of work 5
artificial intelligence, computer science, natural language processing, computational linguistics, artificial intelligence model
Occupation 3
university teacher, academic, computational linguist
Employer 2
University of Pennsylvania, Johns Hopkins University
Edited works 1
Computational Linguistics
Given name 1
Chris
Instance of 1
human
Languages spoken, written or signed 1
English
Sex or gender 1
male
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Summary

Chris Callison-Burch worked as a computational linguist[1], university teacher[2], and academic[3].

Body

Career and Affiliations

Fields of work include computational linguistics[4], an interdisciplinary science[14]; natural language processing[5], an academic discipline[15]; and artificial intelligence[6], a type of technology[16].

FAQs

What did Chris Callison-Burch do for work?

Chris Callison-Burch worked as computational linguist[1], university teacher[2], and academic[3].

Related Entities

artificial intelligence artificial intelligence model computational linguistics computer science Google Johns Hopkins University National Center for Scientific Research natural language processing PennWest California University of Illinois Urbana-Champaign Vector Institute

References

Programmatic citations — every numbered marker resolves to a verifiable graph row below.

Direct Wikidata claims

  1. [8] ↑ . wikidata.org.
  2. [4] ↑ . mitpressjournals.org. Retrieved . mitpressjournals.org. Provenance: wikidata.org.
  3. [5] ↑ . cis.upenn.edu. Retrieved . cis.upenn.edu. Provenance: wikidata.org.
  4. [6] ↑ . cis.upenn.edu. Retrieved . cis.upenn.edu. Provenance: wikidata.org.
  5. [1] ↑ . mitpressjournals.org. Retrieved . mitpressjournals.org. Provenance: wikidata.org.
  6. [2] ↑ . cis.upenn.edu. Retrieved . cis.upenn.edu. Provenance: wikidata.org.
  7. [3] ↑ . cis.upenn.edu. Retrieved . cis.upenn.edu. Provenance: wikidata.org.
  8. [7] ↑ . cis.upenn.edu. cis.upenn.edu. Provenance: wikidata.org.
  9. [9] ↑ . OpenReview. wikidata.org.
  10. [10] ↑ . wikidata.org.
  11. [11] ↑ . wikidata.org.
  12. [12] ↑ . wikidata.org.
  13. [13] ↑ . wikidata.org.

Inline context (facts about related entities)

  1. [14] ↑ . Wikidata. wikidata.org. → on this site
  2. [15] ↑ . Wikidata. wikidata.org. → on this site
  3. [16] ↑ . Wikidata. wikidata.org. → on this site

📑 Cite this page

Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.

APA 4ort.xyz Knowledge Graph. (2026). Chris Callison-Burch. Retrieved November 2, 2026, from https://4ort.xyz/entity/chris-callison-burch
MLA “Chris Callison-Burch.” 4ort.xyz Knowledge Graph, 4ort.xyz, 2 Nov. 2026, https://4ort.xyz/entity/chris-callison-burch.
BibTeX @misc{4ortxyz_chris-callison-burch_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Chris Callison-Burch}}, year = {2026}, url = {https://4ort.xyz/entity/chris-callison-burch}, note = {Accessed: 2026-11-02}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Chris Callison-Burch — https://4ort.xyz/entity/chris-callison-burch (retrieved 2026-11-02)

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