Dawn Lawrie


2020

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Building OCR/NER Test Collections
Dawn Lawrie | James Mayfield | David Etter
Proceedings of the 12th Language Resources and Evaluation Conference

Named entity recognition (NER) identifies spans of text that contain names. Many researchers have reported the results of NER on text created through optical character recognition (OCR) over the past two decades. Unfortunately, the test collections that support this research are annotated with named entities after optical character recognition (OCR) has been run. This means that the collection must be re-annotated if the OCR output changes. Instead by tying annotations to character locations on the page, a collection can be built that supports OCR and NER research without requiring re-annotation when either improves. This means that named entities are annotated on the transcribed text. The transcribed text is all that is needed to evaluate the performance of OCR. For NER evaluation, the tagged OCR output is aligned to the transcriptions the aligned files, creating modified files of each, which are scored. This paper presents a methodology for building such a test collection and releases a collection of Chinese OCR-NER data constructed using the methodology. The paper provides performance baselines for current OCR and NER systems applied to this new collection.

2017

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CADET: Computer Assisted Discovery Extraction and Translation
Benjamin Van Durme | Tom Lippincott | Kevin Duh | Deana Burchfield | Adam Poliak | Cash Costello | Tim Finin | Scott Miller | James Mayfield | Philipp Koehn | Craig Harman | Dawn Lawrie | Chandler May | Max Thomas | Annabelle Carrell | Julianne Chaloux | Tongfei Chen | Alex Comerford | Mark Dredze | Benjamin Glass | Shudong Hao | Patrick Martin | Pushpendre Rastogi | Rashmi Sankepally | Travis Wolfe | Ying-Ying Tran | Ted Zhang
Proceedings of the IJCNLP 2017, System Demonstrations

Computer Assisted Discovery Extraction and Translation (CADET) is a workbench for helping knowledge workers find, label, and translate documents of interest. It combines a multitude of analytics together with a flexible environment for customizing the workflow for different users. This open-source framework allows for easy development of new research prototypes using a micro-service architecture based atop Docker and Apache Thrift.

2013

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KELVIN: a tool for automated knowledge base construction
Paul McNamee | James Mayfield | Tim Finin | Tim Oates | Dawn Lawrie | Tan Xu | Douglas Oard
Proceedings of the 2013 NAACL HLT Demonstration Session

2012

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Creating and Curating a Cross-Language Person-Entity Linking Collection
Dawn Lawrie | James Mayfield | Paul McNamee | Douglas Oard
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

To stimulate research in cross-language entity linking, we present a new test collection for evaluating the accuracy of cross-language entity linking in twenty-one languages. This paper describes an efficient way to create and curate such a collection, judiciously exploiting existing language resources. Queries are created by semi-automatically identifying person names on the English side of a parallel corpus, using judgments obtained through crowdsourcing to identify the entity corresponding to the name, and projecting the English name onto the non-English document using word alignments. Name projections are then curated, again through crowdsourcing. This technique resulted in the first publicly available multilingual cross-language entity linking collection. The collection includes approximately 55,000 queries, comprising between 875 and 4,329 queries for each of twenty-one non-English languages.

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A Context-Aware Approach to Entity Linking
Veselin Stoyanov | James Mayfield | Tan Xu | Douglas Oard | Dawn Lawrie | Tim Oates | Tim Finin
Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction (AKBC-WEKEX)

2011

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Cross-Language Entity Linking
Paul McNamee | James Mayfield | Dawn Lawrie | Douglas Oard | David Doermann
Proceedings of 5th International Joint Conference on Natural Language Processing