Summary of the paper
| Title | Tree-Structured Named Entity Recognition on OCR Data: Analysis, Processing and Results |
|---|---|
| Authors | Marco Dinarelli and Sophie Rosset |
| Abstract | In this paper we deal with named entity detection on data acquired via OCR process on documents dating from 1890. The resulting corpus is very noisy. We perform an analysis to find possible strategies to overcome errors introduced by the OCR process. We propose a preprocessing procedure in three steps to clean data and correct, at least in part, OCR mistakes. The task is made even harder by the complex tree-structure of named entities annotated on data, we solve this problem however by adopting an effective named entity detection system we proposed in previous work. We evaluate our procedure for preprocessing OCR-ized data in two ways: in terms of perplexity and OOV rate of a language model on development and evaluation data, and in terms of the performance of the named entity detection system on the preprocessed data. The preprocessing procedure results to be effective, allowing to improve by a large margin the system we proposed for the official evaluation campaign on Old Press, and allowing to outperform also the best performing system of the evaluation campaign. |
| Topics | Named Entity recognition, Information Extraction, Information Retrieval, Semantics |
Full paper  |
Tree-Structured Named Entity Recognition on OCR Data: Analysis, Processing and Results |
| Bibtex | @InProceedings{DINARELLI12.1046, author = {Marco Dinarelli and Sophie Rosset}, title = {Tree-Structured Named Entity Recognition on OCR Data: Analysis, Processing and Results}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Uğur Doğan and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} } |