Extracting semi-structured text from scientific writing in PDF files is a difficult task that has faced researchers for decades. In the 1990s, this task was largely a computer vision and OCR problem, as PDF files were often the result of scanning printed documents. Today, PDFs have standardized digital typesetting without the need for OCR, but extraction of semi-structured text from these documents remains a nontrivial task. In this paper, we present a system for the reanalysis of glyph-level PDF extracted text that performs block detection, respacing, and tabular data analysis for the purposes of linguistic data mining. We further present our reanalyzed output format, which attempts to eliminate the extreme verbosity of XML output while leaving important positional information available for downstream processes.
@InProceedings{GOODMAN18.947, author = {Michael Wayne Goodman and Ryan Georgi and Fei Xia}, title = "{PDF-to-Text Reanalysis for Linguistic Data Mining}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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