Information Extraction out of Born- Digital Scientific Articles

Transcription

1 Information Extraction out of Born- Digital Scientific Articles Roman Kern EXCITE Workshop 2017 Know-Center GmbH,

2 STARTING POINT: PDF EXTRACTION Know- -Driven Business and Big Data Analytics 2

3 STARTING POINT For Research Interest on PDF Extraction Started with Mendeley research stay As part of a Marie-Curie project Ended with a PDF extraction tool chain Extraction of named entities from scientific publications Research interests Apply machine learning & natural language processing algorithms 3

4 STARTING POINT For Research Interest on PDF Extraction 4

5 STARTING POINT PDF Extraction Pipeline Start with characters within the PDF (bounding box) Group / split iteratively Word -> Lines, Lines -> Block, Blocks -> Columns Reading order (of blocks) Main text, decoration, headings, captions Table of contents Tables Meta data References, citations Named entities and relations within the text Kröll, M., Klampfl, S., & Kern, R. (2014). Towards a Marketplace for the Scientific Community: Accessing Knowledge from the Computer Science Domain. D-Lib Magazine, 20(11), 10. 5

6 Information Extraction from PDFs REFERENCE EXTRACTION EXPERIMENTS Kern, R., & Klampfl, S. (2013). Extraction of References Using Layout and Formatting Information from Scientific Articles. D-Lib Magazine, 19(9/10). Know- -Driven Business and Big Data Analytics 6

7 REFERENCE EXTRACTION General Approach Approach to Reference Extraction Take ParsCit as base 4 processing steps Add formatting information Add layout information Measure performance impact 7

8 REFERENCE EXTRACTION Extraction Steps 1. Collect lines containing references Regular expressions for headings One set for end (e.g. Annex) Text in correct sequence Columns in reading order Decoration text is ignored Detect vertical gap E.g. to avoid including footnotes 8

9 REFERENCE EXTRACTION Extraction Steps 9

10 REFERENCE EXTRACTION Extraction Steps 2. Split lines into individual references List of prefix (e.g. [1]) for marker types, or Heuristics (e.g. line length) If no marker type found Split upon negative indentation If no marker types are found Clustering on lines (x coordinate) 10

11 REFERENCE EXTRACTION Extraction Steps 3. Reference String Preprocessing Remove hyphens Reverse the TEX process Normalise the text Normalise page numbers to <number>---<number> 11

12 REFERENCE EXTRACTION Extraction Steps 4. Reference Token Classification Assign a class to each token of the reference string Slight deviation from the ParCite classes More fine grained e.g. authors -> authorfirstname, authorsurname, authorother Classification (sequence features) and sequence classification Maximum Entropy + Beam Search Conditional Random Fields 12

13 REFERENCE EXTRACTION Extraction Steps 4. Reference Token Classification 13

14 EVALUATION & LESSONS LEARNT Know- -Driven Business and Big Data Analytics 14

15 EVALUATION STRATEGIES Evaluation mostly based on PubMed Does contain a lot of diversity, but Additionally integrate ArXiv LaTeX sources available Tedious LaTeX parser 15

16 EVALUATION STRATEGIES 16

17 LESSONS LEARNT Different PDF Extraction libraries 1. PDFBox Slow, but robust 2. Poppler Wrapper necessary 3. JPod Fast, simple and easy to use 4. itext 5. Mootools 6. Sun/Oracle Render PDF as image 17

18 CURRENT ACTIVITIES & CLOSING Know- -Driven Business and Big Data Analytics 18

19 STARTING POINT Research interests Apply the PDF extraction pipeline Authorship identification E.g. How many people edited a paper Fact extraction (from papers) Open Information Extraction Access to the source code Relvant modules: pdf-extraction and service/annotator Username: anonymous (empty password) 19

20 Know-Center GmbH Research Center for Data-Driven Business and Big Data Analytics Inffeldgasse 13/ Graz, Austria Firmenbuchgericht Graz FN f UID: ATU Dr. Roman Kern rkern@know-center.at Area Head gefördert durch das Programm COMET (Competence Centers for Excellent Technologies), wir danken unseren Fördergebern: Know-Center GmbH