medkhem/grobid-dictionaries

GROBID-Dictionaries

http://img.shields.io/:license-apache-blue.svg](http://www.apache.org/licenses/LICENSE-2.0.html) https://img.shields.io/badge/version-0.4.3--SNAPSHOT-pink.svg](https://github.com/MedKhem/grobid-dictionaries/blob/master/README.md) https://readthedocs.org/projects/grobid-dictionaries/badge/?version=latest](https://readthedocs.org/projects/grobid-dictionaries/?badge=latest)

Purpose

GROBID-Dictionaries is a GROBID sub-project, implementing a java machine learning library, for structuring digitized lexical resources and entry-based documents with encyclopedic or bibliographic content. It allows the parsing, extraction and structuring of text information in such resources.

Approach

GROBID-Dictionaries is based on cascading CRF models. The diagram below presents the architecture enabling the processing and the transfer of the text information through the models.

!GROBID Dictionaries Structure

Dictionary Segmentation This is the first model and has as goal the segmentation of each dictionary page into 3 main blocks: Headnote, Body and Footnote. Another block, "dictScarp" could be generated for text information that do not belong to the principal blocks

Dictionary Body Segmentation The second model gets the Body, recognized by the first model, and processes it to recognize the boundaries of each lexical entry.

Lexical Entry The third model parses each lexical entry, recognized by the second model, to segment it into 4 main blocks: Form, Etymology, Senses, Related Entries. A "dictScrap" block is there as well for unrecognised information.

The rest of the models The same logic applies respectively for the recognised blocks in a lexical entry by having a specific model for each one of them

N.B: The current architecture could change at any milestone of the project, as soon as new ideas or technical constraints emerge.

Input/Output

GROBID-Dictionaries takes as input lexical resources digitized in PDF format. Each model of the aforementioned components generates a TEI P5-encoded hierarchy of the different recognized text structures at that specific cascading level.

Docker Use

To shortcut the installation of the tool, the https://github.com/MedKhem/grobid-dictionaries/wiki/Docker_Instructions could be followed to use the latest image of the tool

To Cite

Mohamed Khemakhem, Luca Foppiano, Laurent Romary. Automatic Extraction of TEI Structures in Digitized Lexical Resources using Conditional Random Fields. electronic lexicography, eLex 2017, Sep 2017, Leiden, Netherlands. hal-01508868v2

Mohamed Khemakhem, Axel Herold, Laurent Romary. Enhancing Usability for Automatically Structuring Digitised Dictionaries. GLOBALEX workshop at LREC 2018, May 2018, Miyazaki, Japan. 2018. hal-01708137v2

More Reading

Hervé Bohbot, Francesca Frontini, Giancarlo Luxardo, Mohamed Khemakhem, Laurent Romary. Presenting the Nénufar Project: a Diachronic Digital Edition of the Petit Larousse Illustré. GLOBALEX 2018 - Globalex workshop at LREC2018, May 2018, Miyazaki, Japan. hal-01728328

Mohamed Khemakhem, Carmen Brando, Laurent Romary, Frédérique Mélanie-Becquet, Jean-Luc Pinol. Fueling Time Machine: Information Extraction from Retro-Digitised Address Directories. JADH2018 "Leveraging Open Data", Sep 2018, Tokyo, Japan. hal-01814189

Mohamed Khemakhem, Laurent Romary, Simon Gabay, Hervé Bohbot, Francesca Frontini, et al.. Automatically Encoding Encyclopedic-like Resources in TEI. The annual TEI Conference and Members Meeting, Sep 2018, Tokyo, Japan.hal-01819505

David Lindemann, Mohamed Khemakhem, Laurent Romary. Retro-digitizing and Automatically Structuring a Large Bibliography Collection. European Association for Digital Humanities (EADH) Conference, Dec 2018, Galway, Ireland. hal-01941534

Documentation

For more expert and development uses, the documentation of the tool is detailed http://grobid-dictionaries.readthedocs.io/en/latest/

Contact

Mohamed Khemakhem (<>), Laurent Romary (<>)