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EXTRACTING INFORMATION FROM CLASSICS SCHOLARLY TEXTS
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[poster] Extracting Information From Classics Scholarly Texts

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Poster presented at the Research Fair 2009 at King's College (London).

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[poster] Extracting Information From Classics Scholarly Texts

  1. 1. EXTRACTING INFORMATION FROM CLASSICS SCHOLARLY TEXTS Matteo Romanello, matteo.romanello@kcl.ac.uk Goal HIDDEN WORD PUZZLE The project at a glance ● PhD research project in Digital Humanities Devising an automatic system to improve To solve the puzzle find the (DH) information retrieval over a discipline-specif c i words in the schema by ● discipline: DH, Classics (Greek and Latin corpus of unstructured texts. using a word list as clue. literature) CORPUS: Open Access At the end you'll have added ● topic: extracting structured information from a information to the initially collection of Classics journal papers corpus of unstructured texts chaotic picture. Why automatic? Because automatic means also Steps Gone digital. What changed? scalable when you are dealing with a huge quantity of data. 1. Building the corpus (OCR, preprocessing) We are moving from books to e- Information retrieval: the task of retrieving books, and from journals to e- information (most of the times accomplished by 2. Making the data sources interoperable journals as we are using them using search engines) (when the same entity E appears in DB1 and DB2, almost daily. the information about E in DB1 have to be added Corpus of unstructured texts: collection of plain Is our way of accessing texts, without any kind of mark-up (such as XML). to information about E in DB2) information actually changed with the use of digital tools? 3. Finding in the corpus the mentions of Information can be REALIA (place, names, work passages, etc.) Did just the format change or accessed using multiple are we provided with innovative access points that are 4. Disambiguating the mentions of REALIA ways of accessing information meaningful for scholars based on digital technologies? in a specif c f eld. i i 5. Automatic creation of new indices to the texts Access points to information in Classics Method Expected results Print resources 1. Reuse existing data resources containing structured information (such as gazetteers, ●Providing automatically multiple ● Table of Content (TOC) authority lists, etc.) stored using different data meaningful entry points to information ● Indexes (index of citations, index of greek word, index of geographic place, index of names, etc.) formats (Relational DataBases, XML f les, i ● Enrich the corpus with links to navigate etc.) Electronic resources through resources ● TOCs 2. Apply Computational Linguistic and ● Access through search engines Natural Language Processing algorithms ● Exploiting extracted information to ● ? for the information extraction improve user access to the corpus * usually provided just for monographs because expensive 3. Use structured data as training data for ●Demonstrate the scalability of the to be produced the algorithms which “mines” the unstructured approach text corpus Centre of Computing in the Humanities (CCH), King's College London

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