1 / 15

Tiran Software

Tiran Software. -TURKUAZ Project- RadeX Tahir Bilal Onur Deniz Soner Kara M. Mert Karadağlı. Assistant: Umut Eroğul Instructor: Meltem T. Yöndem. Outline. Problem Definition Important Aspects Our Approach General Structure Analyzer Component Searcher Component

sutton
Download Presentation

Tiran Software

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Tiran Software -TURKUAZ Project- RadeX Tahir Bilal Onur Deniz Soner Kara M. Mert Karadağlı Assistant: Umut Eroğul Instructor: Meltem T. Yöndem

  2. Outline • Problem Definition • Important Aspects • Our Approach • General Structure • Analyzer Component • Searcher Component • Current Status • Prototype • Tool and Resources • Q/A

  3. Problem Definition • Billions of radiology reports • Unfortunately, they are stored in free-text format • Hard to search and retrieve • Need for searchable information

  4. Important Aspects • Text Mining • NLP • Information Extraction • Morphological Analysis • Named Entity Recognition • Machine Learning • Neural Networks, Decision Trees ...

  5. Our Approach RadeX, Radiology Data Extractor will enable.. • Modular machine learning component • Support for internal/external dictionary connection • Template-based approach for finalizing

  6. General Structure

  7. General Structure (cont.) • Analyzer Component • Preprocess free text • Look-up internal and external lexicons • Gives semantic to words • Extracts searchable data • Searcher Component • Send query strings to database • Retrieve corresponding information

  8. Current Status • Preprocessing. • Connecting and using external sources. • Database implementation. • Applying SVM to unrelated but tagged corpus.

  9. Current Status (cont.) • Mapping Turkish terms to English translations. • Finding stem of unknown words. • Constructing lexicons. • Features of verbs, adjectives, nouns...

  10. In Prototype we will be able to... • ..decompose reports into sub-parts, sentences and words, • .. analyze words using Zemberek and a stemmer. • .. give semantics to words via internal/external lexicons • .. extract simple information using pre-defined templates

  11. Tools & Resources • SVM-Light • WordNet • JWNL • TDK / Zargan • Zemberek, • PostgreSQL

  12. Any Questions?

More Related