1 / 7

Evaluating Relationships and Cause & Effect Questions in Textual Data Analysis

This study, co-chaired by John Prager from IBM and Eric Nyberg from CMU, focuses on the evaluation of relationships and cause & effect questions in textual data. The participants cover a wide range of entities including people, organizations, political bodies, and businesses, aiming to identify meta-types and types of connections like family, co-presence, and borders. Various types of questions, from specific yes-no to open/generic, are explored to uncover causality and relationships in text. The importance of distinguishing between relationship and cause & effect questions is emphasized, along with the role of template-filling. Conclusions are drawn by addressing information needs, utilizing the TREC corpus and analyzing relevant stories like Kosovo, Middle East, and India-Pakistan relations.

anise
Download Presentation

Evaluating Relationships and Cause & Effect Questions in Textual Data Analysis

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. Evaluation ofRelationships and Cause&EffectQuestions Co-chaired by John Prager, IBM & Eric Nyberg, CMU

  2. Participating Entities • People • Organizations • Political • Government • Non-government • Business • Social/Recreational • Countries (or other geographic entities) • Commodities/Products/Services • Events • Natural/Artifical Processes

  3. Meta-Types of Connection • Explicit mentions • In text • Actually, might be implicit • As predicates • Statistical • Inferred • a R b && b R’ c -> a R’’ c • Links are any of 3 kinds

  4. Types of Connection • Family • Co-presence • Specific instances • Member_of • Trades_with • Borders • …

  5. Kinds of R & C-E Questions • Specific yes-no • Is X a member of organization Y? • Missing link • What organization/country/… do X and Y both belong to? • Open/Generic yes-no • What is the relationship between X and Y? • Is X related to Y? • Causality Completion • What caused X? • What was/will be the result of Y?

  6. What Happened • A lot of discussion! • Evaluation in general • Form of answer • Overlap with EELD • Make sure there is a distinction • Articulate the difference • Relationship questions or Cause&Effect questions? • Role of template-filling

  7. Conclusions • Start with a set of questions that are representative of IC needs • Use new TREC corpus • Possibly relevant stories: • Kosovo • Middle East • India-Pakistan • etc.

More Related