1 / 14

French Question Answering in Technical and Open Domains

French Question Answering in Technical and Open Domains. Aoife O’Gorman Documents and Linguistic Technology Group Univeristy of Limerick. Outline. Information Retrieval Vs Question Answering Monolingual Question Answering Cross-Lingual Question Answering Problems and Possible Solutions

newman
Download Presentation

French Question Answering in Technical and Open Domains

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. French Question Answering in Technical and Open Domains Aoife O’Gorman Documents and Linguistic Technology Group Univeristy of Limerick

  2. Outline • Information Retrieval Vs Question Answering • Monolingual Question Answering • Cross-Lingual Question Answering • Problems and Possible Solutions • Research Objectives

  3. Information Retrieval Vs Question Answering • Information Retrieval (IR) “responding to a user’s need for information by retrieving a small number of documents within which the relevant information is to be found.” [van Rijsbergen, 1999] • (Factoid) Question Answering (QA) Often the user wants not whole documents but brief answers to specific questions. Eg: Q: When was the storming of the Bastille? A: July 14, 1789

  4. MonoLingual Question-Answering • Text REtrieval Conference (TREC) - Q&A Track • DLT TREC System - English  English • FYP system - French  French

  5. Architecture of DLT System

  6. Architecture of French System

  7. Examples English English Q: What year did Alaska become a state? [TREC 2002] A:January 3, 1959 Q: “How did Einstein die?” [TREC 2003] A: Ruptured abdominal aortic aneurysms French French Q: Quand Mike Tyson a-t-il mordu l’oreille de Holyfield? A: le 28 juin, 1997 Q: Quelle est la capitale de l’Algérie? A:Alger [TREC 2002 queries translated by Caroline Corsini for FYP project]

  8. Cross-Lingual Question-Answering • Cross-Language Evaluation Forum (CLEF) • European equivalent to TREC • Multilngual IR tasks including French-English QA Q: Combien d'Oscars le film "Sur les quais" a-t-il remportés? A: eight Q: Quelle est la capitale de la Tchétchénie? [CLEF 2003] A: Grozny

  9. CLEF System Architecture Query classification Query translation (Google) & re-formulation Named entity recognition Text retrieval (dtSearch) Answer entity selection

  10. CLEF 2003 System

  11. Problemsidentified in CLEF • Verbs and their arguments are idiomatically linked faire la pêche  make a fish • Sometimes proper names should be translated, sometimes not Tchétchénie  Chechnya Grand Prix  Grand Prize • Titles La Belle et la Bête  The beautiful one and the animal

  12. Problems identified in CLEF • Slang words in source language that may not exist in target language • Eg: “carjacking” • “actes de piraterie routière” • Anaphoric References: • Eg: “Einstein died of one…..” (ruptured abdominal aortic aneurysm)

  13. Possible Solution • Predictive Annotation: • document collection is analysed • output = dictionary containing the collection vocabulary • proper names and multi-word terms • Eg: the sculpure, “Chicken Boy” (sculpture de « garçonà tête de poulet »)

  14. Research Objectives • Focus on translation in Cross-Lingual QA • Identify French names and titles in queries • Combine data from Corpora, Dictionaries and Web • Predict likely English equivalents • Evaluate in context of CLEF competion

More Related