1 / 26

Talk Schedule Question Answering from Email

Talk Schedule Question Answering from Email. Bryan Klimt July 28, 2005. Project Goals. To build a practical working question answering system for personal email To learn about the technologies that go into QA (IR,IE,NLP,MT) To discover which techniques work best and when. System Overview.

mihaly
Télécharger la présentation

Talk Schedule Question Answering from Email

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. Talk Schedule Question Answering from Email Bryan Klimt July 28, 2005

  2. Project Goals • To build a practical working question answering system for personal email • To learn about the technologies that go into QA (IR,IE,NLP,MT) • To discover which techniques work best and when

  3. System Overview

  4. Dataset • 18 months of email (Sept 2003 to Feb 2005) • 4799 total • 196 are talk announcements • hand labelled and annotated • 478 questions and answers

  5. A new email arrives… • Is it a talk announcement? • If so, we should index it.

  6. Email Classifier Decision Logistic Regression Combo Email Data Logistic Regression

  7. Classification Performance • precision = 0.81 • recall = 0.66 • (previous works had better performance) • Top features: • abstract, bio, speaker, copeta, multicast, esm, donut, talk, seminar, cmtv, broadcast, speech, distinguish, ph, lectur, ieee, approach, translat, professor, award

  8. Annotator • Use Information Extraction techniques to identify certains types of data in the emails • speaker names and affiliations • dates and times • locations • lecture series and titles

  9. Annotator

  10. Rule-based Annotator • Combine regular expressions and dictionary lookups • defSpanType date =: ...[re('^\d\d?$') ai(dayEnd)? ai(month)]...; • matches “23rd September”

  11. Conditional Random Fields • Probabilistic framework for labelling sequential data • Known to outperform HMMs (relaxation of independence assumptions) and MEMMs (avoid “label bias” problem) • Allow for multiple output features at each node in the sequence

  12. Rule-based vs. CRFs

  13. Rule-based vs. CRFs • Both results are much higher than in previous study • For dates, times, and locations, rules are easy to write and perform extremely well • For names, titles, affiliations, and series, rules are very difficult to write, and CRFs are preferable

  14. Template Filler • Creates a database record for each talk announced in the email • This database is used by the NLP answer extractor

  15. Filled Template Seminar { title = “Keyword Translation from English to Chinese for Multilingual QA” name = Frank Lin time = 5:30pm date = Thursday, Sept. 23 location = 4513 Newell Simon Hall affiliation = series = }

  16. Search Time • Now the email is index • The user can ask questions

  17. IR Answer Extractor • Performs a traditional IR (TF-IDF) search using the question as a query • Determines the answer type from simple heuristics (“Where”->LOCATION) Where is Frank Lin’s talk? 0.5055 3451.txt search[468:473]: "frank" search[2025:2030]: "frank" search[474:477]: "lin” 0.1249 2547.txt search[580:583]: "lin” 0.0642 2535.txt search[2283:2286]: "lin"

  18. IR Answer Extractor

  19. NL Question Analyzer • Uses Tomita Parser to fully parse questions to translate them into a structured query language • “Where is Frank Lin’s talk?” • ((FIELD LOCATION) (FILTER (NAME “FRANK LIN”)))

  20. NL Answer Extractor • Simply executes the structured query produced by the Question Analyzer • ((FIELD LOCATION) (FILTER (NAME “FRANK LIN”))) • select LOCATION from seminar_templates where NAME=“FRANK LIN”;

  21. Results • NL Answer Extractor -> 0.870 • IR Answer Extractor -> 0.755

  22. Results • Both answer extractors have similar (good) performance • IR based extractor • easy to implement (1-2 days) • better on questions w/ titles and names • very bad on yes/no questions • NLP based extractor • more difficult to implement (4-5 days) • better on questions w/ dates and times

  23. Examples • “Where is the lecture on dolphin language?” • NLP Answer Extractor: Fails to find any talk • IR Answer Extractor: Finds the correct talk • Actual Title: “Natural History and Communication of Spotted Dolphin, Stenella Frontalis, in the Bahamas” • “Who is speaking on September 10?” • NLP Extractor: Finds the correct record(s) • IR Extractor: Extracts the wrong answer • A talk “10 am, November 10” ranks higher than one on “Sept 10th”

  24. Future Work • Add an annotation “feedback loop” for the classifier • Add a planner module to decide which answer extractor to apply to each individual question • Tune parameters for classifier and TF-IDF search engine • Integrate into a mail client!

  25. Conclusions • Overall performance is good enough for the system to be helpful to end users • Both rule-based and automatic annotators should be used, but for different types of annotations • Both IR-based and NLP-based answer extractors should be used, but for different types of questions

  26. DEMO

More Related