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10_Question_Answering

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R. P.
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R. P.
This flashcard set delves into advanced question answering (QA) techniques at the university level, covering key concepts like query reformulation, answer redundancy, and passage retrieval. It explores various question types, answer assessment methods, and the differences between QA and information retrieval. Ideal for computer science students or professionals, this flashcard set provides insights into improving QA systems' accuracy and efficiency, making it valuable for those working on natural language processing or search engine optimization.
Cartes-fiches
13
Utilisateurs
2
Langue
Anglais
Catégorie
Informatique
Niveau
Université
Créé / Mis à jour
07.02.2018 / 09.02.2018
  • 1  / 13

Cartes-fiches

Question Answering vs. Information Retrieval

  •  INPUT:
    •  NL language questions and not keyword-based queries:
      •  QA:
        • How long do polar bears live?
      •  IR:
        • polar bears life span
  •  OUTPUT:
    • Precise and concise answers, not whole documents
      •  QA:
        • In the wild, polar bears live an average of 15 to 18 years, although  biologists have  tagged a few bears in their early 30s. In captivity, they may live until their mid- to late 30s. One zoo bear in London lived to be 41.
      • IR:
        • www.gotpetsonline.com/polar-bear/bear-habitat-polar/polar-bear-life-
        • span.html 
        • www.starbus.com/polarbear/aboutpb.htm
        • www.polarbearsinternational.org/faq

Generic QA System Architecture

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Question Processing (including Question Type)

  • Goal: extract clues from the question
    • Question type
      • Factual answers → Factoid questions
        • How long do polar bears live?
      • Definitional answers → Definition questions
        • Who is Britney Spears?
      • Opinionated answers → Opinion questions
        • What do you think of Britney Spears' last album?
      • There exist different Taxonomy for Question types: e.g. Example, Comparison, Quantification etc.
        • Li&Roth Two-layered Taxonomy with 6 coarse and 50 fine classes
          • Abbreviation:expression, Entity:animal, Description:def, Human:individual, Location:country, Numeric:date
      • More difficult types like How and Why require complex answers
        • TREC-QA: main question types
          • Factoid
          • List
          • Definition
          • Other
    • Expected answer type(s)
      • The answer type is the semantic category of the expected answer
        • Country, author etc.
    • Named Entities
    • Interesting terms used to query the search engine
    • Focus
    • Topic
    • Prediction of the question difficulty

Question Classification

  • Rule-based
    • Biographical questions
      • Who {is | was | are | were} < person name(s) >?
    • Definition questions etc.
    • Pros & Cons?
      • Very powerful
      • Cumbersome(mühsam) to create
      • Do not generalise well
  • Machine Learning
    • Trained on hand-labeled questions, such as the corpus of Li & Roth
    • Question features
      • Tokens
      • Named Entities
      • POS tags
      • Chunks
      • N-grams
      • Question word

Document Retrieval

  • Identify the N most relevant documents given an input question
  • For this, the question has to be reformulated as a query:
    • Removal of stop words
    • Stemming or lemmatization
    • Query expansion
    • Apply query reformulation rules
    • …

Passage Retrieval and Scoring

  • Aim: return the N most relevant passages from the top ranked
  • Passages: sentences, paragraphs, sections / topical segments
  • The others are ranked based on:
    • the number of Named Entities of the right type
    • the number of question keywords in the passage
    • the rank of the document from which the passage was extracted
    • …

Answer Identification

  • Find the best answer to the question
  • Two types of methods:
    • Pattern extraction, using regular expression patterns corresponding to the expected answer type
    • Redundancy-based approach

Query reformulation 

  • Aim: automatically generate answer paraphrases for a given question
  • Formulate multiple queries for each question and retrieve the 100 best matching pages for each
    • Rewrite rules are simple string-based manipulations
      • Question: “Where is the Louvre located?”
      • Rewrite Query:
        •  “+the Louvre +is located”
        • “+the Louvre +is +in”
        • “+the Louvre +is near”
    • Use of a search engine (Google) to find answers on the Web

Answer Redundancy

  • Harvest the summaries returned by the search engine for each query
  • Collect n-grams from the summaries and score them according to their frequency of occurrence and the weight of the query rewrite that retrieved it
  • The n-grams are filtered and reweighted according to how well each candidate matches the expected answer type,
  • Merge similar answers and assemble longer answers from overlapping smaller answer fragments
    • „ABC“ and „BCD“ is tiled into „ABCD“

Answer Assessment

  • Correct
  • Inexact
    • Answer contains unnecessary information or misses relevant information
  • Unsupported
  • Incorrect

QA steps

  • Question processing
    • most important step is to detect correct question type
  • Document processing
    • e.g. using query reformulation
  • Answer processing
    • employ answer redundancy

Mean Reciprocal Rank (MRR)

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  • Reciprocal Rank = inverse of the rank of the first correct answer or 0 if no answer given
  • Mean Reciprocal Rank (MRR): mean of the reciprocal ranks over all questions
    • Example: Image

Accuracy

  • average of SCORE(q) over all questions q, where SCORE(q) is 1 if the first answer to q is relevant, and 0 otherwise.
    • Accuracy = 0, 1, 0 => 1/3 in total for the given example in MRR

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