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IE approaches. Traditional IE (from NLP and CL) Using syntactic and semantic constraints Wrapper (independently developed for WWW) Using delimiter-based extraction patterns This paper Soft Pattern + IR(PRF) + summarization (sentence retrieval/ranking, MMR) techniques.

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Ie approaches
IE approaches

  • Traditional IE (from NLP and CL)

    • Using syntactic and semantic constraints

  • Wrapper (independently developed for WWW)

    • Using delimiter-based extraction patterns

  • This paper

    • Soft Pattern + IR(PRF) + summarization (sentence retrieval/ranking, MMR) techniques


Unsupervised learning of soft patterns for generating definitions from online news
Unsupervised Learning of Soft Patterns for Generating Definitions from Online News

  • IE from QA perspective

  • Research question: finding definition sentence for terms or person names;

  • Previous approaches:

    • hand-crafted rules (previous paper) or

    • supervised learning

  • Research method:

    • unsupervised soft patterns +IR + summarization

  • External tools needed: commercial pos tagger and syntactic chunker (NP, VP)


Soft patterns
Soft Patterns Definitions from Online News

  • A virtual vector representation (window size 3)

    • <Slot-w, ……, Slot-2, Slot-1, SCH_TERM , Slot1, Slot2, ……Slotw : Pa>

  • Slot: a vector of tokens with their probabilities of occurrence

    • <(tokeni1, weighti1), (tokeni2, eighti2) ……(tokenim, weightim): Sloti>

  • Token: word, punctuation or syntactic tag (substituted?)


Soft Patterns Emerged from Text Definitions from Online News


Soft patterns matching process

sentences Definitions from Online News

Test sentence

Tagging, chunking, substitution

Tagging, chunking, substitution

Pa instances

<token-w, ……, token-2, token-1, SCH_TERM, token1, token2, …… tokenw : S>

S instance

Probability estimate

Soft patternsPa

Soft Patterns Matching Process

Matching:1) bag-of-words similarity using Naive Bayes2) sequences fidelity using bigram model3) weighing patterns by their overall weight


Soft patterns matching
Soft Patterns Matching Definitions from Online News

  • bag-of-words similarity using Naive Bayes

  • sequences fidelity using bigram model

Where is Pa?

Manual Tuning alpha?


System architecture
System Architecture Definitions from Online News

Search Term

IR, anaphora resolution

Final sentenceselection

Input relevant sentences

Redundancy removal: MMR

Centroid-basedranking

Matched candidatesentences as definition

Reranking by pattern matching

Ranked sentences

Top n by PRF

SP generation

Pseudo-relevance feedback or assumption?


Centroid word selection
Centroid Word Selection Definitions from Online News

  • Which sentences are mostly likely to contain a definition?

    • Local centroid words (summarization techniques)

    • For each word, compute its mutual info with search term


Summary of the techniques employed
Summary of the techniques employed Definitions from Online News

  • Core: soft pattern generalization and matching

  • Others:

    • Heavy use of summarization techniques

      • MMR for redundancy removal

      • Sentence Ranking/Retrieval

    • Shallow NLP

      • POS tagging and syntactic chunker


Evaluation for information extraction
Evaluation for Information Extraction Definitions from Online News


Evaluation for definition extraction
Evaluation for Definition Extraction Definitions from Online News

  • Test data:

    • TREC QA corpus

    • Online news (heuristics leaning to news text)

  • Experiment:

    • Comparison to HCR and centroid-based statistical method (baseline)

    • F5-measure


Evaluation for TREC collection Definitions from Online News


Evaluation for Web Corpus Definitions from Online News


Questions for this paper
Questions for this paper Definitions from Online News

  • Chunker-variate performance? (NP, VP)

  • Manual tuning parameter (alpha, delta)?

  • Void PRF?

  • Question selection: seed for pattern generation

  • Is it “patterns” or just one pattern at all?

  • Arbitrary window size?

  • Is it really “unsupervised learning?”

    • Part of data used for rule induction

  • Can SP+PRF really beat HCR?


References
References Definitions from Online News

  • Line Eikvil. Information Extraction from World Wide Web. Norwegian Computing Center Technical Report 1999

  • William Cohen and Andrew McCallum. Information Extraction from World Wide Web. Kdd tutorial 2003

  • Stephen Soderland. Learning Information Extraction Rules from Semi-structured and Free-text. Machine Learning (1) 1999

  • Fuchun Peng. Models for Information Extraction. Technical Report (2000 or 2001?)

  • Douglas E. Appelt and David J. Israel. Introduction to Information Extraction Technologies. IJCAI’99 Tutorial.


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