Recognition and Classification of Noun Phrases in Queries for Effective Retrieval

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Recognition and Classification ofNoun Phrases in
Queries for Effective Retrieval
Wei Zhang1 Shuang Liu2
Clement Yu1
wzhang_at_cs.uic.edu shuang.liu_at_ask.com
yu_at_cs.uic.edu Chaojing Sun3
Fang Liu4 Weiyi Meng5
chaojing_at_gmail.com fangliu_at_microsoft.com
meng_at_cs.binghamton.edu 1 Department of
Computer Science, University of Illinois at
Chicago 2 Ask.com 3 Broadcom
Corporation 4 Microsoft 5 Department
of Computer Science, Binghamton University
CIKM 2007
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Outline

• Motivation
• Our definitions of the phrases
• Proper noun and dictionary phrase recognition
• Simple and complex phrase recognition
• Experimental results

CIKM 2007
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Motivation

• Terms in a query are related semantically
• John Smith
• Recognize this relationship
• Partition the query terms to groups (phrases)
• Document retrieval using phrases
• Adding phrases into searching and ranking

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Types of Noun Phrases

• Phrases that have fixed writing formats
• Names of Locations, people, companies,
• Well defined concepts. E.g. computer science
• Freely written phrases
• Not formally defined but used in the real language

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Four Types of Noun Phrases

• Proper Noun (PN)
• A noun phrase that names a specific person, place
  or thing.
• First letters of the content words are
  capitalized
• E.g. John Smith, Atlantic Ocean
• Dictionary Phrase (DP)
• A phrase that has a definition in a dictionary,
  excluding PN
• These two types may overlap
• Atlantic Ocean
• They can not replace each other
• E.g. Linas Pizza, public transportation

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Four Types of Noun Phrases

• Simple Noun Phrase (SNP)
• A grammatically valid noun phrase other than PN
  and DP
• 2 words
• E.g. white car, good hotel
• Complex Noun Phrase (CNP)
• A grammatically valid noun phrase other than PN
  and DP
• 3 or more words
• May contain PN/DP/SNP
• E.g. small white car, city public
  transportation

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Noun Phrase Recognition

• General procedure
• Recognize PN and dictionary phrases first
• Then simple and complex noun phrases
• A n-word query
• Check the original query
• Check the 2 (n-1)-term arrays
• Check the (n-1) 2-term arrays
• Totally n(n-1)/2 candidates
• E.g. World Trade Organization
• World Trade and Trade Organization

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Noun Phrase Recognition

• Tools for phrase recognition
• Dictionaries (Wikipedia, WordNet)
• Large text corpus (Google for experiments)
• Parsers (Minipar, Collins parser) and POS tagger

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PN and DP Recognition

• Wekipedia
• For proper nouns and dictionary phrases
• DP existence of the entry page
• PN content words in the first instance of the
  phrase in the main text should be capitalized

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PN and DP Recognition

• WordNet
• For PN and DP recognition
• DP defined in a dictionary
• PN has a hypernym of city, province, country,
  organization, geographic area, person, syndrome,
  region, building, or nation.

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PN and DP Recognition

• Minipar
• For PN recognition only
• PN label in the parse tree
• Semantic label of person, country, corpname,
  location, corpdesig, fname, gname, or date

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PN and DP Recognition

• List of first names, last names and rules
• First_initial last_name
• First_initial mid_initial last_name
• First_name middle_initial last_name
• First_name last_name

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PN and DP Recognition

• Text corpus
• For less well-known PNs
• Three instances, first letters of the content
  words capitalized
• Not a sub-phrase of a longer PN
• if you choose windows by Vista Window Company,
  
• if you choose windows by Super Vista Window
  Company,

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PN and DP Recognition

• Overlapped phrases
• Search all words together
• Count the instances of each phrase in the
  returned documents
• e.g. Native American Casino
• Native American and American Casino
• Compare ( Count(Native American),
  Count(American Casino) )

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SNP and CNP Recognition

• Only check the phrase candidates that
• are not sub-phrases of a recognized PN/DP
• do not overlap with a recognized PN/DP

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SNP and CNP Recognition

• Implicit phrases
• and / or
• main and contributing factor ?
• main factor
• contributing factor

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SNP and CNP Recognition

• Head word replacement
• Replace the whole phrase by its head word
• Collins parser
• Label the noun phrases

NP/sedan(head word)
NP/sedan(head word)
Compact/JJ
Best/JJS
Sedan/NN
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SNP and CNP Recognition

• Phrase verification
• To verify that a phrase is used in the world
• For CNP it also means to find all the words in a
  text window
• Colin Farrell wallpaper and wallpaper of Colin
  Farrell

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SNP and CNP Recognition

• Overlapped phrases
• Two potential SNP/CNP Search all words, compare
  the numbers of the instances.
• sony dvd handyam ? sony dvd and dvd
  handycam

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Document Retrieval Using Phrases

• Search a phrase in a document
• Exact match PN/DP
• Search all words in a text window SNP/CNP

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Document Retrieval Using Phrases

• Sim(Query, Doc) ltSim_P, Sim_Tgt
• Phrase similarity
• Sim_P(P_i) idf(P_i)
• Sim_P sum ( sim_P(P_i) )
• Term similarity
• Okapi/BM-25 similarity
• Document ranking
• D1 is ranked higher than D2, if
• (Sim_P1gtSim_P2) OR (P1P2 AND T1gtT2)

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Experimental Results

• Phrase recognition experiments
• Tuned by using TREC queries

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Experimental Results

• Phrase recognition experiments
• Tested by using Web queries

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Experimental Results

• Performance of individual tools
• Wikipedia is better than WordNet and Minipar
• Need for a complete dictionary
• Collins parser alone is not enough for SNP/CNP
  recognition
• Lack of real world usage information

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Experimental Results

• Document retrieval experiments
• Ad-hoc TREC 6, 7 and 8, robust TREC 12, 13 and 14
• Retrieval without using phrases
• Using Wikipedia for PN/DP and just collins parser
  for SNP/CNP
• Using phrases from the full recognition algorithm
• 33 MAP increase and 44.27 GMAP increase from 1
  to 2
• 5.8 MAP increase and 12.58 GMAP increase from 2
  to 3

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Conclusions

• Our algorithm can effectively recognize the four
  types of phrases in the short Web queries
• The recognized phrases help improve the retrieval
  effectiveness

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Questions?

• wzhang_at_cs.uic.edu
• http//www.cs.uic.edu/wzhang/