Finding Authoritative People from the Web

1
Finding Authoritative People from the Web

• Masanori Harada, Shin-ya Sato, Kazuhiro Kazama
• harada,sato,kazama_at_ingrid.org
• NTT Network Innovation Labs.

2
Contents

• Motivation
• Why study finding people?
• Examples
• Approach
• Extract personal names on the web
• Find relevant people using a search engine
• Results
• Performance evaluation
• Summary and future plans

3
Background

• As the web is connected to the real world, we
  can
• Find real-world things by searching the web.
• Understand the real world by investigating the
  web (and vice versa).

the real world

... connections


the web
searching
4
Objective

• Find authoritative people for all sorts of
  topics by extending a web search engine
• Why find people?
• Once people have been found, many other things
  (e.g. books) can be retrieved using digital
  libraries
• What is authoritative?
• People mentioned in many web pages with regard to
  a queried topic

5
Screenshot
Relevant personal names
Relationships
Relevant web pages
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Example (1) subject to people

• digital libraries (1007 pages)
• Possible application book finder
• Using library catalogs, it could suggest relevant
  books written by these authoritative people

• Shigeo Sugimoto Univ. Library Information
  Science
• Koichi Tabata Univ. Library Information
  Science
• Jun Adachi National Institute of Informatics
• Takeo Yamamoto National Institute of Informatics
• Hiroyuki Taya National Diet Library

7
Example (2) thing to people

• Spirited Away (35,936 pages)
• Possible application movie recommender
• Using movie databases, it could suggest movies
  which share key people for any queried topic

• Hayao Miyazaki director
• Bunta Sugawara voice actor
• Mari Natsuki voice actress
• Yumi Kimura singer of theme song
• Joe Hisaishi composer

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Example (3) person to people

• Masanori Harada (205 pages)
• Possible application social networking
• Unlike social networking services like orkut,
  there is no need to enter relationships manually

• Masanori Harada me
• Shin-ya Sato co-author of this paper
• Kazuhiro Kazama co-author of this paper
• Kent Tamura web search researcher
• Isao Asai web search researcher

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NEXAS

• Named entity extraction and association search
• Associate an entity and a web page by extracting
  names identifying the entity.
• Find entities associated with top-ranked web
  pages retrieved for a query.

A
More relevant (authoritative)
More relevant (authoritative)

B
web search
Less relevant
Less relevant
C
Irrelevant
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Extracting personal names

• Web data
• 52 million Japanese web pages collected in July
  2003
• Japanese personal name extractor
• Extracts only full names
• Assumes a full name can identify a person
• Big name dictionaries enables accurate extraction
• Precision 93.5, Recall 85.3

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Personal names on the web

• Personal names appear frequently on the web
• 6.6M unique names extracted from 52M web pages
• 1/4 of web pages contain full names
• Celebrities appear gt10 thousand times
• singers, actors, sports stars, novelists,
  politicians, etc.
• Most names appear only a few times
• Name frequency indicates popularity
• But number of pages is easily affected by
  automatically generated texts and spams
• Number of servers is less affected
• Authoritative people are mentioned in many servers

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Procedure for finding people

• 1. Find web pages using a full-text search engine
• 2. List personal names extracted from top T
  relevant and authoritative web pages
• 3. Calculate relevance scores and output top k
  relevant people, who
• Appear frequently on top-ranked web pages
• Do not appear frequently on irrelevant web pages

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Calculating relevance score

• Scoring functions df, sf, dfidf, and sfidf
• df document frequency within top T pages
• sf server frequency within top T pages
  0.01 df
• idf log (N / fp )
• N number of pages in the collection
• fp document frequency
• sf Alleviates effects of generated texts
  and spams
• idf Weight for moderating generally famous
  person

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Performance evaluation

• Compared 4 scoring functions for varying T
• Precision average of scores of top k people
• Judged if a person was relevant (Score 1) or not
  (Score 0) by searching for the personal name
  using Google
• 45 simple topics were used
• 15 musical instruments players or not?
• 15 sports players or not?
• 15 information technologies experts or not?

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Precision of scoring functions

• sf is very effective
• idf is fairly effective

sfidf sf dfidf df
Precision
Number of people evaluated
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Precision with varying T

• More web pages do not necessary yield better
  results

sfidf T 500 T 1000 T 200 T 2000 T 5000 T
100
Precision
Number of people evaluated
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Future work

• Apply for other languages, especially English
• Can we distinguish different John Smith?
• Find books, companies, shops, etc.
• By extracting ISBNs, domain names, places, etc.
• Analyze co-occurrences as a social network

Online demo is available at http//valhalla.ingr
id.org28080/ throughout the conference
Japanese fonts and Java2 plug-in are required
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Precison vs. result size

• Too-specific or too-general topics are difficult.

sfidf T1000
Precision of top 10 people
Number of pages retrieved for a topic
databases
compiler theory
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Popular Japanese names

• singers, actors, sports starts, novelists,
  politicians, etc.

Table Top 10 most frequent names
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Related work

• ReferralWeb Kautz 1997
• Finds experts around the user by searching the
  web
• Tested only with computer science topics
• Web Question Answering Kwok 2001Brill 2001
• Retrieve one exact answer to a long, complete
  natural language question
• Our contributions
• Observed the distribution of personal names on
  the web
• Extended a web search engine so that it
  accurately finds relevant people for all sorts of
  queries.

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Common failures

• Too specific topics
• Too general topics
• Name extraction errors
• Falsely extracted non-names
• Missed (not extracted) names
• Historical/Fictional characters
• Celebrities
• Popular names often appear without regard to a
  specific topic

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Numbers

• Number of web pages 52,302,805
• Number of web servers 664,139
• Number of pages w/ names 13,922,012
• Number of name occurrences 117,091,977
• Number of unique names 6,161,805
• Total size of web pages 450GB
• Size of inverted index 113GB
• Size of dictionaries
• Family names 21,141
• Personal names 12,130
• Full names 19,675

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Topics for the experiment

• Musical instruments
• violin, cello, trumpet, clarinet, harp,
  percussion, synthesizer, ocarina, accordion,
  contrabass, pipe organ, and marimba
• Sports
• soccer, baseball, marathon, swimming, rugby,
  volleyball, basketball, boxing, badminton, ice
  hockey, speed skating, fencing, lacrosse, pole
  vault, and discus throw
• Information technology terms
• databases, java, information retrieval, XML,
  IPv6, speech recognition, P2P, data mining,
  machine translation, complexity theory, web
  search engines, probabilistic reasoning,
  simulated annealing, compiler theory, and
  randomized algorithms

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Name extraction method

• Procedure
• 1. Remove HTML tags.
• 2. Using a morphological analyzer, split each
  sentence into morphemes and assign
  Part-Of-Speech tags.
• 3. Extract ltfamily namegtltpersonal namegt
  sequences.
• Performance improved by enriching dictionaries
• 17k families 12k personals 2k popular full
  names Precision 78.4, Recall 75.0
• 21k families 40k personals 19k popular full
  names Precision 93.5, Recall 85.3

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The same-name problem

• Not very serious when we query a subject
• Different people having the same name rarely
  exist in a specific area
• Japanese family/personal names are diverse
• Still, not a few people share the same name
• Solutions (under consideration)
• Classifies web pages by topic
• Analyze a social network around the name
  (Different people have different friends)

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Popularity and frequency

• Personal name frequencies indicate web users'
  interest in celebrities.
• The number of pages is prone to be affected by
  automatically generated pages and spams.
• The number of servers is better to find popular
  people.