Generating Wikipedia-Like Biographical Sentencesfrom Web People Search Results
Harumi Murakami

Toshimune Konishi

Yoshinobu Ura

Graduate School for Creative Cites
Osaka City University
Osaka, Japan
Email: harumi@media.osaka-cu.ac.jp

Graduate School for Creative Cites
Osaka City University
Osaka, Japan

winspire
Wakayama, Japan

Abstract—To help users understand and select people on theweb, we developed a method of generating biographical sentences
for the results of web people search. We extracted attributeinformation about people (birth date, death date, birthplace,vocation, organization, and position) from the HTML ﬁles of
person clusters that were manually classiﬁed into individuals andgenerated biographical sentences whose style resembles the ﬁrst
sentence of Wikipedia. We evaluated the method using 20 queries(person names) × 50 web search results. Our experimental results
show that it is easy to generate Wikipedia-like biographicalsentences of people using (1) a reasonable Wikipedia-like templateand (2) a simple generating algorithm that combines extracted
attribute information and predeﬁned words.

A. Extracting Attribute Information

Keywords—biographical sentence, biographical summary, attribute information, web people search2) Birthplace: We obtain 100 characters before and after
the person names since person proﬁles tend to be included.
From these characters, we obtain 10 characters before andafter “come from” and 10 characters before “born”. By exactmatching prefecture names with these strings, prefecture names
are extracted as birthplaces.

I.

I NTRODUCTION

The popularity of web people searches continues to riseas the number of people increases about whom the web canprovide information. Through people search, users must distinguish among different people and select appropriate search
results. If the list is merely “person 1, person 2, and so on,”users have difﬁculty determining which person they should
select. Appropriate labels shown with people should help users
select the person they want. Research exists that assigns labels
to people. For example, Wan et al. [1] separated web people
search results and assigned titles to person clusters. Ueda etal. [2] assigned vocation-related information to person clusters.
However, what kind of information is useful to understand or
select a person? One answer might be “Google’s knowledge
graph,” which is displayed on the right side on a keyword
search by a person’s name. Typically, if the searched person
has a Wikipedia page, its 1st sentence is displayed.
In this paper, we present a method of generating Wikipedialike biographical sentences for the results of web people
search. We extract attribute information about people from theHTML ﬁles of person clusters that were manually classiﬁed
into individuals and generate biographical sentences whosestyle resembles the ﬁrst sentence of Wikipedia.
II.

A PPROACH

We extract attribute information about Japanese people(birth date, death date, birthplace, vocation, organization andposition) and generate biographical sentences for them. Whenno attribute information is extracted, predeﬁned words are used
to ﬁll the gap to form sentences.

1) Birth and Death Dates: We extract one birth date andone death date using regular expressions that contain suchterms as “born” or “year, month, date.” We choose the mostfrequent candidates. If the most frequent candidates exceed
one, a candidate is selected from the highest ranked web page.
If more than one candidate remains, a candidate is selected thatis found in the upper most part of the page. This “frequency
ﬁrst, high rank page second, upper part third” strategy, which
selects one answer from the candidates, is common for thefollowing extracting algorithms.

3) Vocation: The vocation list page of Wikipedia contains
Japanese vocation names. Since the longest vocation namehas around 20 characters, we obtain the 20 characters before
and after person names. Character strings that contain such
sufﬁxes as “er” or “et” (usually used for vocations) areselected. We perform morphological analysis and concatenate
the forward continuous nouns with the sufﬁxes. Concatenatedstrings, which end with the sufﬁxes used for the selection,
become vocation candidates.
4) Organization: Since many organization candidates arecontained on web pages, we only use the top ﬁve for extraction.
Lines that contain such terms as “center,” “hospital,” and“university” etc. are extracted and analyzed morphologically,and continuous nouns are concatenated with the terms. Concatenated strings that end with the terms used for the selection
become organization candidates. In addition, lines that containsuch terms as “Inc.,” “clinic,” or “studio,” are extracted andanalyzed morphologically, and except for symbols, morphemes
are concatenated with the terms. Concatenated strings thatbegin or end with the terms used for the selection becomeorganization candidates.
5) Position: When only an organization is extracted, anassociated position will be extracted using the extracted organization. From the 50 characters before and after the aboveorganizations, we select such terms as “president,” “professor,”and “ executive” and analyze them morphologically and concatenate the forward continuous morphemes with the terms.

Concatenated strings that end with the terms used for theselection become position candidates.
B. Generating Biographical Sentences
We generate biographical sentences with a template:“Name (Birth - Death) is a Birthplace Vocation Organization
Position” from the extracted attribute information. When no
attribute information is extracted, such words as “Japanese,”“from,” “person,” and “belong to” are used to ﬁll the gaps toform natural sentences. The generating algorithm is described
below.
function Generate-Japanese-Summary(person-attribute-information)
returns a summary
if Birthplace is empty then Birthplace ← “Japanese”
else Birthplace ← Birthplace + “from”
end if
if Vocation is empty then Vocation ← “person.”else Vocation ← Vocation + “.”end ifif Organization is not empty thenif Position is empty then Position ← “belongs to.”else Position ← Position + “.”end ifend ifSummary ← Name + “(” + Birth “-” + Death + “) is a”+ Birthplace + Vocation + Organization + Position
return Summary

R ESULTS OF EXTRACTING ATTRIBUTE INFORMATION

Birth date
Precision
Recall
82%70%(14/17)(14/20)
Vocation
Precision
Recall
67%24%(16/24)(16/67)

Death date
Precision
Recall100%75%(3/3)(3/4)
Organization
Precision
Recall
62%59%(39/63)(39/66)Birthplace
Precision
Recall93%93%(13/14)(13/14)
Position
Precision
Recall66%
38%(19/29)(19/50)vocation, organization, and position was low and must be
improved.
For biographical sentences, 100% (80/80) were generated;in other words, it was easy to generate Wikipedia-like biographical sentences of people. However, for 12 people (15%),those sentences were just default sentences: “person name isa Japanese person.” In other words, no attribute information
was obtained. For these 12 people, since only one page exists,
and there is insufﬁcient information for them.
IV.

R ELATED W ORK

Schiffman et al. [4] produced biographical summaries fromnewspaper articles using linguistic knowledge with corpus
statistics. Basically their approach selects suitable sentencesfrom a large corpus without producing Wikipedia-like sentences. We analyzed the ﬁrst sentence of Wikipedia articles forpeople and presented an easy-to-generate template and an associated robust generation algorithm specialized to people. Some
research (e.g., [5]) automatically generated general Wikipedia
articles. We focus on people on the web and presented amethod. We believe that our work’s main contribution is togenerate Wikipedia-like biographical sentences for people onthe web. To the best of our knowledge, this is the ﬁrst researchof its kind.

C. Example
Asako Miura (1968 - ) is a Japanese social psychologist. Professorof Department of Psychology, Faculty of Letters, Kwansei GakuinUniversity.

Fig. 1.

TABLE I.

Example of generated biographical sentences

Figure 1 shows an example of biographical sentences
generated for Asako Miura whose birth date, vocation, organization, and position were extracted. Since her birthplace was
not extracted, “Japanese” was added.
III.

E XPERIMENTAs queries, we used 20 Japanese person names that wereused in related work [3]. 50 web pages (HTML ﬁles) wereobtained for all 20 queries from web searches (i.e., 20 × 50 =1,000 HTML ﬁles). We manually classiﬁed these web pages
into different people. 80 people existed.
For the attribute information extraction, we checked all of
the extracted information for the 80 people using the following
evaluation measures:
P recision =correct answers by methodpeople to whom information was assigned by method(1)

Recall =correct answers by methodpeople to whom information was assigned manually(2)

Table I shows the results of the attribute information extraction. The precision of birth date, death date, and birthplaceexceeded 80% and seems reasonable. However, the recall ofV.

S UMMARY

We developed a method of generating biographical sentences for the results of web people search. We extracted
attribute information about people (birth date, death date,birthplace, vocation, organization, and position) and generated
biographical sentences whose style resembles the ﬁrst sentenceof Wikipedia. We evaluated the method using 20 queries(person names) × 50 web search results.
Acknowledgements.: This work was supported by JSPS
KAKENHI Grant Number 25330385, 16K00440.
R EFERENCES[1] X. Wan et al., “Person Resolution in Person Search Results: WebHawk,”
in: Proc. CIKM 2005, 2005, pp. 163–170.
[2] H. Ueda et al., “Assigning Vocation-Related Information to Person
Clusters for Web People Search Results,” in: Proc. GCIS 2009, 2009,
vol. 4, pp. 248–253.
[3] S. Sato et al., “Distinguishing between People on the Web with theSame First and Last Name by Real-world Oriented Web Mining,” IPSJ
Transactions on Databases, vol. 46, no. 8, pp. 26–36, 2005.
[4] B. Schiffman et al., “Producing Biographical Summaries: Combining
Linguistic Knowledge with Corpus Statistics,” in: ACL 01, 2001, pp.
458–465.
[5] C. Super and R. Barzilay, “Automatically Generating Wikipedia Articles:
A Structure-Aware Approach,” in: ACL 09, 2009, pp. 208–216.