Assigning NDLSH Headings
to People on the Web
Masayuki Shimokura and Harumi Murakami
Graduate School for Creative Cities, Osaka City University,3-3-138, Sugimoto, Sumiyoshi, Osaka 558-8585, Japanshimokura@gmail.com

Abstract. We investigate a method that assigns National Diet Library
Subject Headings (NDLSH) to the results of web people searches to help
users select and understand people on the web. NDLSH is a controlled
subject vocabulary list compiled and maintained by the National Diet
Library (NDL) as a subject access tool. By assigning NDLSH headings topeople, well-formed keywords can be assigned, and exploratory searches
using related terms are possible. We examined the following combinationof factors: (a) web-page rank (the number of pages), (b) position inside
the HTML, (c) synonyms, and (d) document frequency. We report ourexperimental results for 405 combination patterns (5 × 9 × 3 × 3) using
our 80-person dataset. Overall, under our experimental settings, the best
combination was (a) the top ten pages, (b) 100 characters before and after
a person’s name (i.e., 200 characters), (c) half weight for synonyms, and(d) document frequency divided by number of web pages.

Keywords: web people search, subject headings assignment, NDLSH, experiment1Introduction

The popularity of web people searches continues to rise as the number of people
increases about whom the web can provide information. If the list of web people
search results is merely “person 1, person 2, and so on,” users have diﬃculty
determining which person they should select. Appropriate labels shown withpeople should help users select the person they want.
In our previous work [1], we assigned people location information [2], vocationrelated information [3], and library classification numbers [4]. In this paper,
we investigate a method that assigns National Diet Library Subject Headings(NDLSH) to the results of web people searches to help users select and understand people on the web. The NDLSH [5] is a controlled subject vocabulary
list compiled and maintained by the National Diet Library (NDL) as a subject
access tool. By assigning NDLSH headings to people, well-formed keywords canbe assigned, and exploratory searches using related terms are possible.

We examined the following combination of factors: (a) web-page rank (thenumber of pages), (b) position inside the HTML, (c) synonyms, and (d) document frequency. We report our experimental results for 405 combination patterns(5 × 9 × 3 × 3) using our 80-person dataset.
Below, we explain our experiment in Sections 2 and 3, and the significance ofour research in Section 4. The examples presented in this paper were translated
from Japanese into English for publication.

2Method

2.1

Procedure

As queries, we used 20 Japanese names from a related work [6] and obtained
50 web pages per each query via Google Custom Search API. We manuallyclassified these pages into diﬀerent people and identified 80 separate people.
NDLSH headings were assigned to HTML files for each person.
First, we extract headings with variants (synonyms) and delete those with
two or fewer single-byte alphanumeric characters, those with only one doublebyte character, and those contain “--(two hyphens)” because they are less important terms and/or not very useful for text-matching.
With regard to terms (headings and synonyms), since longer strings provide
more specific meaning, we count the number of terms (headings and synonyms)
from the longer ones in the HTML documents without tags in the following
conditions: (a) and (b). For example, when processing the “artificial intelligence”
character string in a document, the term “artificial intelligence” is counted but
not the word “intelligence.” After counting the terms (headings and synonyms),
the scores of the headings are calculated.
We prepared the following four types of combination conditions:(a) Search ranking of web pages: five patterns of top 1, 3, 5, 10, and all pages
for each person.
(b) Position in HTML documents: nine patterns of title only, full text, and the20, 40, 60, 80, 100, 150, or 200 characters before and after a person’s name.
(c) Synonyms: three patterns that don’t use synonyms, using synonyms withidentical weights as the headings, and using synonyms with half of the weightof the headings.
(d) Document frequency of headings and synonyms: three patterns of doing
nothing, multiplying document frequency (df) / total number of used documents (N) (i.e., multiplying df/N), and multiplying total number of used
documents (N) / document frequency (df) (i.e., multiplying N/df). The last
one is rephrased as multiplying inverse document frequency (i.e., multiplyingidf).
When these conditions are combined, they become 5×9×3×3 = 405 patterns.
Fig.1 shows an example of the score calculation for headings. A heading havingwith the highest score is assigned to the corresponding person. If no heading isassigned, the answer becomes “none”.

Fig. 1. Example of score calculation

2.2

Evaluation

We manually selected the most appropriate NDLSH heading for each person(79 out of 80 people). For example, a “baseball” heading was selected for Suguru Egawa, a former professional baseball player, and “social psychology” wasselected for Asako Miura, a Kwansei Gakuin University professor.
The following are the evaluation measures:
Correctness =Precision =Recall =number of correct NDLSHs assigned automatically
number of people

number of correct NDLSHs assigned automatically
number of people to whom an NDLSH was assigned automatically
number of correct NDLSHs assigned automatically
number of people to whom an NDLSH was assigned manually
2 × Precision × Recall
Precision + Recall
number of correct answers
Accuracy =number of people
F − measure =When calculating the Accuracy, none is judged correct when there is nocorrect NDLSH heading for a person.

3Results and Analysis

Table 1 shows which pattern had the best correctness. The best pattern for
the whole (80 people) was “top ten pages, 100 characters before and after theperson’s name (total 200 characters), 0.5 times for synonyms, and df/N,” andits correctness was 26.3% (21/80).

Table 1. Patterns of high correctness
Number ofDocuments
AllRanking

11233 or more

1011 or more

103 to 10

510

Position

Synonym Document
Frequency
Before and After
0.5
df/N
100 Characters
Full Text
None
1Correctness

Before and After
200 Characters
Before and After
100 Characters
Before and After
100 Characters
Before and After
60 Characters

0.263(21/80)0.286(10/35)0.333(4/12)
0.364(12/33)0.500(9/18)
0.267(4/15)

0.511df/N

1df/N

0.5

df/N

Since we observed that the trend may diﬀer depending on the number ofdocuments for each person, we classified the number of documents into 1, 2, 3or more, 11 or more, 3 to 10, and conducted our evaluation. For 35 people with
only one document, “full text (not using synonyms)” was best. For 33 people
with 3 or more documents, the result was almost the same as for all the people(the diﬀerence is only the magnification of synonyms), and the result was thesame for 18 people with 11 documents or more.

Table 2. Evaluation for patterns of high correctness
Number of Documents Precision Recall F-measure Accuracy
All0.276 0.266
0.271
0.263
10.286 0.294
0.290
0.286
20.333 0.333
0.333
0.333
3 or more
0.364 0.364
0.364
0.364
11 or more
0.500 0.500
0.500
0.500
3 to 100.182 0.267
0.216
0.267

Evaluations for high correctness patterns are shown in Table 2. Precision andrecall are less than 30% for both all people and people with one document; they
are 50% for people with 11 or more documents.

Table 3. Evaluation using cosine
Number of Documents Precision Recall F-measure Accuracy
All0.026 0.025
0.026
0.025

For comparison, we implemented a method using cosine as a baseline. MeCab[7], a Japanese morphological analyzer, was used to extract terms from documents (web pages), headings and synonyms; nouns that contain two or morecharacters were extracted as terms. To match the conditions to our best pattern, we used the top ten pages, 100 characters before and after a person’s name(200 characters), synonyms, and document frequency. The weights of the terms
extracted from the headings were normalized by frequency × df/N, and theweights of the terms extracted from the synonyms were 0.5 times normalized
frequency × df/N. Table 3 shows the evaluation results with cosine. Our best
pattern significantly outperformed cosine.
Based on the above results, we found that we must narrow down to the top
ten rather than all of the whole web pages, use the character strings before andafter a person’s name rather than the full text of the HTML documents, andweight the words that appear in many documents using synonyms. However,the best approach was counting the number of headings from the full text for
those with only one document. These performances were much better than themethod using cosine.

4Related Work and Discussion

We roughly divided the methods for assigning controlled terms to documents intowith and without machine learning. Our research falls in the latter classification.
Cosine is one of the most commonly used baselines for non-machine learning
methods. The results obtained in this research are significantly higher than thebaseline.
In research that assigns Nippon Decimal Classification (NDC) numbers topeople on the web to develop a person directory [4], we considered two conditionsof documents × methods. The title (texts inside title tags) was best among thedocument conditions, and the result diﬀers from this research. We believe that
this is due to the diﬀerence of research purposes and dataset. [8] suggested thatthe best patterns were diﬀerent based on the number of documents per person(i.e., 1, 2, 3 or more documents), and this is related to this research.
There is research that assigns labels to people except for our previous work.
Wan et al. [9] assigned titles (including vocations) and Mori et al. [10] assigned
keywords to person clusters. WePS-2/3 [11] conducted competitive evaluation on
person attribute extraction on web pages. No such research has assigned subject
headings to people on the web.
Our experimental results revealed the best pattern for assigning NDLSH
headings to people on the web. To the best of our knowledge, this is the firstresearch that assigns library subject headings to people on the web.

5Conclusions

We investigated a method that assigns NDLSH headings to the results of web
people searches to help users select and understand people on the web. We exam-ined the following combination of factors: (a) web-page rank, (b) position inside
HTML, (c) synonyms, and (d) document frequency. We reported the results ofour experiment for 405 patterns with an 80-person dataset. Overall, under ourexperimental settings, the best combination was (a) the top ten pages, (b) 100
characters before and after a person’s name (i.e., 200 characters), (c) half weight
for synonyms, and (d) document frequency divided by the number of web pages.
Future work will examine the use of stop lists and such terms as broader,narrower, and related terms. We also need to evaluate our method for otherheadings (except for highest score) and with large and diverse datasets.
Acknowledgements. This work was supported by JSPS KAKENHI Grant
Number 25330385, 16K00440.

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