System for Category-driven Retrieval of Historical Events
Yasunobu Sumikawa∗

Adam Jatowt

University Education Center,
Tokyo Metropolitan University
ysumikawa@acm.org

Dept. of Social Informatics,
Kyoto University
adam@dl.kuis.kyoto-u.ac.jp

ABSTRACTIn this paper, we demonstrate an online system for historical event
retrieval. Our system outputs ranked events according to an input
text query, time range and category relevance. It is useful for users
searching not just for important past events related to input entities
but events that belong to specified subset of general categories.
It can be also helpful for creating datasets of events falling into
specific categories or for generating specialized timelines.

KEYWORDS
Digital history, historical texts, event ranking
ACM Reference Format:
Yasunobu Sumikawa and Adam Jatowt. 2018. System for Category-driven
Retrieval of Historical Events. In JCDL ’18: The 18th ACM/IEEE Joint Conference on Digital Libraries, June 3–7, 2018, Fort Worth, TX, USA. ACM, New
York, NY, USA, 2 pages. https://doi.org/10.1145/3197026.3203888

1INTRODUCTION

Studying and analyzing history-related data can provide numerousbenefits including improved comprehension of the past and support
for finding meaningful connections or analogies over time. Oneof the common goals of teaching and spreading the knowledge ofhistory is to allow studying how people in the past tried to solve
issues and problems, and then apply the acquired knowledge for
proposing creative solutions to present issues [4].
Due to the recent information explosion the amount of available data about historical events have been also increasing. Suchdata includes both the digitized archival documents such as news
articles as well as collections of retrospective descriptions of pastevents (e.g., past events’ lists in Wikipedia). This growth demands
effective retrieval approaches to let users quickly access what theywant. For example, a user (e.g., a journalist or a university student)
may be interested in health and environment past events involving
Japan that took place in the 19th century. He or she may wish tocreate a specialized timeline representing this particular domain(i.e., health and environment) or may just want to acquire dataset ofrelevant events for detailed study or as a initial data seed for more
extensive retrieval. Yet, such domain-specific event collections are
not immediately available on the Web 1 .
∗ This research was performed while the first author was at Tokyo University of Science, Japan.
1 E.g., Wikipedia generally structures important past events chronologically

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ACM ISBN 978-1-4503-5178-2/18/06.
https://doi.org/10.1145/3197026.3203888

We propose an interactive search system for searching for historical events by category-based filtering realized through automatic
event classification. Our system takes four kinds of data: text query,
event category, time range and ranking method. It outputs only
event descriptions that include the query terms and those thatoccurred within the specified time range. More importantly, thereturned events are sorted based on their relevance to the selected
event category or a set of categories.
Contribution: Compared to related works, the core contribution of our system is to use effective filtering for collecting categoryspecific range of events.
Singh et al. [5] proposed method for supporting historians insearching within document archives. Their approach aims at maximizing coverage and minimizing redundancy with respect to different entities and publication times of documents for a given query.
For example, if a person name is given as a query, the method
lists news articles reporting the person with different entities andpublication times. Sumikawa and Ikejiri [6] proposed approach for
temporal analogy retrieval which uses a category-based ranking
method. Their search engine performs matrix multiplication to sort
past events by the number of categories they share with the categories specified by the user. It assumes however the categories
for all the past events are given in advance. In contrast, our searchengine provides more search options and is based on automatic
event classification using Support Vector Machine (SVM) classifier,thus, it can handle any collections of unlabeled past events.

2EVENT CLASSES AND EVENT COLLECTION

Event Classes. We adopted nine broad event classes introduced
and described in [3] which are based on definitions and guidelines used by Wikipedia editors. The classes are as follows: Armed
Conflicts & Attacks, Arts & Culture, Business & Economy,
Disasters & Accidents, Health & Environment, Law & Crime,
Politics & Elections, Science & Technology and Sport.
Dataset. As an underlying event dataset we collected 70,987 past
events from year articles in Wikipedia2 and Wikipedia’s Current
Portal3 . The timespan of the collected events ranges from AD 1 toAD 2016. On average, for all the classes, the descriptions contain
25 words, though the length can be as short as 10 words.

3

SYSTEM DESCRIPTION

Fig. 1 shows the interface of the proposed system4 . Besides inputting
the query, a user can control three options: event category, time
range and ranking method. The input query and the selected time
range act as filters preparing a subset of events that occurred inthe designated time frame and that contain specified query words(typically, name of entities). When no filtering is specified (null2 E.g., https://en.wikipedia.org/wiki/AD_1
3 https://en.wikipedia.org/wiki/Portal:Current_events
4 The system can be accessed online: http://tk2-240-29917.vs.sakura.ne.jp/dataset_creation/

Figure 1: Snapshot of system interface.

query and unbounded time) all events stored in the database areconsidered. The category option allows for ranking the selectedevents regarding their inherent types. The ranking is based on thedegree to which a dedicated classifier (described further in Sec. 3.2)
judges an event to be of a particular class. Note that unlike usualapproaches in information retrieval (IR) we do not focus on the
topical relevance but, instead, we put special emphasis on category
relevance. Hence, in the current implementation we use a condition
for an event description to represent a relevant event based on
simple query containment5 . Obviously more refined approaches
for topical relevance assessment (e.g., semantic ones) can be usedinstead.
To sum up, event descriptions that contain input query words
and that describe events taking place within the specified date range
are ranked based on how strongly they are recognized as belonging
to the particular set of event categories. The last option, ranking
method, determines the way in which the category probabilities
are aggregated for ranking event descriptions.

3.1

Classifying Events

To decide event classes, we used SVM with RBF kernel equippedwith the following feature groups which are described in detailin [7]: (1) TF-IDF term vectors, (2) LSA vectors (300 dimensions),(3) Doc2Vec vectors (300 dimensions), (4) VerbNet classes of verbs
and ones of head verbs, (5) general entity types detected by Yodie[2] and the type of a head entity, (6) TF-IDF weighted vectors ofterms captured from articles and their categories as returned by
Explicit Semantic Analysis (ESA) [1] which maps target texts toWikipedia articles. Using feature selection (k = 2, 000) and 10fold cross validation our classifier achieved 79.7% of F-measure(Precision of 78.4% and Recall of 79.6%). As labeled data for training
we used the Wikipedia’s Current Portal collecting 32,362 event
descriptions.

3.2

Ranking Algorithm

Our system loads subsets of feature vectors created in Sec. 3.1 only
if the occurrence time of their corresponding events matches thetime frame T and if the event descriptions contain the input query
Q given by the user. The system then applies SVM to the feature
5 This choice is partly driven by the fact that event descriptions in our dataset are very short. We

also assume a user query is going to represent a named entity in most of the cases.

vectors in order to estimate to what degree each considered event isrelevant to the concerned event classes. The probabilities of classes
as estimated by SVM are used as confidence representation6 .
As multiple categories can be input by the user, our systemaggregates membership probabilities over different categories. We
provide two kinds of aggregation methods: Max and Ave. The firstis the straightforward approach; it ranks events by their maximum
confidence among all the selected categories. On the other hand, thesecond one aggregates based on computing the average confidence.
The motivation behind the latter approach is that users may wantto collect events belonging to more than one class. For example,
the outbreak of Zika virus in 2016 caused death of many people(Health & Environment event) but also resulted in the decrease in
the population of bees (Disasters & Accidents event). To return
such events one needs to ensure high confidence probabilities ofboth the classes. Hence, we provide also an option to calculate
average probability values for ranking events.
Finally, our system sorts all the events by their relevant values
in descending order and returns them to the user.
More formally, the ranking algorithm is as follows:E′={e | e ∈ E ∩ year(e) ∈ T ∩ Q ∈ w(e)}(1)

S(f , e ∈ E ′ )=f (CatRel(FV (e)))(2)

where function year(e) returns a year when the event e occurred,function w(e) returns words used in e and FV (e) outputs a feature
vector of e. E is the total set of event descriptions, while E ′ is thesubset of E after the time range and query based filtering. For allthe events, our ranking algorithm applies the higher-order function
S that takes two arguments, a function f that is either Max or Ave
and the set of feature vectors of e. Then, the algorithm sorts theevents by the scores S as being aggregated by f .

4CONCLUSIONS & FUTURE WORKIn this paper, we demonstrate an interactive online system forretrieving past event descriptions. Our system takes query words,
time range and category relevance as an input in order to effectively
collect events that related to query, that occurred at a specified
time range and, most importantly, that fall into a particular event
category. In the future, we plan to propose incorporating more
effective methods for representing topical relevance and we planto add historical importance scores of events.
Acknowledgments. This work was supported in part by MIC SCOPE(#171507010) and MEXT Grant-in-Aids (#17H01828 and #17K12792).

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6 We store the precomputed values offline for online retrieval.