Automatic Extraction of Event Informationfrom Newspaper Articles and Web Pages
Hidetsugu Nanba, Ryuta Saito, Aya Ishino, and Toshiyuki Takezawa
Hiroshima City University, Graduate School of Information Sciences,3-4-1, Ozukahigashi, Asaminamiku, Hiroshima 731-3194 JapanAbstract. In this paper, we propose a method for extracting travelrelated event information, such as an event name or a schedule fromautomatically identiﬁed newspaper articles, in which particular eventsare mentioned. We analyze news corpora using our method, extracting
venue names from them. We then ﬁnd web pages that refer to event
schedules for these venues. To conﬁrm the eﬀectiveness of our method,
we conducted several experiments. From the experimental results, we
obtained a precision of 91.5% and a recall of 75.9% for the automatic
extraction of event information from news articles, and a precision of
90.8% and a recall of 52.8% for the automatic identiﬁcation of eventrelated web pages.
Keywords: newspaper article, web page, event, travel.

1

Introduction

Information about events that are scheduled to take place at the travel destination is crucial when planning a trip. Travel guidebooks and portal sites provided
by tour companies and government tourist oﬃces are useful for checking on
well-known events, such as festivals. However, it is costly and time consuming tocompile information about the full range of events for all tourist spots manuallyand to keep this data up-to-date. We have therefore investigated the automaticcompilation of event information.
We use newspaper articles and web pages as information sources for the extraction of event information. In general, we can obtain information about popular or traditional events through newspaper articles. However, only a small
percentage of the full range of travel-related events appears in newspaper articles. Therefore, we propose a method to collect travel-related event informationfrom both newspaper articles and web pages. Generally, public venues, such as
museums or exhibition halls, have their own web sites, which contain web pages
that give schedules for events at these venues (we call them “event web pages”).
We therefore extract venue names, where public events often occur from news
corpora, and then identify event web pages using this list. Using this procedure,
we can expect to obtain much travel-related event information rapidly.
The remainder of this paper is organized as follows. Section 2 describes relatedwork. Section 3 describes our method. To investigate the eﬀectiveness of ourS.R. Urs, J.-C. Na, and G. Buchanan (Eds.): ICADL 2013, LNCS 8279, pp. 171–175, 2013.
c Springer International Publishing Switzerland 2013
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method, we conducted experiments, with Section 4 reporting the results. We
present some conclusions in Section 5.

2Related WorkIn previous work on extracting event information from texts, Schneider [2] andIssertial et al. [1] proposed a method for extracting academic event information,such as schedules, conference locations, and conference names using “Call forPapers” documents as information sources. In our work, we use news corpora
and web pages as our information sources.

3

Construction of the Event-Retrieval System

We obtain the event information in two steps: (1) extraction of event informationfrom newspaper articles and (2) identiﬁcation of event web pages from the web.
We describe these two steps in Sections 3.1 and 3.2, respectively.
3.1

Extraction of Event Information from Newspaper Articles

We use information extraction based on machine learning to extract event information from event news articles. First, we deﬁne the tags used:– EVENT tag includes an event name;– DATE tag includes a schedule for an event;– ADDRESS tag includes an address of a venue;– LOCATION tag includes a venue name.
We formulate the identiﬁcation of the class of each word in a given sentence byusing machine learning. We opted for the CRF machine-learning method, wherethe features and tags used are as follows: (1) k features occur before a target
word and (2) k features follow a target word. We use the value k = 3, which was
determined via a pilot study. We use the following features for machine learning.
(We use MeCab as a Japanese morphological analysis tool to identify the partof speech):– a word;– its part of speech;– whether the word is a quotation mark;– whether the word is frequently used in the event names, such as “live”,“festival”, or “fair”;– whether the word is frequently used in the names of venues, such as “gallery”,“stage”, “building”, or “hot spring”;– whether the word is a numerical descriptor;– whether the word is frequently used in the address, such as “prefecture” or“city”;– whether the word is frequently used in schedules, such as “date”, “schedule”,or “tomorrow”.

Automatic Exraction of Event Information

3.2

173

Identification of Event Web Pages

For the identiﬁcation of event web pages, we ﬁrst search web pages using pairs
comprising a venue name1 and the word “ibento” (event) as the query. We next
identify event web pages using cue words and unnecessary words as features formachine learning. Examples of the use cue phrases and unnecessary words are:– whether the web page includes cue words, such as “schedule”, “date”, “participation fee”, “event”, “information”, or “fair”;– whether the web page includes unnecessary words, such as “youtube”, “blog”,“twitter”, “livedoor”, “facebook”, “wikipedia”, or “mapple”;– whether the web page’s URL includes cue words, such as “event” or “schedule”;– whether the URL includes unnecessary words, such as “youtube” or “facebook”;– whether the web page includes a table.

4Experiments

We conducted two experiments to test (1) the extraction of event informationfrom news articles, and (2) the identiﬁcation of event web pages.
4.1 Extraction of Event Information from News Articles
Data Sets and Experimental Settings
We used 416 event newspaper articles with four kinds of tags, namely EVENT,DATA, ADDRESS, and LOCATION, annotated manually.
Machine Learning and Evaluation Measures
We employed CRF as our machine learning technique, and performed a fourfold cross-validation test. As a baseline method, we used words and their parts ofspeech as features for machine learning. As evaluation measures, we again usedrecall, precision, and F-measure.
Experimental Results
We show the experimental results in Table 1. To compare with the performance of our method, we calculated scores of recall, precision, and F-measure,when extracting the whole articles as event news articles.
As shown in the table, our method improved the recall score by more than
0.1 without impairing the precision score. However, our method was unable
to extract event information in 24.1% of cases, caused mainly by the lack oflinguistic clues. Table 2 shows examples of errors when our method failed. In
the EVENT error, “HARA Asao ten” (Hara Asao exhibit) should have been
extracted, but our method failed, because no cue words, such as quotation marks
or particles (topic marker), except for “ten” (exhibit) appear in this context. In
the ADDRESS error, there are no linguistic clues around the location name“Umeda”, which should have been extracted as an address.
1

We obtained 30,000 venue names from news corpora automatically using the methodof Section 3.1.

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Table 1. Evaluation results for the extraction of event information from news articles
Baseline
Our method
Precision Recall F-measure Precision Recall F-measure
EVENT
0.899 0.510
0.651
0.912 0.673
0.774
0.965 0.799
0.874
0.968 0.855
0.908
DATE
0.910 0.769
0.833
0.908 0.816
0.860
ADDRESS
0.896 0.547
0.679
0.873 0.693
0.773
LOCATION
Average
0.918 0.656
0.765
0.915 0.759
0.830

Table 2. Examples of errors in the extraction of event information from event articles
EVENT error

kajin, HARA Asao ten deha...
(at the poet HARA Asao exhibit...)ADDRESS error 2/1 made, Daimaru * Umeda no 11 F
(on the 11th ﬂoor of Daimaru (department store) * Umeda,
until Feb. 1.)

4.2 Identification of Event Web Pages
Data Sets and Experimental Settings
We used 1,022 web pages containing the word “ibento” (event) for our experiment. Of these, 264 web pages were identiﬁed manually as event web pages.
Machine Learning and Evaluation Measures
We performed a four-fold cross-validation test, again using TinySVM softwareas the machine-learning package. We again used recall, precision, and F-measure
as evaluation measures.
Experimental Results
We show the experimental results in Table 3. As baseline results to compare with those of our method, we calculated scores for recall, precision, andF-measure, when extracting all pages as event web pages.
Our method achieved the high precision score of 0.824, while the recall scoreremained at 0.522. In many of the event web pages that our method could notidentify, event schedules were written in a tabular form. Although our method
does check for web page containing tables, as one of the features for machinelearning, these pages did not contain additional linguistic cues, which our method
requires for proper identiﬁcation.
Table 3. Evaluation results for identiﬁcation of event web pages
Methods
Precision Recall F-measure
Baseline
0.258 1.000
0.410
Our method
0.824 0.522
0.639

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Conclusions

We have proposed a method for extracting event information from both newspaper articles and event web pages. In the extraction of event information fromnews articles, we obtained a precision of 91.5% and a recall of 75.9%. For theidentiﬁcation of event web pages, we obtained a precision of 90.8% and a recallof 52.8%. Our method outperformed a baseline method, thereby conﬁrming theeﬀectiveness of our method.

References
1. Issertial, L., Tsuji, H.: Information Extraction and Ontology Model for a ‘Call for
Paper’ Manager. In: Proceedings of iiWAS 2011, pp. 539–542 (2011)2. Schneider, K.-M.: An Evaluation of Layout Features for Information Extraction
from Calls for Papers. In: LWA 2005, pp. 111–115 (2005)