A Tool for Automatic Extraction of Informationfrom Company Web Sites in the Field of International ServitizationGábor Berend

Christian Stadlmann

University of Szeged
berendg@inf.u-szeged.hu

University of Applied Sciences Upper Austria
christian.stadlmann@fh-steyr.at

Stefan Mang

Margarethe Überwimmer

University of Passau
stefan.mang@uni-passau.de

University of Applied Sciences Upper Austria
margarethe.ueberwimmer@fh-steyr.at

Abstract
This paper introduces the first prototype of a tool developed
as part of an interdisciplinary applied research effort. Our
long-term goal is to develop such a service which is capable of providing valuable information aiding decision making regarding international servitization. Our platform is currently able to identify the industry for some company basedon the contents of its web site. Key terms, including the extraction of multiword expressions – that we deem as relevant
industry-specific terms – and named entities are also part ofour demo application. The platform also incorporates a preliminary information retrieval component, which returns themost similar company websites included in our database ofcompanies. Our demo is accessible from https://rgai.inf.uszeged.hu/prosperAM/demo.

1Introduction

There has been a recent interest in the computational processing and treatment of financial and business documents(Hahn, Hoste, and Tsai 2018; Chen et al. 2019; Hahn, Hoste,and Zhang 2019; Mahmoud El-Haj et al. 2019). Extracting
automatically information from the myriad of semi,- and unstructured data sources – including corporate web sites – canprovide a valuable source of information to decision makes
in industry.
In this paper, we introduce the first working componentof an online tool which will be integrated into a platformfor providing information to companies planning to export
their services to some foreign market. Our long term goal isto provide companies such a platform which can aid themin assessing their perspectives for exporting services to foreign markets. We think that a first step towards this goal is
to become able to present companies their competitors. To
this end, we developed an online tool which can categorize
the business profile of a company based on its website, thenalso provides a ranked list of the most similar companies
included in our database. Key industry-related terms – also
extracted from the corporate websites – are currently also
determined by our tool.
Copyright c 2020, Association for the Advancement of ArtificialIntelligence (www.aaai.org). All rights reserved.

Due to the interdisciplinary nature of our project, we next
provide a brief overview of the theory of international servitization, then detail our technical contributions.

2International Servitization

In the last decades services provided by manufacturing companies have moved into the focus of both practitioners andresearchers (Oliva and Kallenberg 2003; Baines, Lightfoot,and Smart 2011). Supplementing products with value enhancing services is seen as a clever manoeuvre to reacha competitive advantage (Gebauer, Gustafsson, and Witell2011), profitability and economic stability (Bandinelli andGamberi 2011). According to Service Dominant Logic additional services are central for direct value enhancementof the physical product, which are themselves viewed asa mere distribution mechanism for service delivery (Vargoand Lusch 2004). However, the success of servitizing manufacturers remains debated as research shows mixed results(Gebauer 2005; Wang, Lai, and Shou 2018). The existenceof service business results even in higher bankruptcy risks
for manufacturing firms (Benedettini, Neely, and Swink2015). These failure risks are particularly increased if services are provided in different countries (Zarpelon Neto2015). In international business further challenges emanatefrom outside the company as changes in technology, regulations, competition or customer demands. Moreover, failure
can derive from wrong managerial decision-making as about
local investments, disadvantageous contracts with customers
or network partners or falling behind resident competitors(Benedettini, Neely, and Swink 2015).
Hence, when implementing services the performance ofmanufacturing companies may vary in different regions(Szász et al. 2017). Indeed, manufacturing firms with thesame extent of servitization may achieve diverse performance levels in different geographic regions or if they operate in distinct industrial market sectors (Wang, Lai, and Shou2018). Therefore, it may be concluded that the regional market conditions are decisive for success or failure in sertivitization (Baines et al. 2017). Above, the global competition
has changed dramatically in the last decade. In 2007, globally more than 30 percent of manufacturing firms have already been servitized (Neely 2008). In 2018 though, a radi-cal increase of servitization has been identified in many economic areas around the globe as in China where the numberof servitized manufacturing firms increased from 17 percent
to 38, which is similar to the German level of servitization(Mastrogiacomo, Barravecchia, and Franceschini 2019).
However, the understanding of this external context is still
undeveloped in research and there is a strong need of a descriptive comprehension of the broader environment and social aspects of servitization as well as for prescriptive studies about the external conditions influencing the right time toimplement or adapt a service strategy of manufacturing companies (Baines et al. 2017). From managerial point of view,a continuous monitoring of markets and their characteristics
are fundamental for mitigating failure risks and recognizing
potential market opportunities.
Finding an approach to successfully engage within a market is a difficult investigative and planning challenge due toits nature of an ill-structured problem Grünig (2017). Thus,
two heuristic principles should be applied for solving thisanalytic problem, i.e. the rule of factorization breaking down
the issue into sub-problems and the procedure of modellingfor developing proven solution methods (Grünig 2018). Following Grünig (2017) it is out of purpose to examine alarge number of country and industry market combinations.
Therefore, the focus of this paper is specifically on the US
market as it is one of the most promising regions for European manufacturing companies (International Trade Center2019).
Moreover, various market characteristics are important
for the success of servitization as the political, economic,
competitive, social, technological, environmental, industrial,and regulatory components (Baines et al. 2017). Hence,the developed prototype specifically focuses on industryspecific and competitive elements which are starting points
for a better understanding of the foreign market for servitization. This is done by automatic processing of company web
sites which may be competitors, service or network partners
or potential leads for servitization activities of manufacturing firms.

3

The technical contributions

We next describe the current functionalities of our platform
and provide their technical implementation details.

3.1

Classifying websites according to SIC codes

Standard Industrial Classification (SIC) provides a finegrained hierarchical categorization of industrial activities. Asmall sample of the entire categorization hierarchy can beseen in Table 1.
We used the fasttext (Joulin et al. 2016) Python package for predicting the most appropriate SIC code for a given
company based on its corporate web site. In order to train
our model, we relied on the 2017 version of a US businessdatabase1 which contains information on several millions ofcompanies, including their addresses, contact person names
and most importantly, the URL of their website along withtheir SIC.
1

https://www.uscompanieslist.com/

Table 1: A sample excerpt of the SIC categories
SIC code
Industry
1000
1040
1090
..
.

Metal Mining
Gold and Silver Ores
Miscellaneous Metal Ores
..
.

1731
2000
2011
..
.

Electrical Work
Food and Kindred Products
Meat Packing Plants..
.

Not all companies have a website included in thedatabase, furthermore a fair share of the URLs were nolonger affiliated with the company any more, e.g. they wereselling the domain instead of providing content related to its
onetime corporate owner. In order to reduce the noise fromthe training procedure, we applied a handful of heuristics
which made semi-automatic removal of non-relevant (URL,SIC) pairs possible. In the end we were left with approximately 400K corporate URLs that we could train our SICclassifier on.
The websites that we used for training were processed
only for their opening page, and the same applies for ourmodel when it makes inference to unseen URLs.

3.2

Extracting industry terms

Multi-word expressions (MWEs) are such white space delimited sequences of tokens that bear some idiosyncratic
behavior, such as oil refinery and steel manufacturing. Ofcourse, not all MWEs are relevant from an industry pointof view, e.g. cold war and privacy policy. Nonetheless wemake the simplifying assumption that MWEs can be treatedas such expressions which can be usefully applied to further
characterize and refine company profiles beyond their SIC.
To this end, we trained an MWE detector based on theW IKI 50 training dataset (Vincze, Nagy T., and Berend2011). For determining the multiword expressions (aka. industry terms), we use the same fast conditional random field(CRF) based sequence classification architecture that we introduced earlier for POS tagging and named entity recognition in (Berend 2017). Our implementation is based on thehighly-efficient CRFsuite package (Okazaki 2007).

3.3

Basic IR component

Our platform currently offers a basic information retrieval
component as well. As mentioned already in Section 3.1,
we have compiled a collection of corporate websites consisting of nearly 400K URLs. Upon determining the most likely
SIC for a website entered, we also perform a quick rankingof those corporate websites that belong to the same SIC andreturn those companies which has the highest overlap to thequeried company in terms of their industry terms.
A screenshot from our application can be seen in Figure 1.
The application itself is publicly available from https://rgai.
inf.u-szeged.hu/prosperAM/demo.

Figure 1: A screenshot from the online service

4Conclusions and future work

We have introduced the first component of our platform forproviding help for companies upon making business decisions for their international servizitation. Our tool is currently able to identify the industry of a company based on
its corporate website and returns a ranked list of similar corporate websites. We are planning to improve our results byhandling multilingual documents as well, and integrating alarge scale web crawling mechanism instead of building theinformation retrieval component on a fixed pool of companies.

Acknowledgements
This research has been partly conducted in the project “Progressing Service Performance and Export Results of Advanced Manufacturers Networks”, no CE1569 ProsperAMnet. The project has been supported by the European Fundfor Regional Development in the framework of Interreg
CENTRAL EUROPE 2019-2022. Moreover, we like to acknowledge the early ideation and financial contributions ofPhilipp Schachinger of Salesbeat GmbH.

References
Baines, T.; Ziaee Bigdeli, A.; Bustinza, O. F.; Shi, V. G.;
Baldwin, J.; and Ridgway, K. 2017. Servitization: Revisiting the state-of-the-art and research priorities. International Journal of Operations & Production Management37(2):256–278.
Baines, T.; Lightfoot, H.; and Smart, P. 2011. Servitization
within manufacturing: Exploring the provision of advanced
services and their impact on vertical integration. Journal ofManufacturing Technology Management 22(7):947–954.
Bandinelli, R., and Gamberi, V. 2011. Servitization in oiland gas sector: outcomes of a case study research. Journalof Manufacturing Technology Management 23(1):87–102.

Benedettini, O.; Neely, A.; and Swink, M. 2015. Why
do servitized firms fail? a risk-based explanation. International Journal of Operations & Production Management35(6):946–979.
Berend, G. 2017. Sparse coding of neural word embeddings
for multilingual sequence labeling. Transactions of the Association for Computational Linguistics 5:247–261.
Chen, C.-C.; Huang, H.-H.; Takamura, H.; and Chen, H.H. 2019. Proceedings of the First Workshop on Financial
Technology and Natural Language Processing.
Gebauer, H.; Gustafsson, A.; and Witell, L. 2011. Competitive advantage through service differentiation by manufacturing companies.
Journal of Business Research64(12):1270–1280.
Gebauer, H. 2005. Overcoming the service paradox inmanufacturing companies. European Management Journal23(1):14–27.
Grünig, R. 2017. Developing International Strategies.
Berlin, Heidelberg: Springer, 2nd ed. 2017 edition.
Grünig, R. 2018. The Strategy Planning Process: Analyses,Options, Projects. 2nd ed. 2018 edition.
Hahn, U.; Hoste, V.; and Tsai, M.-F. 2018. Proceedingsof the First Workshop on Economics and Natural Language
Processing. Melbourne, Australia: Association for Computational Linguistics.
Hahn, U.; Hoste, V.; and Zhang, Z. 2019. Proceedings of theSecond Workshop on Economics and Natural Language Processing. Hong Kong: Association for Computational Linguistics.
International Trade Center. 2019. International trade
in services statistics by service. Exports 2000-2018.
http://www.intracen.org/itc/market-info-tools/statisticsexport-service-country/. Accessed: 2019-11-15.
Joulin, A.; Grave, E.; Bojanowski, P.; and Mikolov, T. 2016.
Bag of tricks for efficient text classification. arXiv preprint
arXiv:1607.01759.
Mahmoud El-Haj; Rayson, P.; Young, S.; Bouamor, H.; andFerradans, S. 2019. Proceedings of the Second Financial
Narrative Processing Workshop (FNP 2019). Turku, Finland: Linköping University Electronic Press.
Mastrogiacomo, L.; Barravecchia, F.; and Franceschini, F.
2019. A worldwide survey on manufacturing servitization.
The International Journal of Advanced Manufacturing Technology 103(9-12):3927–3942.
Neely, A. 2008. Exploring the financial consequences ofthe servitization of manufacturing. Operations Management
Research 1(2):103–118.
Okazaki, N. 2007. Crfsuite: a fast implementation of conditional random fields (crfs). URL http://www. chokkan.
org/software/crfsuite.
Oliva, R., and Kallenberg, R. 2003. Managing the transition
from products to services. International Journal of Service
Industry Management 14(2):160–172.
Szász, L.; Demeter, K.; Boer, H.; and Cheng, Y. 2017.
Servitization of manufacturing: the effect of economic con-text. Journal of Manufacturing Technology Management28(8):1011–1034.
Vargo, S. L., and Lusch, R. F. 2004. Evolving to a new dominant logic for marketing. Journal of Marketing 68(1):1–17.
Vincze, V.; Nagy T., I.; and Berend, G. 2011. Multiword expressions and named entities in the wiki50 corpus. In Proceedings of the International Conference Recent Advancesin Natural Language Processing 2011, 289–295. Hissar,
Bulgaria: Association for Computational Linguistics.
Wang, W.; Lai, K.-H.; and Shou, Y. 2018. The impact ofservitization on firm performance: A meta-analysis. International Journal of Operations & Production Management38(7):1562–1588.
Zarpelon Neto, G. 2015. What problems manufacturing companies can face when providing services aroundthe world? Journal of Business & Industrial Marketing
30(5):461–472.