HUMAN LANGUAGE TECHNOLOGIES FOR E-GOV

Mário Rodrigues

, Gonçalo Paiva Dias

ESTGA

1,2

/ IEETA

/ GOVCOPP

, University of Aveiro, Aveiro, Portugal

António Teixeira

DETI / IEETA, University of Aveiro, Aveiro, Portugal

Keywords:

e-gov, Life-event, Human language technologies, Natural language interface, Information extraction.

Abstract:

Effective provision of government services implies that, besides being provided online, services become avail-

able through other channels, are organized according to citizen’s expectations, are accessible to everyone,

anytime and anywhere, and include information from unstructured sources. It is also essential to provide

the tools that allow citizens to correctly identify the services they need. In this paper we will discuss how

it is possible to improve e-gov service delivery by using human language technologies. We argue that these

technologies can contribute to: deliver services in more inclusive manners; provide human centered and mul-

tilingual service and support; and include non-structured information scattered across different sources.

1 INTRODUCTION

eGov “refers to the use by government agencies of in-

formation technologies (such as the Internet and mo-

bile computing) that have the ability to transform re-

lations with citizens, businesses, and other arms of

government. These technologies can serve a variety

of different ends: better delivery of government ser-

vices to citizens, improved interactions with business

and industry, citizen empowerment through access to

information, or more efﬁcient government manage-

ment. The resulting beneﬁts can be less corruption,

increased transparency, greater convenience, revenue

growth, and/or cost reductions” (World Bank, 2009).

Human language technologies (HLT) refer to

speech and natural language processing (NLP). Re-

search and development in speech include automatic

speech recognition (ASR) to get a textual representa-

tion of a speech sound wave, and text to speech (TTS)

to generate a speech sound wave representing a given

text. NLP “refers to computer systems that analyze,

attempt to understand, or produce one or more hu-

man languages, such as English, Japanese, Italian, or

Russian. The input might be text, spoken language,

or keyboard input. The task might be to translate to

another language, to comprehend and represent the

content of text...” (Allen, 2003).

Paper structure: in this paper we will discuss how it

is possible to improve e-gov services by using HLT.

After a brief overview of needs regarding better ser-

vice deliveryin e-gov (section 2), followsan overview

of HLT (section 3) and the potentials of its application

to e-gov (section 4). The last sections of the paper ad-

dress some recent related work, our ongoing work and

the relevant conclusions.

2 CITIZEN NEEDS IN E-GOV

eGov is “a dynamic concept of varying meaning and

signiﬁcance” (Relyea, 2002). It is commonly used

to refer to several alternative or complementary con-

cepts, including the use of the Internet in the interac-

tion between governmentand citizens or businesses, a

reengineering of government processes catalyzed by

Information and communication technologies (ICT),

and a symbol of ICT usage in increasing the efﬁciency

and effectiveness of government.

Electronic service provision is probably the most

common approach to e-gov. In effect, ICT allows tra-

ditional services to become online, with clear bene-

ﬁts for clients (citizens and businesses) and govern-

ment, avoiding travel, speeding up processes and di-

minishing costs. However, besides being delivered

online, government services should also be integrated

in such a way that they ﬁt the citizens and business

400

Rodrigues M., Paiva Dias G. and Teixeira A.

HUMAN LANGUAGE TECHNOLOGIES FOR E-GOV.

DOI: 10.5220/0002857804000403

In Proceedings of the 6th International Conference on Web Information Systems and Technology (WEBIST 2010), page

ISBN: 978-989-674-025-2

concrete needs (Dias and Rafael, 2007). One-stop e-

gov corresponds to this perspective: services should

be provided through a single entry point and be in-

tegrated across agencies from the client’s point of

view (Wimmer et al., 2001). They can be organized

into life-events (as birth of a child, marriage, etc) tar-

geted at speciﬁc costumers at particular times in their

life (Oteniya et al., 2006).

Digital divide refers to the gap between people

with effective access and capacity to use ICT and

those with very limited or no access or capacity at

all. If not properly addressed, online service provi-

sion can contribute to increase this problem. Before

using an e-gov service users sometimes cannot iden-

tify which public administration institutions provide

the services they need and what inputs are required

to execute the service (Sroga, 2008). It is necessary

to have tools to help every client to ﬁnd and use the

appropriate service regardless of its level of expertise

in government services or ICT, because government

must serve 100% of its clients (Trochidis et al., 2008).

Finally, the development of a customer oriented

approach implies that government agencies learn how

to communicate with each other, interoperation be-

coming a crucial issue. To this respect, it is important

to note that a very relevant set of government infor-

mation is not stored in searchable databases. Many

documents although digitally stored, remain in their

original format. eGov services should be able to in-

clude and combine these sources of information.

3 HUMAN LANGUAGE

TECHNOLOGIES

3.1 Interface Technologies

A natural language interface allows people to inter-

act using a human language, such as English, as op-

posed to a computer language, command line inter-

face, or graphical user interface. Spoken language in-

terfaces are capable of handling spoken human lan-

guage. Advances in ASR, language understanding,

language generation, and speech synthesis (see Juraf-

sky and Martin, (2000) for technical details) enabled

the emergence of complex conversational spoken lan-

guage interfaces (Bohus and Rudnicky, 2009).

These systems are typically connected in a

pipeline architecture (Bohus and Rudnicky, 2009).

The audio signal from the user is captured and passed

through a ASR module that produces a sequence of

words. This recognition hypothesis is forwarded to

a language understanding component to create a cor-

responding semantic representation which is passed

to the dialog manager that, using also the discourse

context, produces the next system action, usually in

the form of a semantic output. A language genera-

tion module produces the corresponding textual form,

subsequently passed to a TTS module to produce syn-

thetic speech. It is possible to create an automated

chat by adding text input alongside with the ASR

module and a text renderer alongside TTS or to con-

nect a software agent (e.g. a talking head). Exam-

ples of conversational spoken language interfaces in-

clude (Bohus and Rudnicky, 2009): Jupiter, AdApt,

and TRIPS.

3.2 Natural Language Queries

A natural language interface requires algorithms to

query information using natural language. Also, in

dealing with large amounts of information as in e-

gov, a central problem is the formulation of queries

that are communicable to the system. Database query

languages can be intimidating to the non-expert, lead-

ing to the popularity for keyword based search in spite

of its signiﬁcant limitations (Hendrix et al., 1978).

Two major obstacles lie in the way of support-

ing arbitrary natural language queries: automatically

understanding natural language is itself still an open

research problem; to translate the understood natural

language query into a formal query requires mapping

the understanding of intent into a speciﬁc database

schema. Regarding the ﬁrst problem, state-of-the-art

techniques can reach acceptable results but it is still

far from being totally resolved. The second prob-

lem has been tackled with good results by works like

NaLIX (Li et al., 2005), Panto (Wang et al., 2007),

and ESTER (Bast et al., 2007).

3.3 Unstructured Information

As described above, HLT allows users to perform

queries in an intuitive way and also allows to feed the

system knowledge base with information from more

sources. The knowledge base is accessed by the dia-

log manager or the query system.

Information extraction (IE) takes texts as input

and produces ﬁxed-format, unambiguous data, as out-

put. It involves processing text to identify relevant in-

formation, such as named entities (NE) or relations

between them (Appelt, 1999). NE include people,

organizations, locations and so on, while relations

include physical relations (located, near, partwhole,

etc.), personal or social relations (business, fam-

ily, etc.), and membership (employ-staff, member-of-

group, etc.) (Bontcheva et al., 2008). State-of-the-art

HUMAN LANGUAGE TECHNOLOGIES FOR E-GOV

401

systems perform NE recognition over the web con-

tents with good results (Whitelaw et al., 2008).

To ﬁnd relationships between named entities is a

task related to syntactic parsing, which is the pro-

cess of analyzing a text to determine its grammatical

structure with respect to a given formal grammar. Re-

cent works achievedgood results in any context by in-

cluding machine learning algorithms to ﬁnd relation-

ships (Suchanek et al., 2007).

4 HLT POTENTIAL IN E-GOV

eGov services should be provided in a way that meets

customers natural communication paradigm. There-

fore, whenever possible, e-gov services should use

natural language interfaces: accepting written and

speech inputs (over the web and telephone) and allow-

ing people to experience a level of interaction compa-

rable to traditional, face-to-face services.

In brief, the great potential of speech and natural

language technologies for e-gov can be supported by

the following advantages:

More intuitive user interface - proﬁting from the re-

cent advances of natural language interfaces in other

areas, we argue that it is possible to develop e-gov

support systems that are able to solve user queries for-

mulated in natural language, instead of forcing users

to look for instructions or to ﬁnd information in the

set of rules that regulate the service. The linguistic

coverage of natural language interfaces is not always

obvious but some strategies allow dialog systems to

intuitively drive the user to use the right vocabulary

set (Gorin et al., 1997).

Dialog becomes an option - dialog processing is, by

its very nature, incremental. An incremental system

can work with units smaller than utterances, allow-

ing the creation of a more reactive system capable of

taking the initiative in the conversation to clarify, ask

for missing information, or suggest. By having a con-

versation, individuals would be able to explain their

problem to discover a solution, instead of having to

make a thorough search to ﬁnd how to proceed.

Take advantage of voice channels - speech is the

most natural and easy existing interface, not only for

people with special needs, but for people in general,

as Nass & Brave (Nass and Brave, 2005) state: “Ubiq-

uitous computing - access to all information for any-

one, anywhere, at any time - relies on speech for those

whose eyes or hands are directed to other tasks or for

those who cannot read or type (such as children, the

blind, or the disabled)”. Another advantage is that

telephonesare more popular than computers and more

people feel comfortable with them. Services provided

by voice are already available to the general public

(e.g. Google Voice).

Multilingual - machine translation is not adequate for

online documents because its performance is not per-

fect. The same is not true when using natural lan-

guage interfaces with dialog support because the use

of clariﬁcation can resolve translation errors and the

communication can be effective and more efﬁcient

than having the user speaking a foreign language.

Access to unstructured information - important

sources of information relevant for e-gov are origi-

nally created in natural language documents, such as

forms, laws, regulations, etc. Those documents are

usually available to the public in pdf and html for-

mats. A computer system that does not feature NLP

capacities cannot derive meaningful information from

it, being limited to store and display. Systems able to

understand the information of this type of documents

could assist users more efﬁciently by selecting the in-

formation that is relevant in the client context.

Access for all - the above advantages contribute to

reduce the digital divide. Written and spoken natural

language interfaces facilitate the access of minorities

as the visual impaired, people with speech disabilities,

and people not familiar with ICT. The usage of ubiq-

uitous telephone increases the potential to reach more

users, including those who live in remote areas, are

homebound, etc. Finally, multilingual support and the

ability to search and contextualize unstructured docu-

ments can serve other minorities, such as immigrants,

tourists and people with low literacy levels in gen-

eral. Written interfaces are robust and speech tech-

nology is becoming mature as demonstrated by sev-

eral commercial products in different languages (TTS

systems: Microsoft Reader, Nuance RealSpeak, Lo-

quendoTTS; ASR systems: Nuance Dragon Naturally

Speaking, Microsoft SpeechFX).

5 RELATED WORK

Although a lot of research activity has been done

in e-gov, so far, to our knowledge, just one project

was dedicated to the study of using HLT in e-gov:

HOPS, funded by the European Commission under

Framework-Programme 6 (FP6) (Gatius et al., 2006).

Other e-gov projects use semantic technologies to

specify, develop, deploy services, and are rather cen-

tered in solving problems as interoperability and ser-

vice integration, which are very important problems

and still need to be further addressed. See for example

WEBIST 2010 - 6th International Conference on Web Information Systems and Technologies

402

OneStopGov (Chatzidimitriou and Koumpis, 2008),

also FP6 funded, and Access-eGov (Sroga, 2008).

The authors are particularly interested in applying

the expressed position in the scenario of municipal

service delivery because it is often the closest point of

service for citizens and enterprises. The implementa-

tion will integrate a web and speech interfaces, a ques-

tion and answering (Q&A) module, a dialog manager,

a knowledge manager, and an IE module. For TTS

and ASR off-the-shelf software will be used.

The research has started by addressing the IE and

the knowledge management problems by extending

a semantic knowledge base ideas and public code

(YAGO) to cope with Portuguese texts (Suchanek

et al., 2007). Preliminary tests with a ﬁrst prototype

for the Q&A module, working with the collected data,

are scheduled for near future. The ﬁrst version of

the dialog system will be developed using the CMU

Olympus open-source framework with the Ravenclaw

dialog manager (Bohus and Rudnicky, 2009).

6 CONCLUSIONS

The authors strongly believein the potential of HLT to

improve e-gov services and are working on the appli-

cation of these ideas/technologies to new e-gov ser-

vices for Portuguese municipalities. Other projects,

such as HOPS, show that this is a challenging prob-

lem, which has been seldom addressed and requires

more research. Although good results have been

achieved in some HLT tasks, the integration of these

technologies in complex systems as e-gov services

needs to be further studied in order to have systems

ready to serve any client.

Using HLT in e-gov is still a challenge because it

requires research and adaptation to the speciﬁc area.

The beneﬁts are clear: reduction of the digital di-

vide; more intuitive human interfaces; communica-

tion comparable to traditional face-to-face dialogs;

more channels available; and more knowledgeable

systems. These beneﬁts are particularly relevant in

e-gov because government must serve 100% of the

population and because e-gov success also depends

on how easy it is to use its services.

There is still a lot of work to be done to have liable

applications in Portuguese language - our goal. We

believe that our research can be useful to other lan-

guages in the same way we found fruitful to study, un-

derstand, and adapt to Portuguese the developments

of HLT for other languages, such as English.

REFERENCES

Allen, J. F. (2003). Natural Language Processing. In Ency-

clopedia of Computer Science, pages 1218–1222.

Appelt, D. E. (1999). Introduction to Information Extrac-

tion. AI Communications, 12(3):161–172.

Bast, H., Chitea, A., Suchanek, F., and Weber, I. (2007).

ESTER: Efﬁcient Search on Text, Entities, and Rela-

tions. In Proc. 30th ACM SIGIR, pages 679–686.

Bohus, D. and Rudnicky, A. I. (2009). The ravenclaw di-

alog management framework: Architecture and sys-

tems. Computer Speech and Language, 23.

Bontcheva, K., Davis, B., Funk, A., Li, Y., and Wang, T.

(2008). Human Language Technologies. Semantic

Knowledge Management.

Chatzidimitriou, M. and Koumpis, A. (2008). Marketing

One-stop e-Government Solutions: the European On-

eStopGov Project. IAENG.

Dias, G. P. and Rafael, J. A. (2007). A simple model

and a distributed architecture for realizing one-stop e-

government. ECRA, 6(1):81 – 90.

Gatius, M., Mangham, A., and Lesmo, L. (2006). HOPS:

Developing Transformational Government Services.

Gorin, A. L., Riccardi, G., and Wright, J. H. (1997). How

May I Help You? Speech Communication.

Hendrix, G., Sacerdoti, E., Sagalowicz, D., and Slocum,

J. (1978). Developing a natural language interface to

complex data. ACM TODS, 3(2):105–147.

Li, Y., Yang, H., and Jagadish, H. V. (2005). NaLIX:

an interactive natural language interface for querying

XML. In Proc. ACM SIGMOD, page 902.

Nass, C. and Brave, S. (2005). Wired for Speech: How

Voice Activates and Advances the Human-Computer

Relationship. MIT Press.

Oteniya, O., Janowski, T., and Ojo, A. (2006). Government-

Wide Workﬂow Infrastructure-Enabling Virtual Gov-

ernment Organizations. Springer.

Relyea, H. (2002). E-gov: Introduction and overview. Gov-

ernment Information Quarterly, 19(1):9–36.

Sroga, M. (2008). Access-eGov-Personal Assistant of Pub-

lic Services. In IMCSIT, pages 421–427.

Suchanek, F. M., Kasneci, G., and Weikum, G. (2007).

Yago: a core of semantic knowledge. In WWW ’07.

Trochidis, I., Tambouris, E., and Tarabanis, K. (2008). One-

Stop Government: Literature Review. Prague. 6th

Eastern European eGov Days April 2008.

Wang, C., Xiong, M., Zhou, Q., and Yu, Y. (2007). Panto:

A portable natural language interface to ontologies.

LNCS, 4519:473.

Whitelaw, C., Kehlenbeck, A., Petrovic, N., and Ungar, L.

(2008). Web-scale named entity recognition. In CIKM

’08, pages 123–132, New York, NY, USA. ACM.

Wimmer, M., Traunmüller, R., and Lenk, K. (2001). Elec-

tronic Business Invading the Public Sector: Consider-

ations on Change and Design. HICSS, 5:5006.

World Bank (2009). http://www.worldbank.org/egov.

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