The development of the Semantic Web requires great economic and human effort. Consequently, it is very useful to create mechanisms and tools that facilitate its expansion. From the standpoint of Information Retrieval, access to the contents of the Semantic Web can be favored by the use of natural language, as it is much simpler and faster for the user to engage in his habitual form of expression

Gutiérrez-Artacho, Juncal

Olvera-Lobo, María-Dolores

English

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4747
Copyright © 2015, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Category: Language Learning
DOI: 10.4018/978-1-4666-5888-2.ch466
Overview of Translation Techniques 
in Cross-Language Question 
Answering during the Last Decade
INTRODUCTION
The development of the Semantic Web requires great 
economic and human effort. Consequently, it is very 
useful to create mechanisms and tools that facilitate 
its expansion. From the standpoint of Information 
Retrieval, access to the contents of the Semantic Web 
can be favored by the use of natural language, as it is 
much simpler and faster for the user to engage in his 
habitual form of expression.
Information Retrieval
Since the 40s, the problem of information storage and 
retrieval has attracted increasing attention (Rijsbergen, 
1979). Since then the researchers of different disciplines 
have helped to develop more efficient and sophisticated 
methods to process, manage and retrieve the informa-
tion that satisfies the users’ needs. IR is a discipline 
focused in the problems of information items’ selection 
from a storage system in order to facilitate retrieval for 
the users’ needs (Baeza-Yates & Ribeiro-Neto, 1999; 
Korfhage, 1997; Salton, 1989; Salton & McGill, 1983; 
Rijsbergen, 1979). Traditionally, IR is understood as a 
fully automatic process that responds to a user query 
by examining a collection of documents and returning 
a sorted document list that should be relevant to the 
user requirements as expressed in the query. Simply 
stated, it could be said that retrieval implies finding 
certain requested information in a storage system or 
database of information (Meadow, 1992).
An IR system is a system used to store items of in-
formation that need to be processed, searched, retrieved, 
and disseminated to users (Salton & Mc Gill, 1983). 
The IR systems are information systems that allow an 
efficient and effective identification of documents from 
a collection that best meet the information needs of the 
user, expressed in the search. In just that way, an IR 
system can be viewed as a black box system that accepts 
inputs and produces outputs (Harter & Hert, 1997). An 
optimal IR system recovers all the relevant documents 
(implying an exhaustive search, i.e. a high recall) and 
only the relevant documents (implying perfect accu-
racy, that is to say, a high precision) (Baeza-Yates & 
Ribeiro-Neto, 1999). Therefore it can be affirmed that 
the value of an IR system depends on its capacity to 
quickly and correctly identify useful information, on 
its ability to reject irrelevant or extraneous items and 
on the versatility of the methods it employs (Salton & 
McGill, 1983).
Although, in the latest years, the IR systems have 
evolved toward a greater affinity with the users, that is 
to say that they try to adapt the results to the informa-
tion needs, the traditional models implied restrictions: 
a) the assumption that users want full-text documents, 
rather than answers, and that the query will be satisfied 
with these documents; b) that the process is direct and 
unidirectional rather than interactive; and finally, c) that 
the query and document share the same language. The 
topic of QA systems arises in this context, and such 
will be commented upon in this article.
Cross-Language Information 
Retrieval: A Quick Overview
The growing popularity of Internet and the wide 
availability of web informative resources for general 
audiences are a fairly recent phenomenon. The World 
María-Dolores Olvera-Lobo
Unidad Asociada Grupo SCImago, Madrid & University of Granada, Spain
Juncal Gutiérrez-Artacho
University of Granada, Spain
Category: Language Learning
 L
Overview of Translation Techniques in Cross-Language Question Answering
4748
Wide Web, together with the growing globalization of 
companies and organizations, and the increase of the 
non-English speaking audience, entails the demand 
for tools allowing users to secure information from a 
wide range of resources. Yet the underlying linguistic 
restrictions are often overlooked by researchers and 
designers. Against this background, a key characteristic 
to be evaluated in terms of the efficiency of IR sys-
tems is its capacity to allow users to look up a corpus 
of documents in different languages, and to facilitate 
the relevant information despite limited linguistic 
competence regarding the target language. This may 
call for resorting to translations of the texts involved.
CLIR involves at least two languages in this process. 
In a multi-lingual environment such as the Web, most 
IR systems (search engines) are limited to finding docu-
ments in the language of the query; or alternatively, 
include machine translation systems, which are only 
useful once the documents are located and do not ef-
fectively cross the language barrier. Given a particular 
query, CLIR systems run on a collection of multi-lingual 
documents and retrieve relevant information regardless 
of the language used in the query (Grefenstette, 1998).
These circumstances have fuelled academic inter-
est in CLIR, and the techniques of natural language 
processing. Although Salton (1970) is considered the 
“father” of the earliest research initiatives concerning 
CLIR, the first Workshop geared specifically to CLIR 
topics was celebrated in Zurich and it was organized 
by the Association for Computing Machinery during 
the Special Interest Group on Information Retrieval, 
SIGIR-96 Conference (Grefenstette, 1998). Nowadays, 
there are four important international forums about 
the evaluation of IR systems focusing on techniques 
and proceedings related to CLIR: Text REtrieval 
Conference (TREC), the Cross-Language Evaluation 
Forum (CLEF), the NII Text Collection for IR Systems 
(NTCIR) and the Language Resources and Evaluation 
Conference (LREC) (Olvera-Lobo, 2009). The object 
of our study is CLQA systems and these systems are 
opening a new field of research that is becoming in-
creasingly important within CLIR.
BACKGROUND
Beyond Information Retrieval: 
Question Answering Systems
Question Answering systems are an evolutionary 
improvement in IR systems. QA encompasses psychol-
ogy, philosophy, linguistics, education, translation, 
documentation, computer and library science. As a 
consequence, studies of the artificial intelligence, in 
particular natural language processing, and information 
retrieval aspects of question answering benefit from 
knowledge acquired in other disciplines.
The techniques used in QA systems for the process-
ing and analysis of information largely vary and, accord-
ing to the adopted approach by the system designers, 
the variations range from the use of statistic methods 
to the application of natural language processing 
techniques – so this last option is usually employed 
most frequently. The languages that the systems work 
under, the thematic content of the documents in the 
database, the organization level (structured or not) 
of the information contained in the same or the level 
of interactivity with the user, among other aspects, 
determine the different types of QA systems.
The language criteria is very popular in a large 
number of the proposed classifications (Adiwibowo 
& Adriana, 2007; Izquierdo et al., 2007; Roger et 
al., 2007; Solorio et al., 2005) allows the distinction 
between mono-lingual and multi-lingual systems, 
although in some cases a third type could be incorpo-
rated related to the multi-lingual QA systems that use 
English as it’s pivotal language (García-Cumbreras et 
al., 2006). On the other hand, the thematic coverage of 
the documents in the database is also an aspect to bear 
in mind (Harabagiu et al., 2000; Magnini et al., 2001; 
Moldovan et al., 2003; Roger et al., 2007). So, while 
a few are open domain QA systems, that is, dealing 
with general or multidisciplinary topics, unrestricted, 
others have a specialized collection from a determined 
thematic field. As could be expected, the systems that 
return the best results are the latter since a collection 
of documents with a homogenous theme facilitates the 
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Overview of Translation Techniques in Cross-Language Question Answering during the last decade

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Overview of Translation Techniques in Cross-Language Question Answering during the last decade

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