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Cross Language Text Retrieval (CLTR) has been defined as the retrieval of documents in a language different from that of the original query. To make this possible some kind of mechanism has to be applied in order to translate the information contained in the source sentence. Many different approaches have been carried out with the purpose of transferring the information from the source language query to the target language one. Though all these methods deal with a way of translating as much information as possible from the source query, little research has been conducted in relation to the field of Machine Translation (MT). The purpose of this research work is to determine the feasibility of using MT techniques for CLTR. Specifically, I will describe how a MT system has been adapted without much effort to translate Spanish queries of a specific domain, i.e. Finance and Economics, into English in order to retrieve documents related to that field. The results of this process will then be compared with the results obtained from the retrieval of the original English queries. Thus, I will discuss the advantages and disadvantages of using MT for CLTR.
With the advance of speech, image and video technology, human-computer interaction (HCI) will reach a new phase.In recent years, HCI has been extended to human-machine communication (HMC) and the perceptual user interface (PUI). The final goal in HMC is that the communication between humans and machines is similar to human-to-human communication. Moreover, the machine can support human-to-human communication (e.g. an interface for the disabled). For this reason, various aspects of human communication are to be considered in HMC. The HMC interface, called a multimodal interface, includes different types of input methods, such as natural language, gestures, face and handwriting characters.The nine papers in this book have been selected from the 92 high-quality papers constituting the proceedings of the 2nd International Conference on Multimodal Interface (ICMI '99), which was held in Hong Kong in 1999. The papers cover a wide spectrum of the multimodal interface.
Search for information is no longer exclusively limited within the native language of the user, but is more and more extended to other languages. This gives rise to the problem of cross-language information retrieval (CLIR), whose goal is to find relevant information written in a different language to a query. In addition to the problems of monolingual information retrieval (IR), translation is the key problem in CLIR: one should translate either the query or the documents from a language to another. However, this translation problem is not identical to full-text machine translation (MT): the goal is not to produce a human-readable translation, but a translation suitable for finding relevant documents. Specific translation methods are thus required. The goal of this book is to provide a comprehensive description of the specific problems arising in CLIR, the solutions proposed in this area, as well as the remaining problems. The book starts with a general description of the monolingual IR and CLIR problems. Different classes of approaches to translation are then presented: approaches using an MT system, dictionary-based translation and approaches based on parallel and comparable corpora. In addition, the typical retrieval effectiveness using different approaches is compared. It will be shown that translation approaches specifically designed for CLIR can rival and outperform high-quality MT systems. Finally, the book offers a look into the future that draws a strong parallel between query expansion in monolingual IR and query translation in CLIR, suggesting that many approaches developed in monolingual IR can be adapted to CLIR. The book can be used as an introduction to CLIR. Advanced readers can also find more technical details and discussions about the remaining research challenges in the future. It is suitable to new researchers who intend to carry out research on CLIR. Table of Contents: Preface / Introduction / Using Manually Constructed Translation Systems and Resources for CLIR / Translation Based on Parallel and Comparable Corpora / Other Methods to Improve CLIR / A Look into the Future: Toward a Unified View of Monolingual IR and CLIR? / References / Author Biography
Information Retrieval (IR) is concerned with the effective and efficient retrieval of information based on its semantic content. The central problem in IR is the quest to find the set of relevant documents, among a large collection containing the information sought, satisfying a user's information need usually expressed in a natural language query. Documents may be objects or items in any medium: text, image, audio, or indeed a mixture of all three. This book presents 12 revised lectures given at the Third European Summer School in Information Retrieval, ESSIR 2000, held at the Villa Monastero, Varenna, Italy, in September 2000. The first part of the book is devoted to the foundation of IR and related areas; the second part on advanced topics addresses various current issues, from usability aspects to Web searching and browsing.
Machine Translation and the Information Soup! Over the past fty years, machine translation has grown from a tantalizing dream to a respectable and stable scienti c-linguistic enterprise, with users, c- mercial systems, university research, and government participation. But until very recently, MT has been performed as a relatively distinct operation, so- what isolated from other text processing. Today, this situation is changing rapidly. The explosive growth of the Web has brought multilingual text into the reach of nearly everyone with a computer. We live in a soup of information, an increasingly multilingual bouillabaisse. And to partake of this soup, we can use MT systems together with more and more tools and language processing technologies|information retrieval engines, - tomated text summarizers, and multimodal and multilingual displays. Though some of them may still be rather experimental, and though they may not quite t together well yet, it is clear that the future will o er text manipulation systems that contain all these functions, seamlessly interconnected in various ways.
Learn how to build machine translation systems with deep learning from the ground up, from basic concepts to cutting-edge research.
This book constitutes the proceedings of the 35th European Conference on IR Research, ECIR 2013, held in Moscow, Russia, in March 2013. The 55 full papers, 38 poster papers and 10 demonstrations presented in this volume were carefully reviewed and selected from 287 submissions. The papers are organized in the following topical sections: user aspects; multimedia and cross-media IR; data mining; IR theory and formal models; IR system architectures; classification; Web; event detection; temporal IR, and microblog search. Also included are 4 tutorial and 2 workshop presentations.
Most of the papers in this volume were first presented at the Workshop on Cross-Linguistic Information Retrieval that was held August 22, 1996 dur ing the SIGIR'96 Conference. Alan Smeaton of Dublin University and Paraic Sheridan of the ETH, Zurich, were the two other members of the Scientific Committee for this workshop. SIGIR is the Association for Computing Ma chinery (ACM) Special Interest Group on Information Retrieval, and they have held conferences yearly since 1977. Three additional papers have been added: Chapter 4 Distributed Cross-Lingual Information retrieval describes the EMIR retrieval system, one of the first general cross-language systems to be implemented and evaluated; Chapter 6 Mapping Vocabularies Using Latent Semantic Indexing, which originally appeared as a technical report in the Lab oratory for Computational Linguistics at Carnegie Mellon University in 1991, is included here because it was one of the earliest, though hard-to-find, publi cations showing the application of Latent Semantic Indexing to the problem of cross-language retrieval; and Chapter 10 A Weighted Boolean Model for Cross Language Text Retrieval describes a recent approach to solving the translation term weighting problem, specific to Cross-Language Information Retrieval. Gregory Grefenstette CONTRIBUTORS Lisa Ballesteros David Hull W, Bruce Croft Gregory Grefenstette Center for Intelligent Xerox Research Centre Europe Information Retrieval Grenoble Laboratory Computer Science Department University of Massachusetts Thomas K. Landauer Department of Psychology Mark W. Davis and Institute of Cognitive Science Computing Research Lab University of Colorado, Boulder New Mexico State University Michael L. Littman Bonnie J.
The first evaluation campaign of the Cross-Language Evaluation Forum (CLEF) for European languages was held from January to September 2000. The campaign cul- nated in a two-day workshop in Lisbon, Portugal, 21 22 September, immediately following the fourth European Conference on Digital Libraries (ECDL 2000). The first day of the workshop was open to anyone interested in the area of Cross-Language Information Retrieval (CLIR) and addressed the topic of CLIR system evaluation. The goal was to identify the actual contribution of evaluation to system development and to determine what could be done in the future to stimulate progress. The second day was restricted to participants in the CLEF 2000 evaluation campaign and to their - periments. This volume constitutes the proceedings of the workshop and provides a record of the campaign. CLEF is currently an activity of the DELOS Network of Excellence for Digital - braries, funded by the EC Information Society Technologies to further research in digital library technologies. The activity is organized in collaboration with the US National Institute of Standards and Technology (NIST). The support of DELOS and NIST in the running of the evaluation campaign is gratefully acknowledged. I should also like to thank the other members of the Workshop Steering Committee for their assistance in the organization of this event.