CRL at Ntcir2

We have developed systems of two types for NTCIR2. One is an enhenced version of the system we developed for NTCIR1 and IREX. It submitted retrieval results for JJ and CC tasks. A variety of parameters were tried with the system. It used such characteristics of newspapers as locational information in the CC tasks. The system got good results for both of the tasks. The other system is a portable system which avoids free parameters as much as possible. The system submitted retrieval results for JJ, JE, EE, EJ, and CC tasks. The system automatically determined the number of top documents and the weight of the original query used in automatic-feedback retrieval. It also determined relevant terms quite robustly. For EJ and JE tasks, it used document expansion to augment the initial queries. It achieved good results, except on the CC tasks.Comment: 11 pages. Computation and Language. This paper describes our results of information retrieval in the NTCIR2 contes

Cross-language Information Retrieval

Two key assumptions shape the usual view of ranked retrieval: (1) that the searcher can choose words for their query that might appear in the documents that they wish to see, and (2) that ranking retrieved documents will suffice because the searcher will be able to recognize those which they wished to find. When the documents to be searched are in a language not known by the searcher, neither assumption is true. In such cases, Cross-Language Information Retrieval (CLIR) is needed. This chapter reviews the state of the art for CLIR and outlines some open research questions.Comment: 49 pages, 0 figure

Evaluating Information Retrieval and Access Tasks

This open access book summarizes the first two decades of the NII Testbeds and Community for Information access Research (NTCIR). NTCIR is a series of evaluation forums run by a global team of researchers and hosted by the National Institute of Informatics (NII), Japan. The book is unique in that it discusses not just what was done at NTCIR, but also how it was done and the impact it has achieved. For example, in some chapters the reader sees the early seeds of what eventually grew to be the search engines that provide access to content on the World Wide Web, today’s smartphones that can tailor what they show to the needs of their owners, and the smart speakers that enrich our lives at home and on the move. We also get glimpses into how new search engines can be built for mathematical formulae, or for the digital record of a lived human life. Key to the success of the NTCIR endeavor was early recognition that information access research is an empirical discipline and that evaluation therefore lay at the core of the enterprise. Evaluation is thus at the heart of each chapter in this book. They show, for example, how the recognition that some documents are more important than others has shaped thinking about evaluation design. The thirty-three contributors to this volume speak for the many hundreds of researchers from dozens of countries around the world who together shaped NTCIR as organizers and participants. This book is suitable for researchers, practitioners, and students—anyone who wants to learn about past and present evaluation efforts in information retrieval, information access, and natural language processing, as well as those who want to participate in an evaluation task or even to design and organize one

Applying Machine Translation to Two-Stage Cross-Language Information Retrieval

Cross-language information retrieval (CLIR), where queries and documents are in different languages, needs a translation of queries and/or documents, so as to standardize both of them into a common representation. For this purpose, the use of machine translation is an effective approach. However, computational cost is prohibitive in translating large-scale document collections. To resolve this problem, we propose a two-stage CLIR method. First, we translate a given query into the document language, and retrieve a limited number of foreign documents. Second, we machine translate only those documents into the user language, and re-rank them based on the translation result. We also show the effectiveness of our method by way of experiments using Japanese queries and English technical documents.Comment: 13 pages, 1 Postscript figur

ISCAS in English-Chinese CLIR at NTCIR-5

Abstract We participated in the Chinese single language information retrieval(SLIR) C-C task and EnglishChinese cross-language information retrieval(CLIR) E-C tasks in NTCIR5. Our project concentrates on the two aspects of the CLIR research: 1) We test various IR models especially language models for Chinese SLIR using the training corpus provided by the NTCIR organizer, and different smoothing methods have been studied for Chinese SLIR; 2) Our C-E CLIR task is based on the dictionary-based translation approach, and a new context-based translation algorithm using web corpus is proposed to solve the outof-vocabulary(OOV) problem in CLIR

Multi-word expression-sensitive word alignment

This paper presents a new word alignment method which incorporates knowledge about Bilingual Multi-Word Expressions (BMWEs). Our method of word alignment first extracts such BMWEs in a bidirectional way for a given corpus and then starts conventional word alignment, considering the properties of BMWEs in their grouping as well as their alignment links. We give partial annotation of alignment links as prior knowledge to the word alignment process; by replacing the maximum likelihood estimate in the M-step of the IBM Models with the Maximum A Posteriori (MAP) estimate, prior knowledge about BMWEs is embedded in the prior in this MAP estimate. In our experiments, we saw an improvement of 0.77 Bleu points absolute in JP–EN. Except for one case, our method gave better results than the method using only BMWEs grouping. Even though this paper does not directly address the issues in Cross-Lingual Information Retrieval (CLIR), it discusses an approach of direct relevance to the field. This approach could be viewed as the opposite of current trends in CLIR on semantic space that incorporate a notion of order in the bag-of-words model (e.g. co-occurences)

Language Resources Used in Multi-Lingual Question Answering Systems

Purpose – In the field of information retrieval, some multi-lingual tools are being created to help the users to overcome the language barriers. Nevertheless, these tools are not developed completely and it is necessary to investigate more for their improvement and application. One of their main problems is the choice of the linguistic resources to offer better coverage and to solve the translation problems in the context of the multi-lingual information retrieval. This paper aims to address this issue. Design/methodology/approach – This research is focused on the analysis of resources used by the multi-lingual question-answering systems, which respond to users' queries with short answers, rather than just offering a list of documents related to the search. An analysis of the main publications about the multi-lingual QA systems was carried out, with the aim of identifying the typology, the advantages and disadvantages, and the real use and trend of each of the linguistic resources and tools used in this new kind of system. Findings – Five of the resources most used in the cross-languages QA systems were identified and studied: databases, dictionaries, corpora, ontologies and thesauri. The three most popular traditional resources (automatic translators, dictionaries, and corpora) are gradually leaving a widening gap for others – such as ontologies and the free encyclopaedia Wikipedia. Originality/value – The perspective offered by the translation discipline can improve the effectiveness of QA system

Improved cross-language information retrieval via disambiguation and vocabulary discovery

Cross-lingual information retrieval (CLIR) allows people to find documents irrespective of the language used in the query or document. This thesis is concerned with the development of techniques to improve the effectiveness of Chinese-English CLIR. In Chinese-English CLIR, the accuracy of dictionary-based query translation is limited by two major factors: translation ambiguity and the presence of out-of-vocabulary (OOV) terms. We explore alternative methods for translation disambiguation, and demonstrate new techniques based on a Markov model and the use of web documents as a corpus to provide context for disambiguation. This simple disambiguation technique has proved to be extremely robust and successful. Queries that seek topical information typically contain OOV terms that may not be found in a translation dictionary, leading to inappropriate translations and consequent poor retrieval performance. Our novel OOV term translation method is based on the Chinese authorial practice of including unfamiliar English terms in both languages. It automatically extracts correct translations from the web and can be applied to both Chinese-English and English-Chinese CLIR. Our OOV translation technique does not rely on prior segmentation and is thus free from seg mentation error. It leads to a significant improvement in CLIR effectiveness and can also be used to improve Chinese segmentation accuracy. Good quality translation resources, especially bilingual dictionaries, are valuable resources for effective CLIR. We developed a system to facilitate construction of a large-scale translation lexicon of Chinese-English OOV terms using the web. Experimental results show that this method is reliable and of practical use in query translation. In addition, parallel corpora provide a rich source of translation information. We have also developed a system that uses multiple features to identify parallel texts via a k-nearest-neighbour classifier, to automatically collect high quality parallel Chinese-English corpora from the web. These two automatic web mining systems are highly reliable and easy to deploy. In this research, we provided new ways to acquire linguistic resources using multilingual content on the web. These linguistic resources not only improve the efficiency and effectiveness of Chinese-English cross-language web retrieval; but also have wider applications than CLIR