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1.
《Information processing & management》2005,41(3):523-547
Cross-language information retrieval (CLIR) systems allow users to find documents written in different languages from that of their query. Simple knowledge structures such as bilingual term lists have proven to be a remarkably useful basis for bridging that language gap. A broad array of dictionary-based techniques have demonstrated utility, but comparison across techniques has been difficult because evaluation results often span only a limited range of conditions. This article identifies the key issues in dictionary-based CLIR, develops unified frameworks for term selection and term translation that help to explain the relationships among existing techniques, and illustrates the effect of those techniques using four contrasting languages for systematic experiments with a uniform query translation architecture. Key results include identification of a previously unseen dependence of pre- and post-translation expansion on orthographic cognates and development of a query-specific measure for translation fanout that helps to explain the utility of structured query methods. 相似文献
2.
The paper reports on experiments carried out in transitive translation, a branch of cross-language information retrieval (CLIR). By transitive translation we mean translation of search queries into the language of the document collection through an intermediate (or pivot) language. In our experiments, queries constructed from CLEF 2000 and 2001 Swedish, Finnish and German topics were translated into English through Finnish and Swedish by an automated translation process using morphological analyzers, stopword lists, electronic dictionaries, n-gramming of untranslatable words, and structured and unstructured queries. The results of the transitive runs were compared to the results of the bilingual runs, i.e. runs translating the same queries directly into English. The transitive runs using structured target queries performed well. The differences ranged from −6.6% to +2.9% units (or −25.5% to +7.8%) between the approaches. Thus transitive translation challenges direct translation and considerably simplifies global CLIR efforts. 相似文献
3.
《Information processing & management》2016,52(2):299-318
A main challenge in Cross-Language Information Retrieval (CLIR) is to estimate a proper translation model from available translation resources, since translation quality directly affects the retrieval performance. Among different translation resources, we focus on obtaining translation models from comparable corpora, because they provide appropriate translations for both languages and domains with limited linguistic resources. In this paper, we employ a two-step approach to build an effective translation model from comparable corpora, without requiring any additional linguistic resources, for the CLIR task. In the first step, translations are extracted by deriving correlations between source–target word pairs. These correlations are used to estimate word translation probabilities in the second step. We propose a language modeling approach for the first step, where modeling based on probability distribution provides two key advantages. First, our approach can be tuned easier in comparison with heuristically adjusted previous work. Second, it provides a principled basis for integrating additional lexical and translational relations to improve the accuracy of translations from comparable corpora. As an indication, we integrate monolingual relations of word co-occurrences into the process of translation extraction, which helps to extract more reliable translations for low-frequency words in a comparable corpus. Experimental results on an English–Persian comparable corpus show that our method outperforms the previous approaches in terms of both translation quality and the performance of CLIR. Indeed, the proposed method is naturally applicable to any comparable corpus, regardless of its languages. In addition, we demonstrate the significant impact of word translation probabilities, estimated in the second step of our approach, on the performance of CLIR. 相似文献
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《Information processing & management》2005,41(3):475-487
In this paper, we compile and review several experiments measuring cross-lingual information retrieval (CLIR) performance as a function of the following resources: bilingual term lists, parallel corpora, machine translation (MT), and stemmers. Our CLIR system uses a simple probabilistic language model; the studies used TREC test corpora over Chinese, Spanish and Arabic. Our findings include:
- •One can achieve an acceptable CLIR performance using only a bilingual term list (70–80% on Chinese and Arabic corpora).
- •However, if a bilingual term list and parallel corpora are available, CLIR performance can rival monolingual performance.
- •If no parallel corpus is available, pseudo-parallel texts produced by an MT system can partially overcome the lack of parallel text.
- •While stemming is useful normally, with a very large parallel corpus for Arabic–English, stemming hurt performance in our empirical studies with Arabic, a highly inflected language.
6.
《Information processing & management》2005,41(3):457-473
Two probabilistic approaches to cross-lingual retrieval are in wide use today, those based on probabilistic models of relevance, as exemplified by INQUERY, and those based on language modeling. INQUERY, as a query net model, allows the easy incorporation of query operators, including a synonym operator, which has proven to be extremely useful in cross-language information retrieval (CLIR), in an approach often called structured query translation. In contrast, language models incorporate translation probabilities into a unified framework. We compare the two approaches on Arabic and Spanish data sets, using two kinds of bilingual dictionaries––one derived from a conventional dictionary, and one derived from a parallel corpus. We find that structured query processing gives slightly better results when queries are not expanded. On the other hand, when queries are expanded, language modeling gives better results, but only when using a probabilistic dictionary derived from a parallel corpus.We pursue two additional issues inherent in the comparison of structured query processing with language modeling. The first concerns query expansion, and the second is the role of translation probabilities. We compare conventional expansion techniques (pseudo-relevance feedback) with relevance modeling, a new IR approach which fits into the formal framework of language modeling. We find that relevance modeling and pseudo-relevance feedback achieve comparable levels of retrieval and that good translation probabilities confer a small but significant advantage. 相似文献
7.
Qing Li Yuanzhu Peter Chen Sung-Hyon Myaeng Yun Jin Bo-Yeong Kang 《Information processing & management》2009
For historical and cultural reasons, English phases, especially proper nouns and new words, frequently appear in Web pages written primarily in East Asian languages such as Chinese, Korean, and Japanese. Although such English terms and their equivalences in these East Asian languages refer to the same concept, they are often erroneously treated as independent index units in traditional Information Retrieval (IR). This paper describes the degree to which the problem arises in IR and proposes a novel technique to solve it. Our method first extracts English terms from native Web documents in an East Asian language, and then unifies the extracted terms and their equivalences in the native language as one index unit. For Cross-Language Information Retrieval (CLIR), one of the major hindrances to achieving retrieval performance at the level of Mono-Lingual Information Retrieval (MLIR) is the translation of terms in search queries which can not be found in a bilingual dictionary. The Web mining approach proposed in this paper for concept unification of terms in different languages can also be applied to solve this well-known challenge in CLIR. Experimental results based on NTCIR and KT-Set test collections show that the high translation precision of our approach greatly improves performance of both Mono-Lingual and Cross-Language Information Retrieval. 相似文献
8.
Cross-lingual semantic interoperability has drawn significant attention in recent digital library and World Wide Web research as the information in languages other than English has grown exponentially. Cross-lingual information retrieval (CLIR) across different European languages, such as English, Spanish, and French, has been widely explored; however, CLIR across European languages and Oriental languages is still in the initial stage. To cross language boundary, corpus-based approach is promising to overcome the limitation of the knowledge-based and controlled vocabulary approaches but collecting parallel corpora between European language and Oriental language is not an easy task. Length-based and text-based approaches are two major approaches to align parallel documents. In this paper, we investigate several techniques using these approaches and compare their performances in aligning English and Chinese titles of parallel documents available on the Web. 相似文献
9.
本文分析了跨语言信息检索的现状,介绍和描述跨语言信息检索的主要问题,并针对万方数据和NSTL的现有资源,构建出基于科技文献的跨语言信息检索原型系统,此原型系统通过字典查询得到检索词翻译,应用Google在线翻译进行标题、摘要翻译,并预留中国科学院自动化所提供的翻译引擎扩展.跨语言信息检索强调检索结果翻译的可读性,帮助用户理解基本大意,不刻意追求准确性,力求在应用过程中促进机器翻译技术的进步和发展,辅助科研. 相似文献
10.
《Information processing & management》2005,41(4):859-872
Technical terms and proper names constitute a major problem in dictionary-based cross-language information retrieval (CLIR). However, technical terms and proper names in different languages often share the same Latin or Greek origin, being thus spelling variants of each other. In this paper we present a novel two-step fuzzy translation technique for cross-lingual spelling variants. In the first step, transformation rules are applied to source words to render them more similar to their target language equivalents. The rules are generated automatically using translation dictionaries as source data. In the second step, the intermediate forms obtained in the first step are translated into a target language using fuzzy matching. The effectiveness of the technique was evaluated empirically using five source languages and English as a target language. The two-step technique performed better, in some cases considerably better, than fuzzy matching alone. Even using the first step as such showed promising results. 相似文献
11.
《Information processing & management》2005,41(3):507-522
Query translation is a viable method for cross-language information retrieval (CLIR), but it suffers from translation ambiguities caused by multiple translations of individual query terms. Previous research has employed various methods for disambiguation, including the method of selecting an individual target query term from multiple candidates by comparing their statistical associations with the candidate translations of other query terms. This paper proposes a new method where we examine all combinations of target query term translations corresponding to the source query terms, instead of looking at the candidates for each query term and selecting the best one at a time. The goodness value for a combination of target query terms is computed based on the association value between each pair of the terms in the combination. We tested our method using the NTCIR-3 English–Korean CLIR test collection. The results show some improvements regardless of the association measures we used. 相似文献
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Many operational IR indexes are non-normalized, i.e. no lemmatization or stemming techniques, etc. have been employed in indexing. This poses a challenge for dictionary-based cross-language retrieval (CLIR), because translations are mostly lemmas. In this study, we face the challenge of dictionary-based CLIR in a non-normalized index. We test two optional approaches: FCG (Frequent Case Generation) and s-gramming. The idea of FCG is to automatically generate the most frequent inflected forms for a given lemma. FCG has been tested in monolingual retrieval and has been shown to be a good method for inflected retrieval, especially for highly inflected languages. S-gramming is an approximate string matching technique (an extension of n-gramming). The language pairs in our tests were English–Finnish, English–Swedish, Swedish–Finnish and Finnish–Swedish. Both our approaches performed quite well, but the results varied depending on the language pair. S-gramming and FCG performed quite equally in all the other language pairs except Finnish–Swedish, where s-gramming outperformed FCG. 相似文献
13.
《Information processing & management》2016,52(4):646-657
In contrast with their monolingual counterparts, little attention has been paid to the effects that misspelled queries have on the performance of Cross-Language Information Retrieval (CLIR) systems. The present work makes a first attempt to fill this gap by extending our previous work on monolingual retrieval in order to study the impact that the progressive addition of misspellings to input queries has, this time, on the output of CLIR systems. Two approaches for dealing with this problem are analyzed in this paper. Firstly, the use of automatic spelling correction techniques for which, in turn, we consider two algorithms: the first one for the correction of isolated words and the second one for a correction based on the linguistic context of the misspelled word. The second approach to be studied is the use of character n-grams both as index terms and translation units, seeking to take advantage of their inherent robustness and language-independence. All these approaches have been tested on a from-Spanish-to-English CLIR system, that is, Spanish queries on English documents. Real, user-generated spelling errors have been used under a methodology that allows us to study the effectiveness of the different approaches to be tested and their behavior when confronted with different error rates. The results obtained show the great sensitiveness of classic word-based approaches to misspelled queries, although spelling correction techniques can mitigate such negative effects. On the other hand, the use of character n-grams provides great robustness against misspellings. 相似文献
14.
本文主要论述跨语言信息检索（CLIR）技术与数字图书馆（D-Lib）技术相结合的系统模型。首先介绍CLIR和D-Lib的概念及涉及的相关技术；然后讨论CLIR技术在D-Lib中应用的必然性和可行性；将CLIR的技术平台与D-Lib的系统结构相结合，设计基于CLIR的D-Lib系统模型；最后关于CLIR技术和D-Lib相结合的应用目前存在的问题提出自己见解。 相似文献
15.
《Information processing & management》2001,37(1):147-161
This paper analyzes the features of the Swedish language from the viewpoint of mono- and cross-language information retrieval (CLIR). The study was motivated by the fact that Swedish is known poorly from the IR perspective. This paper shows that Swedish has unique features, in particular gender features, the use of fogemorphemes in the formation of compound words, and a high frequency of homographic words. Especially in dictionary-based CLIR, correct word normalization and compound splitting are essential. It was shown in this study, however, that publicly available morphological analysis tools used for normalization and compound splitting have pitfalls that might decrease the effectiveness of IR and CLIR. A comparative study was performed to test the degree of lexical ambiguity in Swedish, Finnish and English. The results suggest that part-of-speech tagging might be useful in Swedish IR due to the high frequency of homographic words. 相似文献
16.
Experimental results of cross-language information retrieval (CLIR) do not indicate why a model fails or how a model could be improved. One basic research question is thus whether it is possible to provide conditions by which one can evaluate any existing or new CLIR strategy analytically and one can improve the design of CLIR models. Inspired by the heuristics in monolingual IR, we introduce in this paper Dilution/Concentration (D/C) conditions to characterize good CLIR models based on direct intuitions under artificial settings. The conditions, derived from first principles in CLIR, generalize the idea of query structuring approach. Empirical results with state-of-the-art CLIR models show that when a condition is not satisfied, it often indicates non-optimality of the method. In general, we find that the empirical performance of a retrieval formula is tightly related to how well it satisfies the conditions. Lastly, we propose, by following the D/C conditions, several novel CLIR models based on the information-based models, which again shows that the D/C conditions are efficient to feature good CLIR models. 相似文献
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信息检索发展中的一个重要理论问题是如何对查询与文档进行匹配,由此形成了不同的信息检索模型。跨语言信息检索是信息检索研究的一个分支,也是近年来的热点问题。本文主要对信息检索模型的研究进展,及其在跨语言信息检索中的应用进展进行分析与综述。 相似文献
19.
《Information processing & management》2005,41(3):415-431
This introductory paper covers not only the research content of the articles in this special issue of IP&M but attempts to characterize the state-of-the-art in the Cross-Language Information Retrieval (CLIR) domain. We present our view of some major directions for CLIR research in the future. In particular, we find that insufficient attention has been given to the Web as a resource for multilingual research, and to languages which are spoken by hundreds of millions of people in the world but have been mainly neglected by the CLIR research community. In addition, we find that most CLIR evaluation has focussed narrowly on the news genre to the exclusion of other important genres such as scientific and technical literature. The paper concludes by describing an ambitious 5-year research plan proposed by James Mayfield and Paul McNamee. 相似文献
20.
This paper proposes a learning approach for the merging process in multilingual information retrieval (MLIR). To conduct the learning approach, we present a number of features that may influence the MLIR merging process. These features are mainly extracted from three levels: query, document, and translation. After the feature extraction, we then use the FRank ranking algorithm to construct a merge model. To the best of our knowledge, this practice is the first attempt to use a learning-based ranking algorithm to construct a merge model for MLIR merging. In our experiments, three test collections for the task of crosslingual information retrieval (CLIR) in NTCIR3, 4, and 5 are employed to assess the performance of our proposed method. Moreover, several merging methods are also carried out for a comparison, including traditional merging methods, the 2-step merging strategy, and the merging method based on logistic regression. The experimental results show that our proposed method can significantly improve merging quality on two different types of datasets. In addition to the effectiveness, through the merge model generated by FRank, our method can further identify key factors that influence the merging process. This information might provide us more insight and understanding into MLIR merging. 相似文献