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1.
Fernando Martínez-Santiago L. Alfonso Ureña-López Maite Martín-Valdivia 《Information Retrieval》2006,9(1):71-93
A usual strategy to implement CLIR (Cross-Language Information Retrieval) systems is the so-called query translation approach.
The user query is translated for each language present in the multilingual collection in order to compute an independent monolingual
information retrieval process per language. Thus, this approach divides documents according to language. In this way, we obtain
as many different collections as languages. After searching in these corpora and obtaining a result list per language, we
must merge them in order to provide a single list of retrieved articles.
In this paper, we propose an approach to obtain a single list of relevant documents for CLIR systems driven by query translation.
This approach, which we call 2-step RSV (RSV: Retrieval Status Value), is based on the re-indexing of the retrieval documents
according to the query vocabulary, and it performs noticeably better than traditional methods.
The proposed method requires query vocabulary alignment: given a word for a given query, we must know the translation or translations
to the other languages. Because this is not always possible, we have researched on a mixed model. This mixed model is applied
in order to deal with queries with partial word-level alignment. The results prove that even in this scenario, 2-step RSV
performs better than traditional merging methods. 相似文献
2.
Multilingual retrieval (querying of multiple document collections each in a different language) can be achieved by combining several individual techniques which enhance retrieval: machine translation to cross the language barrier, relevance feedback to add words to the initial query, decompounding for languages with complex term structure, and data fusion to combine monolingual retrieval results from different languages. Using the CLEF 2001 and CLEF 2002 topics and document collections, this paper evaluates these techniques within the context of a monolingual document ranking formula based upon logistic regression. Each individual technique yields improved performance over runs which do not utilize that technique. Moreover the techniques are complementary, in that combining the best techniques outperforms individual technique performance. An approximate but fast document translation using bilingual wordlists created from machine translation systems is presented and evaluated. The fast document translation is as effective as query translation in multilingual retrieval. Furthermore, when fast document translation is combined with query translation in multilingual retrieval, the performance is significantly better than that of query translation or fast document translation. 相似文献
3.
Multilingual information retrieval is generally understood to mean the retrieval of relevant information in multiple target
languages in response to a user query in a single source language. In a multilingual federated search environment, different
information sources contain documents in different languages. A general search strategy in multilingual federated search environments
is to translate the user query to each language of the information sources and run a monolingual search in each information
source. It is then necessary to obtain a single ranked document list by merging the individual ranked lists from the information
sources that are in different languages. This is known as the results merging problem for multilingual information retrieval.
Previous research has shown that the simple approach of normalizing source-specific document scores is not effective. On the
other side, a more effective merging method was proposed to download and translate all retrieved documents into the source
language and generate the final ranked list by running a monolingual search in the search client. The latter method is more
effective but is associated with a large amount of online communication and computation costs. This paper proposes an effective
and efficient approach for the results merging task of multilingual ranked lists. Particularly, it downloads only a small
number of documents from the individual ranked lists of each user query to calculate comparable document scores by utilizing
both the query-based translation method and the document-based translation method. Then, query-specific and source-specific
transformation models can be trained for individual ranked lists by using the information of these downloaded documents. These
transformation models are used to estimate comparable document scores for all retrieved documents and thus the documents can
be sorted into a final ranked list. This merging approach is efficient as only a subset of the retrieved documents are downloaded
and translated online. Furthermore, an extensive set of experiments on the Cross-Language Evaluation Forum (CLEF) () data has demonstrated the effectiveness of the query-specific and source-specific results merging algorithm against other
alternatives. The new research in this paper proposes different variants of the query-specific and source-specific results
merging algorithm with different transformation models. This paper also provides thorough experimental results as well as
detailed analysis. All of the work substantially extends the preliminary research in (Si and Callan, in: Peters (ed.) Results
of the cross-language evaluation forum-CLEF 2005, 2005).
相似文献
| Hao YuanEmail: |
4.
交互式跨语言信息检索是信息检索的一个重要分支。在分析交互式跨语言信息检索过程、评价指标、用户行为进展等理论研究基础上,设计一个让用户参与跨语言信息检索全过程的用户检索实验。实验结果表明:用户检索词主要来自检索主题的标题;用户判断文档相关性的准确率较高;目标语言文档全文、译文摘要、译文全文都是用户认可的判断依据;翻译优化方法以及翻译优化与查询扩展的结合方法在用户交互环境下非常有效;用户对于反馈后的翻译仍然愿意做进一步选择;用户对于与跨语言信息检索系统进行交互是有需求并认可的。用户行为分析有助于指导交互式跨语言信息检索系统的设计与实践。 相似文献
5.
We present a system for multilingual information retrieval that allows users to formulate queries in their preferred language and retrieve relevant information from a collection containing documents in multiple languages. The system is based on a process of document level alignments, where documents of different languages are paired according to their similarity. The resulting mapping allows us to produce a multilingual comparable corpus. Such a corpus has multiple interesting applications. It allows us to build a data structure for query translation in cross-language information retrieval (CLIR). Moreover, we also perform pseudo relevance feedback on the alignments to improve our retrieval results. And finally, multiple retrieval runs can be merged into one unified result list. The resulting system is inexpensive, adaptable to domain-specific collections and new languages and has performed very well at the TREC-7 conference CLIR system comparison. 相似文献
6.
文章旨在探讨查询分类技术和跨语言检索技术的关系,前者的应用能否改善后者的系统性能是核心问题。首先提出一种基于查询分类的标准化折扣累积增量评价指标,通过对采用查询分类技术前后信息检索系统的标准化折扣累积增量评价指标的变化进行判断,来检验该评价指标的可用性和有效性。同时,查询分类可以作为降低跨语言检索系统查询翻译的歧义性的技术手段。对大规模查询集随机抽样的查询翻译实验结果表明,本文提出的基于查询分类的查询翻译消歧方法对大部分查询有效,在一些情况下甚至可以直接通过本方法完成查询翻译。结合其他方法进一步消除翻译的歧义性则是下一步的工作内容。 相似文献
7.
This paper investigates the effectiveness of using MeSH® in PubMed through its automatic query expansion process: Automatic Term Mapping (ATM). We run Boolean searches based on a collection of 55 topics and about 160,000 MEDLINE® citations used in the 2006 and 2007 TREC Genomics Tracks. For each topic, we first automatically construct a query by selecting keywords from the question. Next, each query is expanded by ATM, which assigns different search tags to terms in the query. Three search tags: [MeSH Terms], [Text Words], and [All Fields] are chosen to be studied after expansion because they all make use of the MeSH field of indexed MEDLINE citations. Furthermore, we characterize the two different mechanisms by which the MeSH field is used. Retrieval results using MeSH after expansion are compared to those solely based on the words in MEDLINE title and abstracts. The aggregate retrieval performance is assessed using both F-measure and mean rank precision. Experimental results suggest that query expansion using MeSH in PubMed can generally improve retrieval performance, but the improvement may not affect end PubMed users in realistic situations. 相似文献
8.
9.
跨语言信息检索中的查询翻译方法研究 总被引：1，自引：0，他引：1
文章介绍了跨语言信息检索查询翻译的四种基本方法,并且对目前查询翻译过程中所遇到的问题及现阶段的研究进展进行了总结分析,最后总结出跨语言信息检索查询翻译未来的发展方向。 相似文献
10.
将自然语言处理技术——统计语言模型引入信息检索领域产生了一系列全新的检索模型,典型包括查询似然模型、生成相关性模型、词项依赖模型、统计翻译模型、泊松分布模型以及风险最小化框架等。本文从统计学模型以及N-gram技术的角度重点解析这些信息检索模型的演进过程。最后对基于统计语言模型的信息检索模型的发展过程以及未来发展趋势和挑战进行了总结。 相似文献
11.
Prior-art search in patent retrieval is concerned with finding all existing patents relevant to a patent application. Since patents often appear in different languages, cross-language information retrieval (CLIR) is an essential component of effective patent search. In recent years machine translation (MT) has become the dominant approach to translation in CLIR. Standard MT systems focus on generating proper translations that are morphologically and syntactically correct. Development of effective MT systems of this type requires large training resources and high computational power for training and translation. This is an important issue for patent CLIR where queries are typically very long sometimes taking the form of a full patent application, meaning that query translation using MT systems can be very slow. However, in contrast to MT, the focus for information retrieval (IR) is on the conceptual meaning of the search words regardless of their surface form, or the linguistic structure of the output. Thus much of the complexity of MT is not required for effective CLIR. We present an adapted MT technique specifically designed for CLIR. In this method IR text pre-processing in the form of stop word removal and stemming are applied to the MT training corpus prior to the training phase. Applying this step leads to a significant decrease in the MT computational and training resources requirements. Experimental application of the new approach to the cross language patent retrieval task from CLEF-IP 2010 shows that the new technique to be up to 23 times faster than standard MT for query translations, while maintaining IR effectiveness statistically indistinguishable from standard MT when large training resources are used. Furthermore the new method is significantly better than standard MT when only limited translation training resources are available, which can be a significant issue for translation in specialized domains. The new MT technique also enables patent document translation in a practical amount of time with a resulting significant improvement in the retrieval effectiveness. 相似文献
12.
Dictionary-Based Cross-Language Information Retrieval: Problems, Methods, and Research Findings 总被引：3，自引：1，他引：2
Ari Pirkola Turid Hedlund Heikki Keskustalo Kalervo Järvelin 《Information Retrieval》2001,4(3-4):209-230
This paper reviews literature on dictionary-based cross-language information retrieval (CLIR) and presents CLIR research done at the University of Tampere (UTA). The main problems associated with dictionary-based CLIR, as well as appropriate methods to deal with the problems are discussed. We will present the structured query model by Pirkola and report findings for four different language pairs concerning the effectiveness of query structuring. The architecture of our automatic query translation and construction system is presented. 相似文献
13.
面对日益膨胀的多语种信息资源,跨语言信息检索已成为实现全球知识存取和共享的关键技术手段。构建一个实用型的跨语言检索查询翻译接口,可方便地嵌入任意的信息检索平台,扩展现有信息检索平台的多语言信息处理能力。该查询翻译接口采用基于最长短语、查询分类和概率词典等多种翻译消歧策略,并从查询翻译的准确性和接口的运行效率两个角度对构建的查询翻译接口进行评测,实验结果验证所采用方法具有可行性。 相似文献
14.
信息搜寻中用户查询重构研究综述 总被引：1，自引：0，他引：1
基于信息搜寻中人机交互行为,从查询重构类型与模式、查询重构绩效、查询重构影响因素及查询式扩展技术4个方面综述国内外关于用户查询重构的研究。得出结论：基于查询重构模式研究,可获悉用户偏向使用的查询重构序列;结合查询重构影响因素和重构序列,可向不同群体用户推荐高概率的查询词,但该研究也存在一定的局限;尽管从检索系统角度,查询重构得到不少学者的广泛关注,但关于用户如何重构查询的研究在中文文献中尚未见到。 相似文献
15.
Using Statistical Term Similarity for Sense Disambiguation in Cross-Language Information Retrieval 总被引：2，自引：0，他引：2
Mirna Adriani 《Information Retrieval》2000,2(1):71-82
With the increasing availability of machine-readable bilingual dictionaries, dictionary-based automatic query translation has become a viable approach to Cross-Language Information Retrieval (CLIR). In this approach, resolving term ambiguity is a crucial step. We propose a sense disambiguation technique based on a term-similarity measure for selecting the right translation sense of a query term. In addition, we apply a query expansion technique which is also based on the term similarity measure to improve the effectiveness of the translation queries. The results of our Indonesian to English and English to Indonesian CLIR experiments demonstrate the effectiveness of the sense disambiguation technique. As for the query expansion technique, it is shown to be effective as long as the term ambiguity in the queries has been resolved. In the effort to solve the term ambiguity problem, we discovered that differences in the pattern of word-formation between the two languages render query translations from one language to the other difficult. 相似文献
16.
We present a novel approach to re-ranking a document list that was retrieved in response to a query so as to improve precision
at the very top ranks. The approach is based on utilizing a second list that was retrieved in response to the query by using,
for example, a different retrieval method and/or query representation. In contrast to commonly-used methods for fusion of retrieved lists that rely solely on retrieval scores (ranks) of documents, our approach also exploits inter-document-similarities between the lists—a potentially rich source of additional information. Empirical evaluation shows that our methods are effective
in re-ranking TREC runs; the resultant performance also favorably compares with that of a highly effective fusion method.
Furthermore, we show that our methods can potentially help to tackle a long-standing challenge, namely, integration of document-based
and cluster-based retrieved results. 相似文献
17.
18.
Despite a clear improvement of search and retrieval temporal applications, current search engines are still mostly unaware of the temporal dimension. Indeed, in most cases, systems are limited to offering the user the chance to restrict the search to a particular time period or to simply rely on an explicitly specified time span. If the user is not explicit in his/her search intents (e.g., “philip seymour hoffman”) search engines may likely fail to present an overall historic perspective of the topic. In most such cases, they are limited to retrieving the most recent results. One possible solution to this shortcoming is to understand the different time periods of the query. In this context, most state-of-the-art methodologies consider any occurrence of temporal expressions in web documents and other web data as equally relevant to an implicit time sensitive query. To approach this problem in a more adequate manner, we propose in this paper the detection of relevant temporal expressions to the query. Unlike previous metadata and query log-based approaches, we show how to achieve this goal based on information extracted from document content. However, instead of simply focusing on the detection of the most obvious date we are also interested in retrieving the set of dates that are relevant to the query. Towards this goal, we define a general similarity measure that makes use of co-occurrences of words and years based on corpus statistics and a classification methodology that is able to identify the set of top relevant dates for a given implicit time sensitive query, while filtering out the non-relevant ones. Through extensive experimental evaluation, we mean to demonstrate that our approach offers promising results in the field of temporal information retrieval (T-IR), as demonstrated by the experiments conducted over several baselines on web corpora collections. 相似文献
19.
The Effects of Conjunction, Facet Structure, and Dictionary Combinations in Concept-Based Cross-Language Retrieval 总被引：1，自引：1，他引：0
The paper studies concept-based cross-language information retrieval (CLIR). The document collection was a subset of the TREC collection. The test requests were formed from TREC's health related topics. As translation dictionaries the study used a general dictionary and a domain-specific (=medical) dictionary. The effects of translation method, conjunction, and facet order on the effectiveness of concept-based cross-language queries were studied, and concept-based structuring of cross-language queries was compared to mechanical structuring based on the output of dictionaries. The performance of translated Finnish queries against English documents was compared to the performance of original English queries against the English documents, and the performance of different CLIR query types was compared with one another. No major difference was found between concept-based and mechanical structuring. The best translation method was a simultaneous look-up in the medical dictionary and the general dictionary, in which case cross-language queries performed as well as the original English queries. The results showed that especially at high exhaustivity (the number of mutually restrictive concepts in a request) levels cross-language queries perform well in relation to monolingual queries. This suggests that conjunction disambiguates cross-language queries. An extensive study was made of the relative importance of the concepts of requests. On the basis of the classification data of request concepts it was shown how the order of facets in a query affects cross-language as well as monolingual queries. 相似文献
20.
一种改进的余弦向量度量法文本检索模型 总被引：2，自引：1，他引：1