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1.
【目的/意义】跨语言信息检索研究的目的即在消除因语言的差异而导致信息查询的困难,提高从大量纷繁
复杂的查找特定信息的效率。同时提供一种更加方便的途径使得用户能够使用自己熟悉的语言检索另外一种语
言文档。【方法/过程】本文通过对国内外跨语言信息检索的研究现状分析,介绍了目前几种查询翻译的方法,包括：
直接查询翻译、文献翻译、中间语言翻译以及查询—文献翻译方法,对其效果进行比较,然后阐述了跨语言检索关
键技术,对使用基于双语词典、语料库、机器翻译技术等产生的歧义性提出了解决方法及评价。【结果/结论】使用自
然语言处理技术、共现技术、相关反馈技术、扩展技术、双向翻译技术以及基于本体信息检索技术确保知识词典的
覆盖度和歧义性处理,通过对跨语言检索实验分析证明采用知识词典、语料库和搜索引擎组合能够提高查询效
率。【创新/局限】本文为了解决跨语言信息检索使用词典、语料库中词语缺乏的现象,提出通过搜索引擎从网页获
取信息资源来充实语料库中语句对不足的问题。文章主要针对中英文信息检索问题进行了探讨,解决方法还需要
进一步研究,如中文切词困难以及字典覆盖率低等严重影响检索的效率。 相似文献
2.
Dictionary-based query translation for cross-language information retrieval often yields various translation candidates having different meanings for a source term in the query. This paper examines methods for solving the ambiguity of translations based on only the target document collections. First, we discuss two kinds of disambiguation technique: (1) one is a method using term co-occurrence statistics in the collection, and (2) a technique based on pseudo-relevance feedback. Next, these techniques are empirically compared using the CLEF 2003 test collection for German to Italian bilingual searches, which are executed by using English language as a pivot. The experiments showed that a variation of term co-occurrence based techniques, in which the best sequence algorithm for selecting translations is used with the Cosine coefficient, is dominant, and that the PRF method shows comparable high search performance, although statistical tests did not sufficiently support these conclusions. Furthermore, we repeat the same experiments for the case of French to Italian (pivot) and English to Italian (non-pivot) searches on the same CLEF 2003 test collection in order to verity our findings. Again, similar results were observed except that the Dice coefficient outperforms slightly the Cosine coefficient in the case of disambiguation based on term co-occurrence for English to Italian searches. 相似文献
3.
《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
4.
The estimation of query model is an important task in language modeling (LM) approaches to information retrieval (IR). The ideal estimation is expected to be not only effective in terms of high mean retrieval performance over all queries, but also stable in terms of low variance of retrieval performance across different queries. In practice, however, improving effectiveness can sacrifice stability, and vice versa. In this paper, we propose to study this tradeoff from a new perspective, i.e., the bias–variance tradeoff, which is a fundamental theory in statistics. We formulate the notion of bias–variance regarding retrieval performance and estimation quality of query models. We then investigate several estimated query models, by analyzing when and why the bias–variance tradeoff will occur, and how the bias and variance can be reduced simultaneously. A series of experiments on four TREC collections have been conducted to systematically evaluate our bias–variance analysis. Our approach and results will potentially form an analysis framework and a novel evaluation strategy for query language modeling. 相似文献
5.
《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
6.
This paper presents a relevance model to rank the facts of a data warehouse that are described in a set of documents retrieved with an information retrieval (IR) query. The model is based in language modeling and relevance modeling techniques. We estimate the relevance of the facts by the probability of finding their dimensions values and the query keywords in the documents that are relevant to the query. The model is the core of the so-called contextualized warehouse, which is a new kind of decision support system that combines structured data sources and document collections. The paper evaluates the relevance model with the Wall Street Journal (WSJ) TREC test subcollection and a self-constructed fact database. 相似文献
7.
Term weighting for document ranking and retrieval has been an important research topic in information retrieval for decades. We propose a novel term weighting method based on a hypothesis that a term’s role in accumulated retrieval sessions in the past affects its general importance regardless. It utilizes availability of past retrieval results consisting of the queries that contain a particular term, retrieved documents, and their relevance judgments. A term’s evidential weight, as we propose in this paper, depends on the degree to which the mean frequency values for the relevant and non-relevant document distributions in the past are different. More precisely, it takes into account the rankings and similarity values of the relevant and non-relevant documents. Our experimental result using standard test collections shows that the proposed term weighting scheme improves conventional TF*IDF and language model based schemes. It indicates that evidential term weights bring in a new aspect of term importance and complement the collection statistics based on TF*IDF. We also show how the proposed term weighting scheme based on the notion of evidential weights are related to the well-known weighting schemes based on language modeling and probabilistic models. 相似文献
8.
In this paper, a new robust relevance model is proposed that can be applied to both pseudo and true relevance feedback in the language-modeling framework for document retrieval. There are at least three main differences between our new relevance model and other relevance models. The proposed model brings back the original query into the relevance model by treating it as a short, special document, in addition to a number of top-ranked documents returned from the first round retrieval for pseudo feedback, or a number of relevant documents for true relevance feedback. Second, instead of using a uniform prior as in the original relevance model proposed by Lavrenko and Croft, documents are assigned with different priors according to their lengths (in terms) and ranks in the first round retrieval. Third, the probability of a term in the relevance model is further adjusted by its probability in a background language model. In both pseudo and true relevance cases, we have compared the performance of our model to that of the two baselines: the original relevance model and a linear combination model. Our experimental results show that the proposed new model outperforms both of the two baselines in terms of mean average precision. 相似文献
9.
《Information processing & management》2019,56(3):1026-1045
The study of query performance prediction (QPP) in information retrieval (IR) aims to predict retrieval effectiveness. The specificity of the underlying information need of a query often determines how effectively can a search engine retrieve relevant documents at top ranks. The presence of ambiguous terms makes a query less specific to the sought information need, which in turn may degrade IR effectiveness. In this paper, we propose a novel word embedding based pre-retrieval feature which measures the ambiguity of each query term by estimating how many ‘senses’ each word is associated with. Assuming each sense roughly corresponds to a Gaussian mixture component, our proposed generative model first estimates a Gaussian mixture model (GMM) from the word vectors that are most similar to the given query terms. We then use the posterior probabilities of generating the query terms themselves from this estimated GMM in order to quantify the ambiguity of the query. Previous studies have shown that post-retrieval QPP approaches often outperform pre-retrieval ones because they use additional information from the top ranked documents. To achieve the best of both worlds, we formalize a linear combination of our proposed GMM based pre-retrieval predictor with NQC, a state-of-the-art post-retrieval QPP. Our experiments on the TREC benchmark news and web collections demonstrate that our proposed hybrid QPP approach (in linear combination with NQC) significantly outperforms a range of other existing pre-retrieval approaches in combination with NQC used as baselines. 相似文献
10.
《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
11.
Edgar Meij Dolf Trieschnigg Maarten de Rijke Wessel Kraaij 《Information processing & management》2010
Over the years, various meta-languages have been used to manually enrich documents with conceptual knowledge of some kind. Examples include keyword assignment to citations or, more recently, tags to websites. In this paper we propose generative concept models as an extension to query modeling within the language modeling framework, which leverages these conceptual annotations to improve retrieval. By means of relevance feedback the original query is translated into a conceptual representation, which is subsequently used to update the query model. 相似文献
12.
《Information processing & management》2016,52(5):873-884
OCR errors in text harm information retrieval performance. Much research has been reported on modelling and correction of Optical Character Recognition (OCR) errors. Most of the prior work employ language dependent resources or training texts in studying the nature of errors. However, not much research has been reported that focuses on improving retrieval performance from erroneous text in the absence of training data. We propose a novel approach for detecting OCR errors and improving retrieval performance from the erroneous corpus in a situation where training samples are not available to model errors. In this paper we propose a method that automatically identifies erroneous term variants in the noisy corpus, which are used for query expansion, in the absence of clean text. We employ an effective combination of contextual information and string matching techniques. Our proposed approach automatically identifies the erroneous variants of query terms and consequently leads to improvement in retrieval performance through query expansion. Our proposed approach does not use any training data or any language specific resources like thesaurus for identification of error variants. It also does not expend any knowledge about the language except that the word delimiter is blank space. We have tested our approach on erroneous Bangla (Bengali in English) and Hindi FIRE collections, and also on TREC Legal IIT CDIP and TREC 5 Confusion track English corpora. Our proposed approach has achieved statistically significant improvements over the state-of-the-art baselines on most of the datasets. 相似文献
13.
This paper describes our novel retrieval model that is based on contexts of query terms in documents (i.e., document contexts). Our model is novel because it explicitly takes into account of the document contexts instead of implicitly using the document contexts to find query expansion terms. Our model is based on simulating a user making relevance decisions, and it is a hybrid of various existing effective models and techniques. It estimates the relevance decision preference of a document context as the log-odds and uses smoothing techniques as found in language models to solve the problem of zero probabilities. It combines these estimated preferences of document contexts using different types of aggregation operators that comply with different relevance decision principles (e.g., aggregate relevance principle). Our model is evaluated using retrospective experiments (i.e., with full relevance information), because such experiments can (a) reveal the potential of our model, (b) isolate the problems of the model from those of the parameter estimation, (c) provide information about the major factors affecting the retrieval effectiveness of the model, and (d) show that whether the model obeys the probability ranking principle. Our model is promising as its mean average precision is 60–80% in our experiments using different TREC ad hoc English collections and the NTCIR-5 ad hoc Chinese collection. Our experiments showed that (a) the operators that are consistent with aggregate relevance principle were effective in combining the estimated preferences, and (b) that estimating probabilities using the contexts in the relevant documents can produce better retrieval effectiveness than using the entire relevant documents. 相似文献
14.
In this paper we present a new algorithm for relevance feedback (RF) in information retrieval. Unlike conventional RF algorithms which use the top ranked documents for feedback, our proposed algorithm is a kind of active feedback algorithm which actively chooses documents for the user to judge. The objectives are (a) to increase the number of judged relevant documents and (b) to increase the diversity of judged documents during the RF process. The algorithm uses document-contexts by splitting the retrieval list into sub-lists according to the query term patterns that exist in the top ranked documents. Query term patterns include a single query term, a pair of query terms that occur in a phrase and query terms that occur in proximity. The algorithm is an iterative algorithm which takes one document for feedback in each of the iterations. We experiment with the algorithm using the TREC-6, -7, -8, -2005 and GOV2 data collections and we simulate user feedback using the TREC relevance judgements. From the experimental results, we show that our proposed split-list algorithm is better than the conventional RF algorithm and that our algorithm is more reliable than a similar algorithm using maximal marginal relevance. 相似文献
15.
As an effective technique for improving retrieval effectiveness, relevance feedback (RF) has been widely studied in both monolingual and translingual information retrieval (TLIR). The studies of RF in TLIR have been focused on query expansion (QE), in which queries are reformulated before and/or after they are translated. However, RF in TLIR actually not only can help select better query terms, but also can enhance query translation by adjusting translation probabilities and even resolving some out-of-vocabulary terms. In this paper, we propose a novel relevance feedback method called translation enhancement (TE), which uses the extracted translation relationships from relevant documents to revise the translation probabilities of query terms and to identify extra available translation alternatives so that the translated queries are more tuned to the current search. We studied TE using pseudo-relevance feedback (PRF) and interactive relevance feedback (IRF). Our results show that TE can significantly improve TLIR with both types of relevance feedback methods, and that the improvement is comparable to that of query expansion. More importantly, the effects of translation enhancement and query expansion are complementary. Their integration can produce further improvement, and makes TLIR more robust for a variety of queries. 相似文献
16.
本文介绍了用以词为基础的概念学习法来自动扩展提问式的算法，该算法通过学习出现在当前提问中的概念描述词来逐词扩展提问。实验表明，与传统的向量空间检索模型及相关反馈算法相比，本算法能大大提高查全率和查准率。该方法可用于数字图书馆和WWW等的检索中。 相似文献
17.
Knowledge acquisition and bilingual terminology extraction from multilingual corpora are challenging tasks for cross-language information retrieval. In this study, we propose a novel method for mining high quality translation knowledge from our constructed Persian–English comparable corpus, University of Tehran Persian–English Comparable Corpus (UTPECC). We extract translation knowledge based on Term Association Network (TAN) constructed from term co-occurrences in same language as well as term associations in different languages. We further propose a post-processing step to do term translation validity check by detecting the mistranslated terms as outliers. Evaluation results on two different data sets show that translating queries using UTPECC and using the proposed methods significantly outperform simple dictionary-based methods. Moreover, the experimental results show that our methods are especially effective in translating Out-Of-Vocabulary terms and also expanding query words based on their associated terms. 相似文献
18.
The relevance feedback process uses information obtained from a user about a set of initially retrieved documents to improve subsequent search formulations and retrieval performance. In extended Boolean models, the relevance feedback implies not only that new query terms must be identified and re-weighted, but also that the terms must be connected with Boolean And/Or operators properly. Salton et al. proposed a relevance feedback method, called DNF (disjunctive normal form) method, for a well established extended Boolean model. However, this method mainly focuses on generating Boolean queries but does not concern about re-weighting query terms. Also, this method has some problems in generating reformulated Boolean queries. In this study, we investigate the problems of the DNF method and propose a relevance feedback method using hierarchical clustering techniques to solve those problems. We also propose a neural network model in which the term weights used in extended Boolean queries can be adjusted by the users’ relevance feedbacks. 相似文献
19.
《Information processing & management》2016,52(3):478-489
Pseudo-relevance feedback is the basis of a category of automatic query modification techniques. Pseudo-relevance feedback methods assume the initial retrieved set of documents to be relevant. Then they use these documents to extract more relevant terms for the query or just re-weigh the user's original query. In this paper, we propose a straightforward, yet effective use of pseudo-relevance feedback method in detecting more informative query terms and re-weighting them. The query-by-query analysis of our results indicates that our method is capable of identifying the most important keywords even in short queries. Our main idea is that some of the top documents may contain a closer context to the user's information need than the others. Therefore, re-examining the similarity of those top documents and weighting this set based on their context could help in identifying and re-weighting informative query terms. Our experimental results in standard English and Persian test collections show that our method improves retrieval performance, in terms of MAP criterion, up to 7% over traditional query term re-weighting methods. 相似文献
20.
Typical pseudo-relevance feedback methods assume the top-retrieved documents are relevant and use these pseudo-relevant documents to expand terms. The initial retrieval set can, however, contain a great deal of noise. In this paper, we present a cluster-based resampling method to select novel pseudo-relevant documents based on Lavrenko’s relevance model approach. The main idea is to use overlapping clusters to find dominant documents for the initial retrieval set, and to repeatedly use these documents to emphasize the core topics of a query. 相似文献