共查询到20条相似文献，搜索用时 781 毫秒
1.
This paper explores the integration of textual and visual information for cross-language image retrieval. An approach which automatically transforms textual queries into visual representations is proposed. First, we mine the relationships between text and images and employ the mined relationships to construct visual queries from textual ones. Then, the retrieval results of textual and visual queries are combined. To evaluate the proposed approach, we conduct English monolingual and Chinese–English cross-language retrieval experiments. The selection of suitable textual query terms to construct visual queries is the major issue. Experimental results show that the proposed approach improves retrieval performance, and use of nouns is appropriate to generate visual queries. 相似文献
2.
We propose an approach to the retrieval of entities that have a specific relationship with the entity given in a query. Our research goal is to investigate whether related entity finding problem can be addressed by combining a measure of relatedness of candidate answer entities to the query, and likelihood that the candidate answer entity belongs to the target entity category specified in the query. An initial list of candidate entities, extracted from top ranked documents retrieved for the query, is refined using a number of statistical and linguistic methods. The proposed method extracts the category of the target entity from the query, identifies instances of this category as seed entities, and computes similarity between candidate and seed entities. The evaluation was conducted on the Related Entity Finding task of the Entity Track of TREC 2010, as well as the QA list questions from TREC 2005 and 2006. Evaluation results demonstrate that the proposed methods are effective in finding related entities. 相似文献
3.
《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.
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(4):777-787
Latent Semantic Indexing (LSI) uses the singular value decomposition to reduce noisy dimensions and improve the performance of text retrieval systems. Preliminary results have shown modest improvements in retrieval accuracy and recall, but these have mainly explored small collections. In this paper we investigate text retrieval on a larger document collection (TREC) and focus on distribution of word norm (magnitude). Our results indicate the inadequacy of word representations in LSI space on large collections. We emphasize the query expansion interpretation of LSI and propose an LSI term normalization that achieves better performance on larger collections. 相似文献
6.
7.
《Information processing & management》2001,37(3):507-520
The results from a series of three experiments that used Text Retrieval Conference (TREC) data and TREC search topics are compared. These experiments each involved three novel user interfaces (one per experiment). User interfaces that made it easier for users to view text were found to improve recall in all three experiments. A distinction was found between a cluster of subjects (a majority of whom were search experts) who tended to read fewer documents more carefully (readers, or exclusives) and subjects who skimmed through more documents without reading them as carefully (skimmers, or inclusives). Skimmers were found to have significantly better recall overall. A major outcome from our experiments at TREC and with the TREC data, is that hypertext interfaces to information retrieval (IR) tasks tend to increase recall. Our interpretation of this pattern of results across the three experiments is that increased interaction with the text (more pages viewed) generally improves recall. Findings from one of the experiments indicated that viewing a greater diversity of text on a single screen (i.e., not just more text per se, but more articles available at once) may also improve recall. In an experiment where a traditional (type-in) query interface was contrasted with a condition where queries were marked up on the text, the improvement in recall due to viewing more text was more pronounced with search novices. Our results demonstrate that markup and hypertext interfaces to text retrieval systems can benefit recall and can also benefit novices. The challenge now will be to find modified versions of hypertext interfaces that can improve precision, as well as recall and that can work with users who prefer to use different types of search strategy or have different types of training and experience. 相似文献
8.
Jie Mei Aminul Islam Abidalrahman Moh’d Yajing Wu Evangelos Milios 《Information processing & management》2018,54(6):874-887
Modern OCR engines incorporate some form of error correction, typically based on dictionaries. However, there are still residual errors that decrease performance of natural language processing algorithms applied to OCR text. In this paper, we present a statistical learning model for post-processing OCR errors, either in a fully automatic manner or followed by minimal user interaction to further reduce error rate. Our model employs web-scale corpora and integrates a rich set of linguistic features. Through an interdependent learning pipeline, our model produces and continuously refines the error detection and suggestion of candidate corrections. Evaluated on a historical biology book with complex error patterns, our model outperforms various baseline methods in the automatic mode and shows an even greater advantage when involving minimal user interaction. Quantitative analysis of each computational step further suggests that our proposed model is well-suited for handling volatile and complex OCR error patterns, which are beyond the capabilities of error correction incorporated in OCR engines. 相似文献
9.
《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
10.
Ian A. Macleod 《Information processing & management》1977,13(3):167-175
Among the problems associated with modern information retrieval systems is the lack of any systematic approach to the design of query language interfaces. In this paper we attempt to show how a relationally organised data base is well suited to bibliographic data management, and how, given such a relational organisation it is possible to construct an interface which separates the query language from the physical representation of the data base. It is also shown how such a query language organisation may be usefully interfaced to existing retrieval systems. Finally a query language for retrieval applications is proposed. 相似文献
11.
【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
12.
《Information processing & management》2022,59(5):103026
Pseudo-relevance feedback (PRF) is a classical technique to improve search engine retrieval effectiveness, by closing the vocabulary gap between users’ query formulations and the relevant documents. While PRF is typically applied on the same target corpus as the final retrieval, in the past, external expansion techniques have sometimes been applied to obtain a high-quality pseudo-relevant feedback set using the external corpus. However, such external expansion approaches have only been studied for sparse (BoW) retrieval methods, and its effectiveness for recent dense retrieval methods remains under-investigated. Indeed, dense retrieval approaches such as ANCE and ColBERT, which conduct similarity search based on encoded contextualised query and document embeddings, are of increasing importance. Moreover, pseudo-relevance feedback mechanisms have been proposed to further enhance dense retrieval effectiveness. In particular, in this work, we examine the application of dense external expansion to improve zero-shot retrieval effectiveness, i.e. evaluation on corpora without further training. Zero-shot retrieval experiments with six datasets, including two TREC datasets and four BEIR datasets, when applying the MSMARCO passage collection as external corpus, indicate that obtaining external feedback documents using ColBERT can significantly improve NDCG@10 for the sparse retrieval (by upto 28%) and the dense retrieval (by upto 12%). In addition, using ANCE on the external corpus brings upto 30% NDCG@10 improvements for the sparse retrieval and upto 29% for the dense retrieval. 相似文献
13.
Due to natural language morphology, words can take on various morphological forms. Morphological normalisation – often used in information retrieval and text mining systems – conflates morphological variants of a word to a single representative form. In this paper, we describe an approach to lexicon-based inflectional normalisation. This approach is in between stemming and lemmatisation, and is suitable for morphological normalisation of inflectionally complex languages. To eliminate the immense effort required to compile the lexicon by hand, we focus on the problem of acquiring automatically an inflectional morphological lexicon from raw corpora. We propose a convenient and highly expressive morphology representation formalism on which the acquisition procedure is based. Our approach is applied to the morphologically complex Croatian language, but it should be equally applicable to other languages of similar morphological complexity. Experimental results show that our approach can be used to acquire a lexicon whose linguistic quality allows for rather good normalisation performance. 相似文献
14.
《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
15.
Document similarity search (i.e. query by example) aims to retrieve a ranked list of documents similar to a query document in a text corpus or on the Web. Most existing approaches to similarity search first compute the pairwise similarity score between each document and the query using a retrieval function or similarity measure (e.g. Cosine), and then rank the documents by the similarity scores. In this paper, we propose a novel retrieval approach based on manifold-ranking of document blocks (i.e. a block of coherent text about a subtopic) to re-rank a small set of documents initially retrieved by some existing retrieval function. The proposed approach can make full use of the intrinsic global manifold structure of the document blocks by propagating the ranking scores between the blocks on a weighted graph. First, the TextTiling algorithm and the VIPS algorithm are respectively employed to segment text documents and web pages into blocks. Then, each block is assigned with a ranking score by the manifold-ranking algorithm. Lastly, a document gets its final ranking score by fusing the scores of its blocks. Experimental results on the TDT data and the ODP data demonstrate that the proposed approach can significantly improve the retrieval performances over baseline approaches. Document block is validated to be a better unit than the whole document in the manifold-ranking process. 相似文献
16.
We propose a new query reformulation approach, using a set of query concepts that are introduced to precisely denote the user’s information need. Since a document collection is considered to be a domain which includes latent primitive concepts, we identify those concepts through a local pattern discovery and a global modeling using data mining techniques. For a new query, we select its most associated primitive concepts and choose the most probable interpretations as query concepts. We discuss the issue of constructing the primitive concepts from either the whole corpus or from the retrieved set of documents. Our experiments are performed on the TREC8 collection. The experimental evaluation shows that our approach is as good as current query reformulation approaches, while being particularly effective for poorly performing queries. Moreover, we find that the approach using the primitive concepts generated from the set of retrieved documents leads to the most effective performance. 相似文献
17.
基于本体的文本信息检索研究 总被引：5，自引：0，他引：5
本文对如何构建基于本体的文本信息检索系统进行了探讨．并认为，利用反映概念之间关系的领域本体指导主题标引，利用反映实体之间关系的领域本体指导实体关系标引，并以本体的形式表示文档替代物和查询表达式，可以进一步提高文本信息检索系统的性能。 相似文献
18.
《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
19.
Interdocument similarities are the fundamental information source required in cluster-based retrieval, which is an advanced retrieval approach that significantly improves performance during information retrieval (IR). An effective similarity metric is query-sensitive similarity, which was introduced by Tombros and Rijsbergen as method to more directly satisfy the cluster hypothesis that forms the basis of cluster-based retrieval. Although this method is reported to be effective, existing applications of query-specific similarity are still limited to vector space models wherein there is no connection to probabilistic approaches. We suggest a probabilistic framework that defines query-sensitive similarity based on probabilistic co-relevance, where the similarity between two documents is proportional to the probability that they are both co-relevant to a specific given query. We further simplify the proposed co-relevance-based similarity by decomposing it into two separate relevance models. We then formulate all the requisite components for the proposed similarity metric in terms of scoring functions used by language modeling methods. Experimental results obtained using standard TREC test collections consistently showed that the proposed query-sensitive similarity measure performs better than term-based similarity and existing query-sensitive similarity in the context of Voorhees’ nearest neighbor test (NNT). 相似文献
20.
In this paper, we propose a text matching method for document image retrieval without any language model. Two word images are first normalized to an appropriate size and image features are extracted using the local crowdedness method. Similarity between the two features is then measured by calculating a Hausdorff distance. We performed three experiments. The first experiment proves the effectiveness of the proposed method for text matching, and the other two experiments verify the language independence and font size independence of the proposed method. 相似文献