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1.
Smoothing of document language models is critical in language modeling approaches to information retrieval. In this paper,
we present a novel way of smoothing document language models based on propagating term counts probabilistically in a graph
of documents. A key difference between our approach and previous approaches is that our smoothing algorithm can iteratively
propagate counts and achieve smoothing with remotely related documents. Evaluation results on several TREC data sets show that the proposed method significantly outperforms the
simple collection-based smoothing method. Compared with those other smoothing methods that also exploit local corpus structures,
our method is especially effective in improving precision in top-ranked documents through “filling in” missing query terms
in relevant documents, which is attractive since most users only pay attention to the top-ranked documents in search engine
applications.
相似文献
| ChengXiang ZhaiEmail: |
2.
Fernando Diaz 《Information Retrieval》2007,10(6):531-562
We adapt the cluster hypothesis for score-based information retrieval by claiming that closely related documents should have
similar scores. Given a retrieval from an arbitrary system, we describe an algorithm which directly optimizes this objective
by adjusting retrieval scores so that topically related documents receive similar scores. We refer to this process as score
regularization. Because score regularization operates on retrieval scores, regardless of their origin, we can apply the technique
to arbitrary initial retrieval rankings. Document rankings derived from regularized scores, when compared to rankings derived
from un-regularized scores, consistently and significantly result in improved performance given a variety of baseline retrieval
algorithms. We also present several proofs demonstrating that regularization generalizes methods such as pseudo-relevance
feedback, document expansion, and cluster-based retrieval. Because of these strong empirical and theoretical results, we argue
for the adoption of score regularization as general design principle or post-processing step for information retrieval systems.
相似文献
| Fernando DiazEmail: |
3.
Content-oriented XML retrieval approaches aim at a more focused retrieval strategy: Instead of retrieving whole documents, document components that are exhaustive to the information need while at the same time being as specific as possible should be retrieved. In this article, we show that the evaluation methods developed for standard retrieval must be modified in order to deal with the structure of XML documents. More precisely, the size and overlap of document components must be taken into account. For this purpose, we propose a new effectiveness metric based on the definition of a concept space defined upon the notions of exhaustiveness and specificity of a search result. We compare the results of this new metric by the results obtained with the official metric used in INEX, the evaluation initiative for content-oriented XML retrieval.
相似文献
| Gabriella KazaiEmail: |
4.
Towards enhancing retrieval effectiveness of search engines for diacritisized Arabic documents 总被引：1，自引：1，他引：0
Bassam H. Hammo 《Information Retrieval》2009,12(3):300-323
5.
Fotis Lazarinis Jesús Vilares John Tait Efthimis N. Efthimiadis 《Information Retrieval》2009,12(3):230-250
With increasingly higher numbers of non-English language web searchers the problems of efficient handling of non-English Web
documents and user queries are becoming major issues for search engines. The main aim of this review paper is to make researchers
aware of the existing problems in monolingual non-English Web retrieval by providing an overview of open issues. A significant
number of papers are reviewed and the research issues investigated in these studies are categorized in order to identify the
research questions and solutions proposed in these papers. Further research is proposed at the end of each section.
相似文献
| Efthimis N. EfthimiadisEmail: |
6.
Document length is widely recognized as an important factor for adjusting retrieval systems. Many models tend to favor the
retrieval of either short or long documents and, thus, a length-based correction needs to be applied for avoiding any length
bias. In Language Modeling for Information Retrieval, smoothing methods are applied to move probability mass from document
terms to unseen words, which is often dependant upon document length. In this article, we perform an in-depth study of this
behavior, characterized by the document length retrieval trends, of three popular smoothing methods across a number of factors,
and its impact on the length of documents retrieved and retrieval performance. First, we theoretically analyze the Jelinek–Mercer,
Dirichlet prior and two-stage smoothing strategies and, then, conduct an empirical analysis. In our analysis we show how Dirichlet
prior smoothing caters for document length more appropriately than Jelinek–Mercer smoothing which leads to its superior retrieval
performance. In a follow up analysis, we posit that length-based priors can be used to offset any bias in the length retrieval
trends stemming from the retrieval formula derived by the smoothing technique. We show that the performance of Jelinek–Mercer
smoothing can be significantly improved by using such a prior, which provides a natural and simple alternative to decouple
the query and document modeling roles of smoothing. With the analysis of retrieval behavior conducted in this article, it
is possible to understand why the Dirichlet Prior smoothing performs better than the Jelinek–Mercer, and why the performance
of the Jelinek–Mercer method is improved by including a length-based prior.
相似文献
| Leif AzzopardiEmail: |
7.
Due to the heavy use of gene synonyms in biomedical text, people have tried many query expansion techniques using synonyms
in order to improve performance in biomedical information retrieval. However, mixed results have been reported. The main challenge
is that it is not trivial to assign appropriate weights to the added gene synonyms in the expanded query; under-weighting
of synonyms would not bring much benefit, while overweighting some unreliable synonyms can hurt performance significantly.
So far, there has been no systematic evaluation of various synonym query expansion strategies for biomedical text. In this
work, we propose two different strategies to extend a standard language modeling approach for gene synonym query expansion
and conduct a systematic evaluation of these methods on all the available TREC biomedical text collections for ad hoc document
retrieval. Our experiment results show that synonym expansion can significantly improve the retrieval accuracy. However, different
query types require different synonym expansion methods, and appropriate weighting of gene names and synonym terms is critical
for improving performance.
相似文献
| Chengxiang ZhaiEmail: |
8.
Oren Kurland 《Information Retrieval》2009,12(4):437-460
To obtain high precision at top ranks by a search performed in response to a query, researchers have proposed a cluster-based
re-ranking paradigm: clustering an initial list of documents that are the most highly ranked by some initial search, and using
information induced from these (often called) query-specific clusters for re-ranking the list. However, results concerning the effectiveness of various automatic cluster-based re-ranking methods have been inconclusive. We show that using query-specific clusters for automatic re-ranking
of top-retrieved documents is effective with several methods in which clusters play different roles, among which is the smoothing of document language models. We do so by adapting previously-proposed cluster-based retrieval approaches, which are based on (static) query-independent
clusters for ranking all documents in a corpus, to the re-ranking setting wherein clusters are query-specific. The best performing
method that we develop outperforms both the initial document-based ranking and some previously proposed cluster-based re-ranking
approaches; furthermore, this algorithm consistently outperforms a state-of-the-art pseudo-feedback-based approach. In further
exploration we study the performance of cluster-based smoothing methods for re-ranking with various (soft and hard) clustering
algorithms, and demonstrate the importance of clusters in providing context from the initial list through a comparison to
using single documents to this end.
相似文献
| Oren KurlandEmail: |
9.
Andrew MacFarlane 《Information Retrieval》2009,12(2):162-178
Understanding of mathematics is needed to underpin the process of search, either explicitly with Exact Match (Boolean logic,
adjacency) or implicitly with Best match natural language search. In this paper we outline some pedagogical challenges in
teaching mathematics for information retrieval (IR) to postgraduate information science students. The aim is to take these
challenges either found by experience or in the literature, to identify both theoretical and practical ideas in order to improve
the delivery of the material and positively affect the learning of the target audience by using a tutorial style of teaching.
Results show that there is evidence to support the notion that a more pro-active style of teaching using tutorials yield benefits
both in terms of assessment results and student satisfaction.
相似文献
| Andrew MacFarlaneEmail: |
10.
Query structuring and expansion with two-stage term dependence for Japanese web retrieval 总被引：1，自引：1，他引：0
In this paper, we propose a new term dependence model for information retrieval, which is based on a theoretical framework
using Markov random fields. We assume two types of dependencies of terms given in a query: (i) long-range dependencies that
may appear for instance within a passage or a sentence in a target document, and (ii) short-range dependencies that may appear
for instance within a compound word in a target document. Based on this assumption, our two-stage term dependence model captures
both long-range and short-range term dependencies differently, when more than one compound word appear in a query. We also
investigate how query structuring with term dependence can improve the performance of query expansion using a relevance model.
The relevance model is constructed using the retrieval results of the structured query with term dependence to expand the
query. We show that our term dependence model works well, particularly when using query structuring with compound words, through
experiments using a 100-gigabyte test collection of web documents mostly written in Japanese. We also show that the performance
of the relevance model can be significantly improved by using the structured query with our term dependence model.
相似文献
| Koji EguchiEmail: |
11.
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: |
12.
Diego Reforgiato Recupero 《Information Retrieval》2007,10(6):563-579
Text document clustering provides an effective and intuitive navigation mechanism to organize a large amount of retrieval
results by grouping documents in a small number of meaningful classes. Many well-known methods of text clustering make use
of a long list of words as vector space which is often unsatisfactory for a couple of reasons: first, it keeps the dimensionality
of the data very high, and second, it ignores important relationships between terms like synonyms or antonyms. Our unsupervised
method solves both problems by using ANNIE and WordNet lexical categories and WordNet ontology in order to create a well structured
document vector space whose low dimensionality allows common clustering algorithms to perform well. For the clustering step
we have chosen the bisecting k-means and the Multipole tree, a modified version of the Antipole tree data structure for, respectively, their accuracy and
speed.
相似文献
| Diego Reforgiato RecuperoEmail: |
13.
A summary overview of the children’s and young adult publishing industry in China with a focus on the size of the market,
ten major publishing houses, copyright and trends. Special emphasis has been placed on specific transaction for the sale of
translation rights from German language publishers to China and minimal activities of German rights sold to Chinese publishers.
相似文献
| Jing BartzEmail: |
14.
Intelligent use of the many diverse forms of data available on the Internet requires new tools for managing and manipulating
heterogeneous forms of information. This paper uses WHIRL, an extension of relational databases that can manipulate textual
data using statistical similarity measures developed by the information retrieval community. We show that although WHIRL is
designed for more general similarity-based reasoning tasks, it is competitive with mature systems designed explicitly for
inductive classification. In particular, WHIRL is well suited for combining different sources of knowledge in the classification
process. We show on a diverse set of tasks that the use of appropriate sets of unlabeled background knowledge often decreases
error rates, particularly if the number of examples or the size of the strings in the training set is small. This is especially
useful when labeling text is a labor-intensive job and when there is a large amount of information available about a particular
problem on the World Wide Web.
相似文献
| Haym HirshEmail: |
15.
Modern retrieval test collections are built through a process called pooling in which only a sample of the entire document
set is judged for each topic. The idea behind pooling is to find enough relevant documents such that when unjudged documents
are assumed to be nonrelevant the resulting judgment set is sufficiently complete and unbiased. Yet a constant-size pool represents
an increasingly small percentage of the document set as document sets grow larger, and at some point the assumption of approximately
complete judgments must become invalid. This paper shows that the judgment sets produced by traditional pooling when the pools
are too small relative to the total document set size can be biased in that they favor relevant documents that contain topic
title words. This phenomenon is wholly dependent on the collection size and does not depend on the number of relevant documents
for a given topic. We show that the AQUAINT test collection constructed in the recent TREC 2005 workshop exhibits this biased
relevance set; it is likely that the test collections based on the much larger GOV2 document set also exhibit the bias. The
paper concludes with suggested modifications to traditional pooling and evaluation methodology that may allow very large reusable
test collections to be built.
相似文献
| Ellen VoorheesEmail: |
16.
Massih R. Amini Anastasios Tombros Nicolas Usunier Mounia Lalmas 《Information Retrieval》2007,10(3):233-255
Documents formatted in eXtensible Markup Language (XML) are available in collections of various document types. In this paper,
we present an approach for the summarisation of XML documents. The novelty of this approach lies in that it is based on features
not only from the content of documents, but also from their logical structure. We follow a machine learning, sentence extraction-based
summarisation technique. To find which features are more effective for producing summaries, this approach views sentence extraction
as an ordering task. We evaluated our summarisation model using the INEX and SUMMAC datasets. The results demonstrate that
the inclusion of features from the logical structure of documents increases the effectiveness of the summariser, and that
the learnable system is also effective and well-suited to the task of summarisation in the context of XML documents. Our approach
is generic, and is therefore applicable, apart from entire documents, to elements of varying granularity within the XML tree.
We view these results as a step towards the intelligent summarisation of XML documents.
相似文献
| Mounia LalmasEmail: |
17.
The complexity and diversity of government regulations make understanding and retrieval of regulations a non-trivial task. One of the issues is the existence of multiple sources of regulations and interpretive guides with differences in format, terminology and context. This paper describes a comparative analysis scheme developed to help retrieval of related provisions from different regulatory documents. Specifically, the goal is to identify the most strongly related provisions between regulations. The relatedness analysis makes use of not only traditional term match but also a combination of feature matches, and not only content comparison but also structural analysis.Regulations are first compared based on conceptual information as well as domain knowledge through feature matching. Regulations also possess specific organizational structures, such as a tree hierarchy of provisions and heavy referencing between provisions. These structures represent useful information in locating related provisions, and are therefore exploited in the comparison of regulations for completeness. System performance is evaluated by comparing a similarity ranking produced by users with the machine-predicted ranking. Ranking produced by the relatedness analysis system shows a reduction in error compared to that of Latent Semantic Indexing. Various pairs of regulations are compared and the results are analyzed along with observations based on different feature usages. An example of an e-rulemaking scenario is shown to demonstrate capabilities and limitations of the prototype relatedness analysis system.
相似文献
| Gio WiederholdEmail: |
18.
We present software that generates phrase-based concordances in real-time based on Internet searching. When a user enters
a string of words for which he wants to find concordances, the system sends this string as a query to a search engine and
obtains search results for the string. The concordances are extracted by performing statistical analysis on search results
and then fed back to the user. Unlike existing tools, this concordance consultation tool is language-independent, so concordances
can be obtained even in a language for which there are no well-established analytical methods. Our evaluation has revealed
that concordances can be obtained more effectively than by only using a search engine directly.
相似文献
| Yuichiro IshiiEmail: |
19.
Precision prediction based on ranked list coherence 总被引：1，自引：0，他引：1
We introduce a statistical measure of the coherence of a list of documents called the clarity score. Starting with a document list ranked by the query-likelihood retrieval model, we demonstrate the score's relationship to query ambiguity with respect to the collection. We also show that the clarity score is correlated with the average precision of a query and lay the groundwork for useful predictions by discussing a method of setting decision thresholds automatically. We then show that passage-based clarity scores correlate with average-precision measures of ranked lists of passages, where a passage is judged relevant if it contains correct answer text, which extends the basic method to passage-based systems. Next, we introduce variants of document-based clarity scores to improve the robustness, applicability, and predictive ability of clarity scores. In particular, we introduce the ranked list clarity score that can be computed with only a ranked list of documents, and the weighted clarity score where query terms contribute more than other terms. Finally, we show an approach to predicting queries that perform poorly on query expansion that uses techniques expanding on the ideas presented earlier.
相似文献
| W. Bruce CroftEmail: |
20.
On rank-based effectiveness measures and optimization 总被引：1，自引：0，他引：1
Many current retrieval models and scoring functions contain free parameters which need to be set—ideally, optimized. The process
of optimization normally involves some training corpus of the usual document-query-relevance judgement type, and some choice
of measure that is to be optimized. The paper proposes a way to think about the process of exploring the space of parameter
values, and how moving around in this space might be expected to affect different measures. One result, concerning local optima,
is demonstrated for a range of rank-based evaluation measures.
相似文献
| Hugo ZaragozaEmail: |