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1.
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: |
2.
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: |
3.
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
4.
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: |
5.
In retrieving medical free text, users are often interested in answers pertinent to certain scenarios that correspond to common
tasks performed in medical practice, e.g., treatment or diagnosis of a disease. A major challenge in handling such queries is that scenario terms in the query (e.g., treatment) are often too general to match specialized terms in relevant documents (e.g., chemotherapy). In this paper, we propose a knowledge-based query expansion method that exploits the UMLS knowledge source to append the
original query with additional terms that are specifically relevant to the query's scenario(s). We compared the proposed method
with traditional statistical expansion that expands terms which are statistically correlated but not necessarily scenario
specific. Our study on two standard testbeds shows that the knowledge-based method, by providing scenario-specific expansion,
yields notable improvements over the statistical method in terms of average precision-recall. On the OHSUMED testbed, for
example, the improvement is more than 5% averaging over all scenario-specific queries studied and about 10% for queries that
mention certain scenarios, such as treatment of a disease and differential diagnosis of a symptom/disease.
相似文献
| Wesley W. ChuEmail: |
6.
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: |
7.
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: |
8.
To put an end to the large copyright trade deficit, both Chinese government agencies and publishing houses have been striving
for entering the international publication market. The article analyzes the background of the going-global strategy, and sums
up the performance of both Chinese administrations and publishers.
相似文献
| Qing Fang (Corresponding author)Email: |
9.
Evaluation is a major driving force in advancing the state of the art in language technologies. In particular, methods for automatically assessing the quality of machine output is the preferred method for measuring progress, provided that these metrics have been validated against human judgments. Following recent developments in the automatic evaluation of machine translation and document summarization, we present a similar approach, implemented in a measure called POURPRE, an automatic technique for evaluating answers to complex questions based on n-gram co-occurrences between machine output and a human-generated answer key. Until now, the only way to assess the correctness of answers to such questions involves manual determination of whether an information “nugget” appears in a system's response. The lack of automatic methods for scoring system output is an impediment to progress in the field, which we address with this work. Experiments with the TREC 2003, TREC 2004, and TREC 2005 QA tracks indicate that rankings produced by our metric correlate highly with official rankings, and that POURPRE outperforms direct application of existing metrics.
相似文献
| Dina Demner-FushmanEmail: |
10.
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: |
11.
Sandeep Chaufla 《Publishing Research Quarterly》2008,24(3):187-201
A review and analysis of the rules and regulations including the tax aspects of making an investment in India is presented.
The full range from Foreign Direct Investment to different forms of doing business with specific examples from the publishing
industry is explored to help understand current policies and regulations.
相似文献
| Sandeep ChauflaEmail: Email: |
12.
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: |
13.
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: |
14.
15.
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: |
16.
Andy Weissberg 《Publishing Research Quarterly》2008,24(4):255-260
This article analyzes current industry practices toward the identification of digital book content. It highlights key technology
trends, workflow considerations and supply chain behaviors, and examines the implications of these trends and behaviors on
the production, discoverability, purchasing and consumption of digital book products.
相似文献
| Andy WeissbergEmail: |
17.
Jacob Soll 《Archival Science》2007,7(4):331-342
This article examines the archival methods developed by Colbert to train his son in state administration. Based on Colbert’s
correspondence with his son, it reveals the practices Colbert thought necessary to collect and manage information in his state
encyclopedic archive during the last half of the 17th century.
相似文献
| Jacob SollEmail: |
18.
Jun Wang Stephen Robertson Arjen P. de Vries Marcel J. T. Reinders 《Information Retrieval》2008,11(6):477-497
Collaborative filtering is concerned with making recommendations about items to users. Most formulations of the problem are
specifically designed for predicting user ratings, assuming past data of explicit user ratings is available. However, in practice
we may only have implicit evidence of user preference; and furthermore, a better view of the task is of generating a top-N
list of items that the user is most likely to like. In this regard, we argue that collaborative filtering can be directly
cast as a relevance ranking problem. We begin with the classic Probability Ranking Principle of information retrieval, proposing a probabilistic
item ranking framework. In the framework, we derive two different ranking models, showing that despite their common origin,
different factorizations reflect two distinctive ways to approach item ranking. For the model estimations, we limit our discussions
to implicit user preference data, and adopt an approximation method introduced in the classic text retrieval model (i.e. the
Okapi BM25 formula) to effectively decouple frequency counts and presence/absence counts in the preference data. Furthermore,
we extend the basic formula by proposing the Bayesian inference to estimate the probability of relevance (and non-relevance),
which largely alleviates the data sparsity problem. Apart from a theoretical contribution, our experiments on real data sets
demonstrate that the proposed methods perform significantly better than other strong baselines.
相似文献
| Marcel J. T. ReindersEmail: |
19.
E. Herrera-Viedma A. G. López-Herrera S. Alonso J. M. Moreno F. J. Cabrerizo C. Porcel 《Information Retrieval》2009,12(2):179-200
This paper describes a computer-supported learning system to teach students the principles and concepts of Fuzzy Information
Retrieval Systems based on weighted queries. This tool is used to support the teacher’s activity in the degree course Information Retrieval Systems Based on Artificial Intelligence at the Faculty of Library and Information Sciences at the University of Granada. Learning of languages of weighted queries
in Fuzzy Information Retrieval Systems is complex because it is very difficult to understand the different semantics that
could be associated to the weights of queries together with their respective strategies of query evaluation. We have developed
and implemented this computer-supported education system because it allows to support the teacher’s activity in the classroom
to teach the use of weighted queries in FIRSs and it helps students to develop self-learning processes on the use of such
queries. We have evaluated the performance of its use in the learning process according to the students’ perceptions and their
results obtained in the course’s exams. We have observed that using this software tool the students learn better the management
of the weighted query languages and then their performance in the exams is improved.
相似文献
| C. PorcelEmail: |
20.
Index maintenance strategies employed by dynamic text retrieval systems based on inverted files can be divided into two categories:
merge-based and in-place update strategies. Within each category, individual update policies can be distinguished based on
whether they store their on-disk posting lists in a contiguous or in a discontiguous fashion. Contiguous inverted lists, in
general, lead to higher query performance, by minimizing the disk seek overhead at query time, while discontiguous inverted
lists lead to higher update performance, requiring less effort during index maintenance operations. In this paper, we focus
on retrieval systems with high query load, where the on-disk posting lists have to be stored in a contiguous fashion at all
times. We discuss a combination of re-merge and in-place index update, called Hybrid Immediate Merge. The method performs strictly better than the re-merge baseline policy used in our experiments, as it leads to the same query
performance, but substantially better update performance. The actual time savings achievable depend on the size of the text
collection being indexed; a larger collection results in greater savings. In our experiments, variations of Hybrid Immediate Merge were able to reduce the total index update overhead by up to 73% compared to the re-merge baseline.
相似文献
| Stefan BüttcherEmail: |