共查询到20条相似文献，搜索用时 31 毫秒
1.
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
2.
Query recommendation has long been considered a key feature of search engines, which can improve users’ search experience by providing useful query suggestions for their search tasks. Most existing approaches on query recommendation aim to recommend relevant queries, i.e., alternative queries similar to a user’s initial query. However, the ultimate goal of query recommendation is to assist users to reformulate queries so that they can accomplish their search task successfully and quickly. Only considering relevance in query recommendation is apparently not directly toward this goal. In this paper, we argue that it is more important to directly recommend queries with high utility, i.e., queries that can better satisfy users’ information needs. For this purpose, we attempt to infer query utility from users’ sequential search behaviors recorded in their search sessions. Specifically, we propose a dynamic Bayesian network, referred as Query Utility Model (QUM), to capture query utility by simultaneously modeling users’ reformulation and click behaviors. We then recommend queries with high utility to help users better accomplish their search tasks. We empirically evaluated the performance of our approach on a publicly released query log by comparing with the state-of-the-art methods. The experimental results show that, by recommending high utility queries, our approach is far more effective in helping users find relevant search results and thus satisfying their information needs. 相似文献
3.
User queries to the Web tend to have more than one interpretation due to their ambiguity and other characteristics. How to diversify the ranking results to meet users’ various potential information needs has attracted considerable attention recently. This paper is aimed at mining the subtopics of a query either indirectly from the returned results of retrieval systems or directly from the query itself to diversify the search results. For the indirect subtopic mining approach, clustering the retrieval results and summarizing the content of clusters is investigated. In addition, labeling topic categories and concept tags on each returned document is explored. For the direct subtopic mining approach, several external resources, such as Wikipedia, Open Directory Project, search query logs, and the related search services of search engines, are consulted. Furthermore, we propose a diversified retrieval model to rank documents with respect to the mined subtopics for balancing relevance and diversity. Experiments are conducted on the ClueWeb09 dataset with the topics of the TREC09 and TREC10 Web Track diversity tasks. Experimental results show that the proposed subtopic-based diversification algorithm significantly outperforms the state-of-the-art models in the TREC09 and TREC10 Web Track diversity tasks. The best performance our proposed algorithm achieves is α-nDCG@5 0.307, IA-P@5 0.121, and α#-nDCG@5 0.214 on the TREC09, as well as α-nDCG@10 0.421, IA-P@10 0.201, and α#-nDCG@10 0.311 on the TREC10. The results conclude that the subtopic mining technique with the up-to-date users’ search query logs is the most effective way to generate the subtopics of a query, and the proposed subtopic-based diversification algorithm can select the documents covering various subtopics. 相似文献
4.
Jovan Pehcevski James A. Thom Anne-Marie Vercoustre Vladimir Naumovski 《Information Retrieval》2010,13(5):568-600
Entity ranking has recently emerged as a research field that aims at retrieving entities as answers to a query. Unlike entity
extraction where the goal is to tag names of entities in documents, entity ranking is primarily focused on returning a ranked
list of relevant entity names for the query. Many approaches to entity ranking have been proposed, and most of them were evaluated
on the INEX Wikipedia test collection. In this paper, we describe a system we developed for ranking Wikipedia entities in
answer to a query. The entity ranking approach implemented in our system utilises the known categories, the link structure
of Wikipedia, as well as the link co-occurrences with the entity examples (when provided) to retrieve relevant entities as
answers to the query. We also extend our entity ranking approach by utilising the knowledge of predicted classes of topic
difficulty. To predict the topic difficulty, we generate a classifier that uses features extracted from an INEX topic definition
to classify the topic into an experimentally pre-determined class. This knowledge is then utilised to dynamically set the
optimal values for the retrieval parameters of our entity ranking system. Our experiments demonstrate that the use of categories
and the link structure of Wikipedia can significantly improve entity ranking effectiveness, and that topic difficulty prediction
is a promising approach that could also be exploited to further improve the entity ranking performance. 相似文献
5.
Enterprise search is important, and the search quality has a direct impact on the productivity of an enterprise. Enterprise data contain both structured and unstructured information. Since these two types of information are complementary and the structured information such as relational databases is designed based on ER (entity-relationship) models, there is a rich body of information about entities in enterprise data. As a result, many information needs of enterprise search center around entities. For example, a user may formulate a query describing a problem that she encounters with an entity, e.g., the web browser, and want to retrieve relevant documents to solve the problem. Intuitively, information related to the entities mentioned in the query, such as related entities and their relations, would be useful to reformulate the query and improve the retrieval performance. However, most existing studies on query expansion are term-centric. In this paper, we propose a novel entity-centric query expansion framework for enterprise search. Specifically, given a query containing entities, we first utilize both unstructured and structured information to find entities that are related to the ones in the query. We then discuss how to adapt existing feedback methods to use the related entities and their relations to improve search quality. Experimental results over two real-world enterprise collections show that the proposed entity-centric query expansion strategies are more effective and robust to improve the search performance than the state-of-the-art pseudo feedback methods for long natural language-like queries with entities. Moreover, results over a TREC ad hoc retrieval collections show that the proposed methods can also work well for short keyword queries in the general search domain. 相似文献
6.
The collective feedback of the users of an Information Retrieval (IR) system has been shown to provide semantic information
that, while hard to extract using standard IR techniques, can be useful in Web mining tasks. In the last few years, several
approaches have been proposed to process the logs stored by Internet Service Providers (ISP), Intranet proxies or Web search
engines. However, the solutions proposed in the literature only partially represent the information available in the Web logs.
In this paper, we propose to use a richer data structure, which is able to preserve most of the information available in the
Web logs. This data structure consists of three groups of entities: users, documents and queries, which are connected in a
network of relations. Query refinements correspond to separate transitions between the corresponding query nodes in the graph,
while users are linked to the queries they have issued and to the documents they have selected. The classical query/document
transitions, which connect a query to the documents selected by the users’ in the returned result page, are also considered.
The resulting data structure is a complete representation of the collective search activity performed by the users of a search
engine or of an Intranet. The experimental results show that this more powerful representation can be successfully used in
several Web mining tasks like discovering semantically relevant query suggestions and Web page categorization by topic. 相似文献
7.
Social tagging systems have gained increasing popularity as a method of annotating and categorizing a wide range of different web resources. Web search that utilizes social tagging data suffers from an extreme example of the vocabulary mismatch problem encountered in traditional information retrieval (IR). This is due to the personalized, unrestricted vocabulary that users choose to describe and tag each resource. Previous research has proposed the utilization of query expansion to deal with search in this rather complicated space. However, non-personalized approaches based on relevance feedback and personalized approaches based on co-occurrence statistics only showed limited improvements. This paper proposes a novel query expansion framework based on individual user profiles mined from the annotations and resources the user has marked. The underlying theory is to regularize the smoothness of word associations over a connected graph using a regularizer function on terms extracted from top-ranked documents. The intuition behind the model is the prior assumption of term consistency: the most appropriate expansion terms for a query are likely to be associated with, and influenced by terms extracted from the documents ranked highly for the initial query. The framework also simultaneously incorporates annotations and web documents through a Tag-Topic model in a latent graph. The experimental results suggest that the proposed personalized query expansion method can produce better results than both the classical non-personalized search approach and other personalized query expansion methods. Hence, the proposed approach significantly benefits personalized web search by leveraging users’ social media data. 相似文献
8.
Search engine results are often biased towards a certain aspect of a query or towards a certain meaning for ambiguous query terms. Diversification of search results offers a way to supply the user with a better balanced result set increasing the probability that a user finds at least one document suiting her information need. In this paper, we present a reranking approach based on minimizing variance of Web search results to improve topic coverage in the top-k results. We investigate two different document representations as the basis for reranking. Smoothed language models and topic models derived by Latent Dirichlet?allocation. To evaluate our approach we selected 240 queries from Wikipedia disambiguation pages. This provides us with ambiguous queries together with a community generated balanced representation of their (sub)topics. For these queries we crawled two major commercial search engines. In addition, we present a new evaluation strategy based on Kullback-Leibler divergence and Wikipedia. We evaluate this method using the TREC sub-topic evaluation on the one hand, and manually annotated query results on the other hand. Our results show that minimizing variance in search results by reranking relevant pages significantly improves topic coverage in the top-k results with respect to Wikipedia, and gives a good overview of the overall search result. Moreover, latent topic models achieve competitive diversification with significantly less reranking. Finally, our evaluation reveals that our automatic evaluation strategy using Kullback-Leibler divergence correlates well with α-nDCG scores used in manual evaluation efforts. 相似文献
9.
Elke Achtert Christian B?hm Peer Kr?ger Peter Kunath Alexey Pryakhin Matthias Renz 《Informatik - Forschung und Entwicklung》2007,15(3):179-195
The reverse k-nearest neighbor (RkNN) problem, i.e. finding all objects in a data set the k-nearest
neighbors of which include a specified query object, has received increasing attention recently. Many
industrial and scientific applications call for solutions of the RkNN problem in arbitrary metric spaces
where the data objects are not Euclidean and only a metric distance function is given for specifying
object similarity. Usually, these applications need a solution for the generalized problem where the
value of k is not known in advance and may change from query to query. In addition, many applications
require a fast approximate answer of RkNN-queries. For these scenarios, it is important to generate
a fast answer with high recall. In this paper, we propose the first approach for efficient approximative
RkNN search in arbitrary metric spaces where the value of k is specified at query time. Our approach
uses the advantages of existing metric index structures but proposes to use an approximation of the
nearest-neighbor-distances in order to prune the search space. We show that our method scales significantly
better than existing non-approximative approaches while producing an approximation of the true query
result with a high recall. 相似文献
10.
Bing Bai Jason Weston David Grangier Ronan Collobert Kunihiko Sadamasa Yanjun Qi Olivier Chapelle Kilian Weinberger 《Information Retrieval》2010,13(3):291-314
In this article we present Supervised Semantic Indexing which defines a class of nonlinear (quadratic) models that are discriminatively
trained to directly map from the word content in a query-document or document-document pair to a ranking score. Like Latent
Semantic Indexing (LSI), our models take account of correlations between words (synonymy, polysemy). However, unlike LSI our
models are trained from a supervised signal directly on the ranking task of interest, which we argue is the reason for our
superior results. As the query and target texts are modeled separately, our approach is easily generalized to different retrieval
tasks, such as cross-language retrieval or online advertising placement. Dealing with models on all pairs of words features
is computationally challenging. We propose several improvements to our basic model for addressing this issue, including low
rank (but diagonal preserving) representations, correlated feature hashing and sparsification. We provide an empirical study
of all these methods on retrieval tasks based on Wikipedia documents as well as an Internet advertisement task. We obtain
state-of-the-art performance while providing realistically scalable methods. 相似文献
11.
Ruihua Song Dingquan Wang Jian-Yun Nie Ji-Rong Wen Yong Yu 《Information Retrieval》2016,19(6):573-593
Users often issue all kinds of queries to look for the same target due to the intrinsic ambiguity and flexibility of natural languages. Some previous work clusters queries based on co-clicks; however, the intents of queries in one cluster are not that similar but roughly related. It is desirable to conduct automatic mining of queries with equivalent intents from a large scale search logs. In this paper, we take account of similarities between query strings. There are two issues associated with such similarities: it is too costly to compare any pair of queries in large scale search logs, and two queries with a similar formulation, such as “SVN” (Apache Subversion) and support vector machine (SVM), are not necessarily similar in their intents. To address these issues, we propose using the similarities of query strings above the co-click based clustering results. Our method improves precision over the co-click based clustering method (lifting precision from 0.37 to 0.62), and outperforms a commercial search engine’s query alteration (lifting \(F_1\) measure from 0.42 to 0.56). As an application, we consider web document retrieval. We aggregate similar queries’ click-throughs with the query’s click-throughs and evaluate them on a large scale dataset. Experimental results indicate that our proposed method significantly outperforms the baseline method of using a query’s own click-throughs in all metrics. 相似文献
12.
Query suggestion, which enables the user to revise a query with a single click, has become one of the most fundamental features of Web search engines. However, it has not been clear what circumstances cause the user to turn to query suggestion. In order to investigate when and how the user uses query suggestion, we analyzed three kinds of data sets obtained from a major commercial Web search engine, comprising approximately 126 million unique queries, 876 million query suggestions and 306 million action patterns of users. Our analysis shows that query suggestions are often used (1) when the original query is a rare query, (2) when the original query is a single-term query, (3) when query suggestions are unambiguous, (4) when query suggestions are generalizations or error corrections of the original query, and (5) after the user has clicked on several URLs in the first search result page. Our results suggest that search engines should provide better assistance especially when rare or single-term queries are input, and that they should dynamically provide query suggestions according to the searcher’s current state. 相似文献
13.
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
14.
Modern information retrieval systems use several levels of caching to speedup computation by exploiting frequent, recent or costly data used in the past. Previous studies show that the use of caching techniques is crucial in search engines, as it helps reducing query response times and processing workloads on search servers. In this work we propose and evaluate a static cache that acts simultaneously as list and intersection cache, offering a more efficient way of handling cache space. We also use a query resolution strategy that takes advantage of the existence of this cache to reorder the query execution sequence. In addition, we propose effective strategies to select the term pairs that should populate the cache. We also represent the data in cache in both raw and compressed forms and evaluate the differences between them using different configurations of cache sizes. The results show that the proposed Integrated Cache outperforms the standard posting lists cache in most of the cases, taking advantage not only of the intersection cache but also the query resolution strategy. 相似文献
15.
When speaking of information retrieval, we often mean text retrieval. But there exist many other forms of information retrieval applications. A typical example is collaborative filtering that suggests interesting items to a user by taking into account other users’ preferences or tastes. Due to the uniqueness of the problem, it has been modeled and studied differently in the past, mainly drawing from the preference prediction and machine learning view point. A few attempts have yet been made to bring back collaborative filtering to information (text) retrieval modeling and subsequently new interesting collaborative filtering techniques have been thus derived. In this paper, we show that from the algorithmic view point, there is an even closer relationship between collaborative filtering and text retrieval. Specifically, major collaborative filtering algorithms, such as the memory-based, essentially calculate the dot product between the user vector (as the query vector in text retrieval) and the item rating vector (as the document vector in text retrieval). Thus, if we properly structure user preference data and employ the target user’s ratings as query input, major text retrieval algorithms and systems can be directly used without any modification. In this regard, we propose a unified formulation under a common notational framework for memory-based collaborative filtering, and a technique to use any text retrieval weighting function with collaborative filtering preference data. Besides confirming the rationale of the framework, our preliminary experimental results have also demonstrated the effectiveness of the approach in using text retrieval models and systems to perform item ranking tasks in collaborative filtering. 相似文献
16.
17.
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
18.
We propose a method for search privacy on the Internet, focusing on enhancing plausible deniability against search engine query-logs. The method approximates the target search results, without submitting the intended query and avoiding other exposing queries, by employing sets of queries representing more general concepts. We model the problem theoretically, and investigate the practical feasibility and effectiveness of the proposed solution with a set of real queries with privacy issues on a large web collection. The findings may have implications for other IR research areas, such as query expansion and fusion in meta-search. Finally, we discuss ideas for privacy, such as k-anonymity, and how these may be applied to search tasks. 相似文献
19.
It is known that users of internet search engines often enter queries with misspellings in one or more search terms. Several web search engines make suggestions for correcting misspelled words, but the methods used are proprietary and unpublished to our knowledge. Here we describe the methodology we have developed to perform spelling correction for the PubMed search engine. Our approach is based on the noisy channel model for spelling correction and makes use of statistics harvested from user logs to estimate the probabilities of different types of edits that lead to misspellings. The unique problems encountered in correcting search engine queries are discussed and our solutions are outlined. 相似文献
20.
The deployment of Web 2.0 technologies has led to rapid growth of various opinions and reviews on the web, such as reviews
on products and opinions about people. Such content can be very useful to help people find interesting entities like products,
businesses and people based on their individual preferences or tradeoffs. Most existing work on leveraging opinionated content
has focused on integrating and summarizing opinions on entities to help users better digest all the opinions. In this paper,
we propose a different way of leveraging opinionated content, by directly ranking entities based on a user’s preferences.
Our idea is to represent each entity with the text of all the reviews of that entity. Given a user’s keyword query that expresses
the desired features of an entity, we can then rank all the candidate entities based on how well opinions on these entities
match the user’s preferences. We study several methods for solving this problem, including both standard text retrieval models
and some extensions of these models. Experiment results on ranking entities based on opinions in two different domains (hotels
and cars) show that the proposed extensions are effective and lead to improvement of ranking accuracy over the standard text
retrieval models for this task. 相似文献