Mining subtopics from text fragments for a web query

Web search queries are often ambiguous or faceted, and the task of identifying the major underlying senses and facets of queries has received much attention in recent years. We refer to this task as query subtopic mining. In this paper, we propose to use surrounding text of query terms in top retrieved documents to mine subtopics and rank them. We first extract text fragments containing query terms from different parts of documents. Then we group similar text fragments into clusters and generate a readable subtopic for each cluster. Based on the cluster and the language model trained from a query log, we calculate three features and combine them into a relevance score for each subtopic. Subtopics are finally ranked by balancing relevance and novelty. Our evaluation experiments with the NTCIR-9 INTENT Chinese Subtopic Mining test collection show that our method significantly outperforms a query log based method proposed by Radlinski et al. (2010) and a search result clustering based method proposed by Zeng et al. (2004) in terms of precision, I-rec, D-nDCG and D#-nDCG, the official evaluation metrics used at the NTCIR-9 INTENT task. Moreover, our generated subtopics are significantly more readable than those generated by the search result clustering method.     

Leveraging integrated information to extract query subtopics for search result diversification

Search result diversification aims to diversify search results to cover different query subtopics, i.e., pieces of relevant information. The state of the art diversification methods often explicitly model the diversity based on query subtopics, and their performance is closely related to the quality of subtopics. Most existing studies extracted query subtopics only from the unstructured data such as document collections. However, there exists a huge amount of information from structured data, which complements the information from the unstructured data. The structured data can provide valuable information about domain knowledge, but is currently under-utilized. In this article, we study how to leverage the integrated information from both structured and unstructured data to extract high quality subtopics for search result diversification. We first discuss how to extract subtopics from structured data. We then propose three methods to integrate structured and unstructured data. Specifically, the first method uses the structured data to guide the subtopic extraction from unstructured data, the second one uses the unstructured data to guide the extraction, and the last one first extracts the subtopics separately from two data sources and then combines those subtopics. Experimental results in both Enterprise and Web search domains show that the proposed methods are effective in extracting high quality subtopics from the integrated information, which can lead to better diversification performance.     

Reranking web search results for diversity

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.     

Optimizing search results for human learning goals

While past research has shown that learning outcomes can be influenced by the amount of effort students invest during the learning process, there has been little research into this question for scenarios where people use search engines to learn. In fact, learning-related tasks represent a significant fraction of the time users spend using Web search, so methods for evaluating and optimizing search engines to maximize learning are likely to have broad impact. Thus, we introduce and evaluate a retrieval algorithm designed to maximize educational utility for a vocabulary learning task, in which users learn a set of important keywords for a given topic by reading representative documents on diverse aspects of the topic. Using a crowdsourced pilot study, we compare the learning outcomes of users across four conditions corresponding to rankings that optimize for different levels of keyword density. We find that adding keyword density to the retrieval objective gave significant learning gains on some topics, with higher levels of keyword density generally corresponding to more time spent reading per word, and stronger learning gains per word read. We conclude that our approach to optimizing search ranking for educational utility leads to retrieved document sets that ultimately may result in more efficient learning of important concepts.     

Re-ranking search results using an additional retrieved list

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.     

Re-ranking search results using language models of query-specific clusters

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.

Intent-based diversification of web search results: metrics and algorithms

We study the problem of web search result diversification in the case where intent based relevance scores are available. A diversified search result will hopefully satisfy the information need of user-L.s who may have different intents. In this context, we first analyze the properties of an intent-based metric, ERR-IA, to measure relevance and diversity altogether. We argue that this is a better metric than some previously proposed intent aware metrics and show that it has a better correlation with abandonment rate. We then propose an algorithm to rerank web search results based on optimizing an objective function corresponding to this metric and evaluate it on shopping related queries.     

Search markets and search results: The case of Bing

Bing and Google customize their results to target people with different geographic locations and languages but, despite the importance of search engines for web users and webometric research, the extent and nature of these differences are unknown. This study compares the results of seventeen random queries submitted automatically to Bing for thirteen different English geographic search markets at monthly intervals. Search market choice alters a small majority of the top 10 results but less than a third of the complete sets of results. Variation in the top 10 results over a month was about the same as variation between search markets but variation over time was greater for the complete results sets. Most worryingly for users, there were almost no ubiquitous authoritative results: only one URL was always returned in the top 10 for all search markets and points in time, and Wikipedia was almost completely absent from the most common top 10 results. Most importantly for webometrics, results from at least three different search markets should be combined to give more reliable and comprehensive results, even for queries that return fewer than the maximum number of URLs.     

Use of a search summary table to improve systematic review search methods,results, and efficiency

Background:Systematic reviews are comprehensive, robust, inclusive, transparent, and reproducible when bringing together the evidence to answer a research question. Various guidelines provide recommendations on the expertise required to conduct a systematic review, where and how to search for literature, and what should be reported in the published review. However, the finer details of the search results are not typically reported to allow the search methods or search efficiency to be evaluated.Case Presentation:This case study presents a search summary table, containing the details of which databases were searched, which supplementary search methods were used, and where the included articles were found. It was developed and published alongside a recent systematic review. This simple format can be used in future systematic reviews to improve search results reporting.Conclusions:Publishing a search summary table in all systematic reviews would add to the growing evidence base about information retrieval, which would help in determining which databases to search for which type of review (in terms of either topic or scope), what supplementary search methods are most effective, what type of literature is being included, and where it is found. It would also provide evidence for future searching and search methods research.     

An effective and efficient results merging strategy for multilingual information retrieval in federated search environments

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).

面向案例的隐性知识挖掘方法研究

案例是对以往经验的知识表达,它是组织保存隐性知识的一种重要形式,从案例中挖掘隐性知识是知识管理的重要内容.案例表达是案例挖掘的首要和关键环节.本文提出了一种基于本体的案例表达模型,它能够基于本体中通用词汇和概念间多种关系对案例进行准确地描述和清晰地组织,且具有较高的扩展性和灵活性.为了提高案例中隐性知识的挖掘效率,提出了谓词路径图的概念和相关理论,以及基于谓词路径图的多维关联规则挖掘算法Ex-Apriori,该算法只需一遍扫描案例库.最后,通过构建一个小型手机维修案例库,验证了该方法的有效性.     

Automatic search from streaming data

Streaming data poses a variety of new and interesting challenges for information retrieval and text analysis. Unlike static document collections, which are typically analyzed and indexed off-line to support ad-hoc queries, streaming data often must be analyzed on the fly and acted on as the data passes through the analysis system. Speech is one example of streaming data that is a challenge to exploit, yet has significant potential to provide value in a knowledge management system. We are specifically interested in techniques that analyze streaming data and automatically find collateral information, or information that clarifies, expands, and generally enhances the value of the streaming data. We present a system that analyzes a data stream and automatically finds documents related to the current topic of discussion in the data stream. Experimental results show that the system generates result lists with an average precision at 10 hits of better than 60%. We also present a hit-list re-ranking technique based on named entity analysis and automatic text categorization that can improve the search results by 6%–12%.     

A study of the integration of passage-, document-, and cluster-based information for re-ranking search results

Cluster-based and passage-based document retrieval paradigms were shown to be effective. While the former are based on utilizing query-related corpus context manifested in clusters of similar documents, the latter address the fact that a document can be relevant even if only a very small part of it contains query-pertaining information. Hence, cluster-based approaches could be viewed as based on “expanding” the document representation, while passage-based approaches can be thought of as utilizing a “contracted” document representation. We present a study of the relative benefits of using each of these two approaches, and of the potential merits of their integration. To that end, we devise two methods that integrate whole-document-based, cluster-based and passage-based information. The methods are applied for the re-ranking task, that is, re-ordering documents in an initially retrieved list so as to improve precision at the very top ranks. Extensive empirical evaluation attests to the potential merits of integrating these information types. Specifically, the resultant performance substantially transcends that of the initial ranking; and, is often better than that of a state-of-the-art pseudo-feedback-based query expansion approach.     

Determination of continuing medical education needs of clinicians from a literature search study. Part II. Questionnaire results.

Part II of this study of the needs of clinicians for continuing medical education (CME) examines the results of a questionnaire sent of Oklahoma physicians to determine if they would request formal CME courses in the same subject areas in which they had previously requested in formation from librarians. The degree of correlation between literature search requests and responses to the questionnaire confirms that the analysis of library information requests may be one approach to determining CME needs.     

Document keyphrases as subject metadata: incorporating document key concepts in search results

Most search engines display some document metadata, such as title, snippet and URL, in conjunction with the returned hits to aid users in determining documents. However, metadata is usually fragmented pieces of information that, even when combined, does not provide an overview of a returned document. In this paper, we propose a mechanism of enriching metadata of the returned results by incorporating automatically extracted document keyphrases with each returned hit. We hypothesize that keyphrases of a document can better represent the major theme in that document. Therefore, by examining the keyphrases in each returned hit, users can better predict the content of documents and the time spent on downloading and examining the irrelevant documents will be reduced substantially.     

How users search and what they search for in the medical domain

The internet is an important source of medical knowledge for everyone, from laypeople to medical professionals. We investigate how these two extremes, in terms of user groups, have distinct needs and exhibit significantly different search behaviour. We make use of query logs in order to study various aspects of these two kinds of users. The logs from America Online, Health on the Net, Turning Research Into Practice and American Roentgen Ray Society (ARRS) GoldMiner were divided into three sets: (1) laypeople, (2) medical professionals (such as physicians or nurses) searching for health content and (3) users not seeking health advice. Several analyses are made focusing on discovering how users search and what they are most interested in. One possible outcome of our analysis is a classifier to infer user expertise, which was built. We show the results and analyse the feature set used to infer expertise. We conclude that medical experts are more persistent, interacting more with the search engine. Also, our study reveals that, conversely to what is stated in much of the literature, the main focus of users, both laypeople and professionals, is on disease rather than symptoms. The results of this article, especially through the classifier built, could be used to detect specific user groups and then adapt search results to the user group.     

感性工学视角下的用户需求挖掘研究

以用户需求为中心的产品设计和营销策略,可以帮助企业在市场竞争中获得优势。而社会经济的快速发展,使得用户对产品的要求逐渐提高。用户期望产品在具备功能性特征的同时,拥有符合感性美学的设计,从而满足自身的感性需求。感性工学作为一种将用户感性情感与产品设计要素相关联的研究框架,可以有效挖掘用户感性需求。因此,本文在感性工学的视角下,以产品评论为语料,利用word2vec模型和滑动窗口技术半自动化生成用户感性情感词典和产品特征词表,并在此基础上提出特征-感性情感模型。本文以iPhone手机的产品评论为例,验证模型的有效性。结果表明,相较于传统的情感词典,结合感性工学理论进行情感分析可以更为有效地捕获用户感性需求,为企业提供决策支持。     

Known-item searches and search tactics in library search systems: Results from four transaction log analysis studies

A better understanding of users' search interactions in library search systems is key to improving the result ranking. By focusing on known-item searches (searches for an item already known) and search tactics, vast improvement can be made. To better understand user behaviour, we conducted four transaction-log studies, comprising more than 4.2 million search sessions from two German library search systems. Results show that most sessions are rather short; users tend to issue short queries and usually do not go beyond the first search engine result page (SERP). The most frequently used search tactic was the extension of a query (‘Exhaust’). Looking at the known-item searches, it becomes clear that this query type is of great importance. Between 38%–57% of all queries are known-item queries. Titles or title parts were the most frequent elements of these queries, either alone or in combination with the author's name. Unsuccessful known-item searches were often caused by items not available in the system. Results can be applied by libraries and library system vendors to improve their systems, as well as when designing new systems. Future research, in addition to log data, should also include background information on the usage, for example, through user surveys.