Search result diversification aims at returning diversified document lists to cover different user intents of a query. Existing diversity measures assume that the intents of a query are disjoint, and do not consider their relationships. In this paper, we introduce intent hierarchies to model the relationships between intents, and present four weighing schemes. Based on intent hierarchies, we propose several hierarchical measures that take into account the relationships between intents. We demonstrate the feasibility of hierarchical measures by using a new test collection based on TREC Web Track 2009-2013 diversity test collections and by using NTCIR-11 IMine test collection. Our main experimental findings are: (1) Hierarchical measures are more discriminative and intuitive than existing measures. In terms of intuitiveness, it is preferable for hierarchical measures to use the whole intent hierarchies than to use only the leaf nodes. (2) The types of intent hierarchies used affect the discriminative power and intuitiveness of hierarchical measures. We suggest the best type of intent hierarchies to be used according to whether the nonuniform weights are available. (3) To measure the benefits of the diversification algorithms which use automatically mined hierarchical intents, it is important to use hierarchical measures instead of existing measures.

Using classical statistical significance tests, researchers can only discuss PD+jH, the probability of observing the data D at hand or something more extreme, under the assumption that the hypothesis H is true (i.e., the p-value). But what we usually want is PHjD, the probability that a hypothesis is true, given the data. If we use Bayesian statistics with state-of-The-Art Markov Chain Monte Carlo (MCMC) methods for obtaining posterior distributions, this is no longer a problem. .at is, instead of the classical p-values and 95% confidence intervals, which are offen misinterpreted respectively as "probability that the hypothesis is (in)correct" and "probability that the true parameter value drops within the interval is 95%," we can easily obtain PHjD and credible intervals which represent exactly the above. Moreover, with Bayesian tests, we can easily handle virtually any hypothesis, not just "equality of means," and obtain an Expected A Posteriori (EAP) value of any statistic that we are interested in. We provide simple tools to encourage the IR community to take up paired and unpaired Bayesian tests for comparing two systems. Using a variety of TREC and NTCIR data, we compare PHjD with p-values, credible intervals with con.-dence intervals, and Bayesian EAP effect sizes with classical ones. Our results show that (a) p-values and confidence intervals can respectively be regarded as approximations of what we really want, namely, PHjD and credible intervals
and (b) sample effect sizes from classical significance tests can di.er considerably from the Bayesian EAP effect sizes, which suggests that the former can be poor estimates of population effect sizes. For both paired and unpaired tests, we propose that the IR community report the EAP, the credible interval, and the probability of hypothesis being true, not only for the raw di.erence in means but also for the effect size in terms of Glass's.δ.

Mobile search engine result pages (SERPs) are becoming highly visual and heterogenous. Unlike the traditional ten-blue-link SERPs for desktop search, different verticals and cards occupy different amounts of space within the small screen. Hence, traditional retrieval measures that regard the SERP as a ranked list of homogeneous items are not adequate for evaluating the overall quality of mobile SERPs. Specifically, we address the following new problems in mobile search evaluation: (1) Different retrieved items have different heights within the scrollable SERP, unlike a ten-blue-link SERP in which results have similar heights with each other. Therefore, the traditional rank-based decaying functions are not adequate for mobile search metrics. (2) For some types of verticals and cards, the information that the user seeks is already embedded in the snippet, which makes clicking on those items to access the landing page unnecessary. (3) For some results with complex sub-components (and usually a large height), the total gain of the results cannot be obtained if users only read part of their contents. The benefit brought by the result is affected by user's reading behavior and the internal gain distribution (over the height) should be modeled to get a more accurate estimation. To tackle these problems, we conduct a lab-based user study to construct suitable user behavior model for mobile search evaluation. From the results, we find that the geometric heights of user's browsing trails can be adopted as a good signal of user effort. Based on these findings, we propose a new evaluation metric, Height-Biased Gain, which is calculated by summing up the product of gain distribution and discount factors that are both modeled in terms of result height. To evaluate the effectiveness of the proposed metric, we compare the agreement of evaluation metrics with side-by-side user preferences on a test collection composed of four mobile search engines. Experimental results show that HBG agrees with user preferences 85.33% of the time, which is better than all existing metrics.

Recent advances in neural networks, along with the growth of rich and diverse community question answering (cQA) data, have en-abled researchers to construct robust open-domain question an-swering (QA) systems. It is often claimed that such state-of-The-art QA systems far outperform traditional IR baselines such as BM25. However, most such studies rely on relatively small data sets, e.g., those extracted from the old TREC QA tracks. Given mas-sive training data plus a separate corpus of Q&amp
A pairs as the tar-get knowledge source, how well would such a system really per-form? How fast would it respond? In this demonstration, we pro-vide the attendees of SIGIR 2017 an opportunity to experience a live comparison of two open-domain QA systems, one based on a long short-Term memory (LSTM) architecture with over 11 mil-lion Yahoo! Chiebukuro (i.e., Japanese Yahoo! Answers) questions and over 27.4 million answers for training, and the other based on BM25. Both systems use the same Q&amp
A knowledge source for answer retrieval. Our core demonstration system is a pair of Japan-ese monolingual QA systems, but we leverage machine translation for letting the SIGIR attendees enter English questions and com-pare the Japanese responses from the two systems after translating them into English.

Time plays an essential role in multiple areas of Information Retrieval (IR) studies such as search evaluation, user behavior analysis, temporal search result ranking and query understanding. Especially, in search evaluation studies, time is usually adopted as a measure to quantify users' efforts in search processes. Psychological studies have reported that the time perception of human beings can be affected by many stimuli, such as attention and motivation, which are closely related to many cognitive factors in search. Considering the fact that users' search experiences are affected by their subjective feelings of time, rather than the objective time measured by timing devices, it is necessary to look into the different factors that have impacts on search users' perception of time. In this work, we make a first step towards revealing the time perception mechanism of search users with the following contributions: (1) We establish an experimental research framework to measure the subjective perception of time while reading documents in search scenario, which originates from but is also different from traditional time perception measurements in psychological studies. (2) With the framework, we show that while users are reading result documents, document relevance has small yet visible effect on search users' perception of time. By further examining the impact of other factors, we demonstrate that the effect on relevant documents can also be influenced by individuals and tasks. (3) We conduct a preliminary experiment in which the difference between perceived time and dwell time is taken into consideration in a search evaluation task. We found that the revised framework achieved a better correlation with users' satisfaction feedbacks. This work may help us better understand the time perception mechanism of search users and provide insights in how to better incorporate time factor in search evaluation studies.

Webber, Moffat and Zobel proposed score standardization for information retrieval evaluation with multiple test collections. Given a topic-by-run raw score matrix in terms of some evaluation measure, each score can be standardised using the topic's sample mean and sample standard deviation across a set of past runs so as to quantify how different a system is from the "average" system in standard deviation units. Using standardised scores, researchers can compare systems across different test collections without worrying about topic hardness or normalisation. While Webber et al. mapped the standardised scores to the [0,1] range using a standard normal cumulative density function, the present study demonstrates that linear transformation of the standardised scores, a method widely used in educational research, can be a simple and effective alternative. We use three TREC robust track data sets with graded relevance assessments and official runs to compare these methods by means of leave-one-out tests, discriminative power, swap rate tests, and topic set size design. In particular, we demonstrate that our method is superior to the method of Webber et al. in terms of swap rates and topic set size design: put simply, our method ensures pairwise system comparisons that are more consistent across different data sets, and is arguably more convenient for designing a new test collection from a statistical viewpoint.

Search result diversification aims at returning diversified document lists to cover different user intents for ambiguous or broad queries. Existing diversity measures assume that user intents are independent or exclusive, and do not consider the relationships among the intents. In this paper, we introduce intent hierarchies to model the relationships among intents. Based on intent hierarchies, we propose several hierarchical measures that can consider the relationships among intents. We demonstrate the feasibility of hierarchical measures by using a new test collection based on TREC Web Track 2009-2013 diversity test collections. Our main experimental findings are: (1) Hierarchical measures are generally more discriminative and intuitive than existing measures using flat lists of intents
(2) When the queries have multilayer intent hierarchies, hierarchical measures are less correlated to existing measures, but can get more improvement in discriminative power
(3) Hierarchical measures are more intuitive in terms of diversity or relevance. The hierarchical measures using the whole intent hierarchies are more intuitive than only using the leaf nodes in terms of diversity and relevance.

Traditional pooling-based information retrieval (IR) test collections typically have n = 50-100 topics, but it is difficult for an IR researcher to say why the topic set size should really be n. The present study provides details on principled ways to determine the number of topics for a test collection to be built, based on a specific set of statistical requirements. We employ Nagata's three sample size design techniques, which are based on the paired t test, one-way ANOVA, and confidence intervals, respectively. These topic set size design methods require topic-by-run score matrices from past test collections for the purpose of estimating the within-system population variance for a particular evaluation measure. While the previous work of Sakai incorrectly used estimates of the total variances, here we use the correct estimates of the within-system variances, which yield slightly smaller topic set sizes than those reported previously by Sakai. Moreover, this study provides a comparison across the three methods. Our conclusions nevertheless echo those of Sakai: as different evaluation measures can have vastly different within-system variances, they require substantially different topic set sizes under the same set of statistical requirements; by analysing the tradeoff between the topic set size and the pool depth for a particular evaluation measure in advance, researchers can build statistically reliable yet highly economical test collections.

Given a topic-by-run score matrix from past data, topic set size design methods can help test collection builders determine the number of topics to create for a new test collection from a statistical viewpoint. In this study, we apply a recently-proposed score standardisation method called std-AB to score matrices before applying topic set size design, and demonstrate its advantages. For topic set size design, std-AB suppresses score variances and thereby enables test collection builders to consider realistic choices of topic set sizes, and to handle unnormalised measures in the same way as normalised measures. In addition, even discrete measures that clearly violate normality assumptions look more continuous after applying std-AB, which may make them more suitable for statistically motivated topic set size design. Our experiments cover a variety of tasks and evaluation measures from NTCIR-12.

A large percentage of queries issued to search engines are broad or ambiguous. Search result diversification aims to solve this problem, by returning diverse results that can fulfill as many different information needs as possible. Most existing intent-aware search result diversification algorithms formulate user intents for a query as a flat list of subtopics. In this paper, we introduce a new hierarchical structure to represent user intents and propose two general hierarchical diversification models to leverage hierarchical intents. Experimental results show that our hierarchical diversification models outperform state-of-the-art diversification methods that use traditional flat subtopics.

When a digital library user searches for publications by an author name, she often sees a mixture of publications by different authors who have the same name. With the growth of digital libraries and involvement of more authors, this author ambiguity problem is becoming critical. Author disambiguation (AD) often tries to solve this problem by leveraging metadata such as coauthors, research topics, publication venues and citation information, since more personal information such as the contact details is often restricted or missing. In this paper, we study the problem of how to efficiently disambiguate author names given an incessant stream of published papers. To this end, we propose a "BatchAD+IncAD" framework for dynamic author disambiguation. First, we perform batch author disambiguation (BatchAD) to disambiguate all author names at a given time by grouping all records (each record refers to a paper with one of its author names) into disjoint clusters. This establishes a one-to-one mapping between the clusters and real-world authors. Then, for newly added papers, we periodically perform incremental author disambiguation (IncAD), which determines whether each new record can be assigned to an existing cluster, or to a new cluster not yet included in the previous data. Based on the new data, IncAD also tries to correct previous AD results. Our main contributions are: (1) We demonstrate with real data that a small number of new papers often have overlapping author names with a large portion of existing papers, so it is challenging for IncAD to effectively leverage previous AD results. (2) We propose a novel IncAD model which aggregates metadata from a cluster of records to estimate the author's profile such as her coauthor distributions and keyword distributions, in order to predict how likely it is that a new record is "produced" by the author. (3) Using two labeled datasets and one large-scale raw dataset, we show that the proposed method is much more efficient than state-of-the-art methods while ensuring high accuracy.

Short Text Conversation (STC) is a new NTCIR task which tackles the following research question: given a microblog repository and a new post to that microblog, can systems reuse an old comment from the respository to satisfy the author of the new post? The official evaluation measures of STC are normalised gain at 1 (nG@1), normalised expected reciprocal rank at 10 (nERR@10), and P+, all of which can be regarded as evaluation measures for navigational intents. In this study, we apply the topic set size design technique of Sakai to decide on the number of test topics, using variance estimates of the above evaluation measures. Our main conclusion is to create 100 test topics, but what distinguishes our work from other tasks with similar topic set sizes is that we know what this topic set size means from a statistical viewpoint for each of our evaluation measures. We also demonstrate that, under the same set of statistical requirements, the topic set sizes required by nERR@10 and P+ are more or less the same, while nG@1 requires more than twice as many topics. To our knowledge, our task is the first among all efforts at TREC-like evaluation conferences to actually create a new test collection by using this principled approach.

A researcher decides to build a test collection for comparing her new information retrieval (IR) systems with several state-of-the-art baselines. She wants to know the number of topics (n) she needs to create in advance, so that she can start looking for (say) a query log large enough for sampling n good topics, and estimating the relevance assessment cost. We provide practical solutions to researchers like her using power analysis and sample size design techniques, and demonstrate its usefulness for several IR tasks and evaluation measures. We consider not only the paired t-test but also one-way analysis of variance (ANOVA) for significance testing to accommodate comparison of m(≥ 2) systems under a given set of statistical requirements (α: the Type I error rate, β: the Type II error rate, and minD: the minimum detectable difference between the best and the worst systems). Using our simple Excel tools and some pooled variance estimates from past data, researchers can design statistically well-designed test collections. We demonstrate that, as different evaluation measures have different variances across topics, they inevitably require different topic set sizes. This suggests that the evaluation measures should be chosen at the test collection design phase. Moreover, through a pool depth reduction experiment with past data, we show how the relevance assessment cost can be reduced dramatically while freezing the set of statistical requirements. Based on the cost analysis and the available budget, researchers can determine the right balance betweeen n and the pool depth pd. Our techniques and tools are applicable to test collections for non-IR tasks as well.

Review comments posted in online websites can help the user decide a product to purchase or place to visit. They can also be useful to closely compare a couple of candidate entities. However, the user may have to read different webpages back and forth for comparison, and this is not desirable particularly when she is using a mobile device. We present ReviewCollage, a mobile interface that aggregates information about two reviewed entities in a one-page view. ReviewCollage uses attribute-value pairs, known to be effective for review text summarization, and highlights the similarities and differences between the entities. Our user study confirms that ReviewCollage can support the user to compare two entities and make a decision within a couple of minutes, at least as quickly as existing summarization interfaces. It also reveals that ReviewCollage could be most useful when two entities are very similar.

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.

The evaluation of diversified web search results is a relatively new research topic and is not as well-understood as the time-honoured evaluation methodology of traditional IR based on precision and recall. In diversity evaluation, one topic may have more than one intent, and systems are expected to balance relevance and diversity. The recent NTCIR-9 evaluation workshop launched a new task called INTENT which included a diversified web search subtask that differs from the TREC web diversity task in several aspects: the choice of evaluation metrics, the use of intent popularity and per-intent graded relevance, and the use of topic sets that are twice as large as those of TREC. The objective of this study is to examine whether these differences are useful, using the actual data recently obtained from the NTCIR-9 INTENT task. Our main experimental findings are: (1) The evaluation framework used at NTCIR provides more "intuitive" and statistically reliable results than Intent-Aware Expected Reciprocal Rank; (2) Utilising both intent popularity and per-intent graded relevance as is done at NTCIR tends to improve discriminative power, particularly for -nDCG; and (3) Reducing the topic set size, even by just 10 topics, can affect not only significance testing but also the entire system ranking; when 50 topics are used (as in TREC) instead of 100 (as in NTCIR), the system ranking can be substantially different from the original ranking and the discriminative power can be halved. These results suggest that the directions being explored at NTCIR are valuable.

Building test collections based on nuggets is useful evaluating systems that return documents, answers, or summaries. However, nugget construction requires a lot of manual work and is not feasible for large query sets. Towards an efficient and scalable nugget-based evaluation, we study the applicability of semi-automatic nugget extraction in the context of the ongoing NTCIR One Click Access (1CLICK) task. We compare manually-extracted and semi-automatically- extracted Japanese nuggets to demonstrate the coverage and efficiency of the semi-automatic nugget extraction. Our findings suggest that the manual nugget extraction can be replaced with a direct adaptation of the English semi-automatic nugget extraction system, especially for queries for which the user desires broad answers from free-form text. Copyright © 2013 ACM.

The One Click Access Task (1CLICK) of NTCIR requires systems to return a concise multi-document summary of web pages in response to a query which is assumed to have been submitted in a mobile context. Systems are evaluated based on information units (or iUnits), and are required to present important pieces of information first and to minimise the amount of text the user has to read. Using the official Japanese results of the second round of the 1CLICK task from NTCIR-10, we discuss our task setting and evaluation framework. Our analyses show that: (1) Simple baseline methods that leverage search engine snippets or Wikipedia are effective for "lookup" type queries but not necessarily for other query types
(2) There is still a substantial gap between manual and automatic runs
and (3) Our evaluation metrics are relatively robust to the incompleteness of iUnits. Copyright © 2013 ACM.

The NTCIR INTENT task comprises two subtasks: Subtopic Mining, where systems are required to return a ranked list of subtopic strings for each given query
and Document Ranking, where systems are required to return a diversified web search result for each given query. This paper summarises the novel features of the Second INTENT task at NTCIR-10 and its main findings, and poses some questions for future diversified search evaluation. Copyright © 2013 ACM.

Most commercial search engines have a query suggestion feature, which is designed to capture various possible search intents behind the user's original query. However, even though different search intents behind a given query may have been popular at different time periods in the past, existing query suggestion methods neither utilize nor present such information. In this study, we propose Time-aware Structured Query Suggestion (TaSQS) which clusters query suggestions along a timeline so that the user can narrow down his search from a temporal point of view. Moreover, when a suggested query is clicked, TaSQS presents web pages from query-URL bipartite graphs after ranking them according to the click counts within a particular time period. Our experiments using data from a commercial search engine log show that the time-aware clustering and the time-aware document ranking features of TaSQS are both effective. Copyright © 2013 ACM.

Aggregating search results from a variety of diverse verticals such as news, images, videos and Wikipedia into a single interface is a popular web search presentation paradigm. Although several aggregated search (AS) metrics have been proposed to evaluate AS result pages, their properties remain poorly understood. In this paper, we compare the properties of existing AS metrics under the assumptions that (1) queries may have multiple preferred verticals
(2) the likelihood of each vertical preference is available
and (3) the topical relevance assessments of results returned from each vertical is available. We compare a wide range of AS metrics on two test collections. Our main criteria of comparison are (1) discriminative power, which represents the reliability of a metric in comparing the performance of systems, and (2) intuitiveness, which represents how well a metric captures the various key aspects to be measured (i.e. various aspects of a user's perception of AS result pages). Our study shows that the AS metrics that capture key AS components (e.g., vertical selection) have several advantages over other metrics. This work sheds new lights on the further developments and applications of AS metrics. Copyright 2013 ACM.

It has long been recognized that search queries are often broad and ambiguous. Even when submitting the same query, different users may have different search intents. Moreover, the intents are dynamically evolving. Some intents are constantly popular with users, others are more bursty. We propose a method for mining dynamic query intents from search query logs. By regarding the query logs as a data stream, we identify constant intents while quickly capturing new bursty intents. To evaluate the accuracy and efficiency of our method, we conducted experiments using 50 topics from the NTCIR INTENT-9 data and additional five popular topics, all supplemented with six-month query logs from a commercial search engine. Our results show that our method can accurately capture new intents with short response time. Copyright 2013 ACM.

Most of the existing Information Retrieval (IR) metrics discount the value of each retrieved relevant document based on its rank. This statement also applies to the evaluation of diversified search: the widely-used diversity metrics, namely, α-nDCG, Intent-Aware Expected Reciprocal Rank (ERR-IA) and D#-nDCG, are all rank-based. These evaluation metrics regard the system output as a list of document IDs, and ignore all other features such as snippets and document full texts of various lengths. In contrast, the U-measure framework of Sakai and Dou uses the amount of text read by the user as the foundation for discounting the value of relevant information, and can take into account the user's snippet reading and full text reading behaviours. The present study compares the diversity versions of U-measure (D-U and U-IA) with the state-of-the-art diversity metrics using the concordance test: given a pair of ranked lists, we quantify the ability of each metric to favour the more diversified and more relevant list. Our results show that while D#-nDCG is the overall winner in terms of simultaneous concordance with diversity and relevance, D-U and U-IA statistically significantly outperform other state-of-the-art metrics. Moreover, in terms of concordance with relevance alone, D-U and U-IA significantly outperform all rank-based diversity metrics. Thus, D-U and U-IA are not only more realistic but also more relevance-oriented than other diversity metrics. © 2013 Springer-Verlag.

In sponsored search, many studies focus on finding the most relevant advertisements (ads) and their optimal ranking for a submitted query. Determining whether it is suitable to show ads has received less attention. In this paper, we introduce the concept of user-aware advertisability, which refers to the probability of ad-click on sponsored ads when a specific user submits a query. When computing the advertisability for a given query-user pair, we first classify the clicked web pages based on a pre-defined category hierarchy and use the aggregated topical categories of clicked web pages to represent user preference. Taking user preference into account, we then compute the ad-click probability for this query-user pair. Compared with existing methods, the experimental results show that user preference is of great value for generating user-specific advertisability. In particular, our approach that computes advertisability per query-user pair outperforms the two state-of-the-art methods that compute advertisability per query in terms of a variant of the normalized Discounted Cumulative Gain metric. © 2013 Springer-Verlag.

Given an ambiguous or underspecified query, search result diversification aims at accommodating different user intents within a single Search Engine Result Page (SERP). While automatic identification of different intents for a given query is a crucial step for result diversification, also important is the estimation of intent types (informational vs. navigational). If it is possible to distinguish between informational and navigational intents, search engines can aim to return one best URL for each navigational intent, while allocating more space to the informational intents within the SERP. In light of the observations, we propose a new framework for search result diversification that is intent importance-aware and type-aware. Our experiments using the NTCIR-9 INTENT Japanese Subtopic Mining and Document Ranking test collections show that: (a) our intent type estimation method for Japanese achieves 64.4% accuracy
and (b) our proposed diversification method achieves 0.6373 in D#-nDCG and 0.5898 in DIN#-nDCG over 56 topics, which are statistically significant gains over the top performers of the NTCIR-9 INTENT Japanese Document Ranking runs. Moreover, our relevance oriented model significantly outperforms our diversity oriented model and the original model by Dou et al.. © 2013 Springer-Verlag.

Search result diversification is important for accommodating different user needs by means of covering popular and diverse query intents within a single result page. To evaluate diversity, we believe that it is important to consider the distinction between informational and navigational intents, as users would not want redundant information especially for navigational intents. In this study, we conduct intent type-sensitive diversity evaluation based on both top-down labelling, which labels each intent as either navigational or informational a priori, and bottom-up labelling, which labels each intent based on whether a "navigational relevant" document has actually been identified in the document collection. Our results suggest that reliable type-sensitive diversity evaluation can be conducted using the top-down approach with a clear intent labelling guideline, while ensuring that the desired URLs for navigational intents make their way into relevance assessments. © 2013 Springer-Verlag.

We propose a new method for query-oriented extractive multi-document summarization. To enrich the information need representation of a given query, we build a co-occurrence graph to obtain words that augment the original query terms. We then formulate the summarization problem as a Maximum Coverage Problem with Knapsack Constraints based on word pairs rather than single words. Our experiments with the NTCIR ACLIA question answering test collections show that our method achieves a pyramid F3-score of up to 0.313, a 36% improvement over a baseline using Maximal Marginal Relevance.

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 is often difficult for the user to choose from a list of query suggestions, and to understand the relation between an input query and suggested ones. In this paper, we propose a new method to present query suggestions to the user, which has been designed to help two popular query reformulation actions, namely, specialization (e.g. from "nikon" to "nikon camera") and parallel movement (e.g. from "nikon camera" to "canon camera"). Using a query log collected from a popular commercial Web search engine, our prototype called SParQS classifies query suggestions into automatically generated categories and generates a label for each category. Moreover, SParQS presents some new entities as alternatives to the original query (e.g. "canon" in response to the query "nikon"), together with their query suggestions classified in the same way as the original query's suggestions. We conducted a task-based user study to compare SParQS with a traditional "flat list" query suggestion interface. Our results show that the SParQS interface enables subjects to search more successfully than the flat list case, even though query suggestions presented were exactly the same in the two interfaces. In addition, the subjects found the query suggestions more helpful when they were presented in the SParQS interface rather than in a flat list.

We propose a new method for query-oriented extractive multi-document summarization. To enrich the information need representation of a given query, we build a co-occurrence graph to obtain words that augment the original query terms. We then formulate the summarization problem as a Maximum Coverage Problem with Knapsack Constraints based on word pairs rather than single words. Our experiments with the NTCIR ACLIA question answering test collections show that our method achieves a pyramid F3-score of up to 0.313, a 36% improvement over a baseline using Maximal Marginal Relevance.

A diversified search result for an underspecified query generally contains web pages in which there are answers that are relevant to different aspects of the query. In order to help the user locate such relevant answers, we propose a simple extension to the standard Search Engine Result Page (SERP) interface, called AspecTiles. In addition to presenting a ranked list of URLs with their titles and snippets, AspecTiles visualizes the relevance degree of a document to each aspect by means of colored squares ("tiles"). To compare AspecTiles with the standard SERP interface in terms of usefulness, we conducted a user study involving 30 search tasks designed based on the TREC web diversity task topics as well as 32 participants. Our results show that AspecTiles has some advantages in terms of search performance, user behavior, and user satisfaction. First, AspecTiles enables the user to gather relevant information significantly more efficiently than the standard SERP interface for tasks where the user considers several different aspects of the query to be important at the same time (multi-aspect tasks). Second, AspecTiles affects the user's information seeking behavior: with this interface, we observed significantly fewer query reformulations, shorter queries and deeper examinations of ranked lists in multi-aspect tasks. Third, participants of our user study found AspecTiles significantly more useful for finding relevant information and easy to use than the standard SERP interface. These results suggest that simple interfaces like AspecTiles can enhance the search performance and search experience of the user when their queries are underspecified. © 2012 ACM.

This paper tackles the problem of mining subgoals of a given search goal from data. For example, when a searcher wants to travel to London, she may need to accomplish several subtasks such as "book flights," "book a hotel," "find good restaurants" and "decide which sightseeing spots to visit." As another example, if a searcher wants to lose weight, there may exist several alternative solutions such as "do physical exercise," "take diet pills," and "control calorie intake." In this paper, we refer to such subtasks or solutions as subgoals, and propose to utilize sponsored search data for finding subgoals of a given query by means of query clustering. Advertisements (ads) reflect advertisers' tremendous efforts in trying to match a given query with implicit user needs. Moreover, ads are usually associated with a particular action or transaction. We therefore hypothesized that they are useful for subgoal mining. To our knowledge, our work is the first to use sponsored search data for this purpose. Our experimental results show that sponsored search data is a good resource for obtaining related queries and for identifying subgoals via query clustering. In particular, our method that combines ad impressions from sponsored search data and query co-occurrences from session data outperforms a state-of-the-art query clustering method that relies on document clicks rather than ad impressions in terms of purity, NMI, Rand Index, F1-measure and subgoal recall. © 2012 ACM.

Traditional ad hoc IR test collections were built using a relatively large pool depth (e.g. 100), and are usually assumed to be reusable. Moreover, when they are reused to compare a new system with another or with systems that contributed to the pools ("contributors"), an even larger measurement depth (e.g. 1,000) is often used for computing evaluation metrics. In contrast, the web diversity test collections that have been created in the past few years at TREC and NTCIR use a much smaller pool depth (e.g. 20). The measurement depth is also small (e.g. 10-30), as search result diversification is primarily intended for the first result page. In this study, we examine the reusability of a typical web diversity test collection, namely, one from the NTCIR-9 INTENT-1 Chinese Document Ranking task, which used a pool depth of 20 and official measurement depths of 10, 20 and 30. First, we conducted additional relevance assessments to expand the official INTENT-1 collection to achieve a pool depth of 40. Using the expanded relevance assessments, we show that run rankings at the measurement depth of 30 are too unreliable, given that the pool depth is 20. Second, we conduct a leave-one-out experiment for every participating team of the INTENT-1 Chinese task, to examine how (un)fairly new runs are evaluated with the INTENT-1 collection. We show that, for the purpose of comparing new systems with the contributors of the test collection being used, condensed-list versions of existing diversity evaluation metrics are more reliable than the raw metrics. However, even the condensed-list metrics may be unreliable if the new systems are not competitive compared to the contributors. © Springer-Verlag 2012.

This paper extends the evaluation framework of the NTCIR-9 One Click Access Task (1CLICK-1), which required systems to return a single, concise textual output in response to a query in order to satisfy the user immediately after a click on the SEARCH button. Unlike traditional nugget-based summarisation and question answering evaluation methods, S-measure, the official evaluation metric of 1CLICK-1, discounts the value of each information unit based on its position within the textual output. We first show that the discount parameter L of S-measure affects system ranking and discriminative power, and that using multiple values, e.g. L = 250 (user has only 30 seconds to view the text) and L = 500 (user has one minute), is beneficial. We then complement the recall-like S-measure with a simple, precision-like metric called T-measure as well as a combination of S-measure and T-measure, called S#. We show that S# with a heavy emphasis on S-measure imposes an appropriate length penalty to 1CLICK-1 system outputs and yet achieves discriminative power that is comparable to S-measure. These new metrics will be used at NTCIR-10 1CLICK-2. © Springer-Verlag 2012.

This paper presents a grid-based interaction model that is designed to encourage searchers to organize a complex search space by managing n x m sub spaces. A search interface was developed based on the proposed interaction model, and its performance was evaluated by a user study carried out in the context of the NTCIR-9 VisEx Task. With the proposed interface, there were cases where subjects discovered new knowledge without accessing external resources when compared to a baseline system. The encouraging results from experiments warrant further studies on the model. © Springer-Verlag 2012.

Community Question Answering (CQA) sites such as Yahoo ! Answers have emerged as rich knowledge resources for information seekers. However, answers posted to CQA sites can be irrelevant, incomplete, redundant, incorrect, biased, ill-formed or even abusive. Hence, automatic selection of "good" answers for a given posted question is a practical research problem that will help us manage the quality of accumulated knowledge. One way to evaluate answer selection systems for CQA would be to use the Best Answers (BAs) that are readily available from the CQA sites. However, BAs may be biased, and even if they are not, there may be other good answers besides BAs. To remedy these two problems, we propose system evaluation methods that involve multiple answer assessors and graded-relevance information retrieval metrics. Our main findings from experiments using the NTCIR-8 CQA task data are that, using our evaluation methods, (a) we can detect many substantial differences between systems that would have been overlooked by BA-based evaluation
and (b) we can better identify hard questions (i.e. those that are handled poorly by many systems and therefore require focussed investigation) compared to BAbased evaluation. We therefore argue that our approach is useful for building effective CQA answer selection systems despite the cost of manual answer assessments. Copyright 2011 ACM.

In this research, we investigated whether a computer could estimate the best answer on a Q site. First, a best answer estimation experiment was carried out with human assessors. The data of Yahoo! Chiebukuro was used for the experiment; 50 questions extracted at random from four categories, viz., "Consultation of love," "Personal computer,""General knowledge," and "Politics," were used. The accuracy rate(precision) of the estimation by two assessors was 50% and 52% (random estimation: 34%) for "Consultation of love," 62% and 58% (random estimation: 38%) for "Personal computer," 54% and 56% (random estimation: 37%) for "General knowledge," and 56% and 60% (random estimation:35.8%) for "Politics." Next, the experimental results were analyzed, and the machine learning system with "Detailed","Evidence", and "Polite" in the feature as a factor to choose the best answer was constructed. The precision of the machine learning system exceeded the assessors' results in the "Personal computer"(67%) category, and it fell below the assessors' results in the "Consultation of love"(41%) category. In the "General knowledge" and "Politics" categories, the precision of the machine learning system was almost equal to the assessors' results.

Information Retrieval (IR) test collections are growing larger, and relevance data constructed through pooling are suspected of becoming more and more incomplete and biased. Several studies have used IR evaluation metrics specifically designed to handle this problem, but most of them have only examined the metrics under incomplete but unbiased conditions, using random samples of the original relevance data. This paper examines nine metrics in more realistic settings, by reducing the number of pooled systems and the number of pooled documents. Even though previous studies have shown that metrics based on a condensed list, obtained by removing all unjudged documents from the original ranked list, are effective for handling very incomplete but unbiased relevance data, we show that these results do not hold when the relevance data are biased towards particular systems or towards the top of the pools. More specifically, we show that the condensed-list versions of Average Precision, Qmeasure and normalised Discounted Cumulative Gain, which we denote as AP′, Q′ and nDCG′, are not necessarily superior to the original metrics for handling biases. Nevertheless, AP′ and Q′ are generally superior to bpref, Rank-Biased Precision and its condensed-list version even in the presence of biases.

We analyse the query log of a click-oriented Japanese search engine that utilises the link structures of Wikipedia for encouraging the user to change his information need and to perform repeated, serendipitous, exploratory search. Our results show that users tend to make transitions within the same query type: from person names to person names, from place names to place names, and so on.

Modern information retrieval (IR) test collections have grown in size, but the available manpower for relevance assessments has more or less remained constant. Hence, how to reliably evaluate and compare IR systems using incomplete relevance data, where many documents exist that were never examined by the relevance assessors, is receiving a lot of attention. This article compares the robustness of IR metrics to incomplete relevance assessments, using four different sets of graded-relevance test collections with submitted runs-the TREC 2003 and 2004 robust track data and the NTCIR-6 Japanese and Chinese IR data from the crosslingual task. Following previous work, we artificially reduce the original relevance data to simulate IR evaluation environments with extremely incomplete relevance data. We then investigate the effect of this reduction on discriminative power, which we define as the proportion of system pairs with a statistically significant difference for a given probability of Type I Error, and on Kendall's rank correlation, which reflects the overall resemblance of two system rankings according to two different metrics or two different relevance data sets. According to these experiments, Q', nDCG' and AP' proposed by Sakai are superior to bpref proposed by Buckley and Voorhees and to Rank-Biased Precision proposed by Moffat and Zobel. We also point out some weaknesses of bpref and Rank-Biased Precision by examining their formal definitions.

Information retrieval systems are compared using evaluation metrics, with researchers commonly reporting results for simple metrics such as precision-at-10 or reciprocal rank together with more complex ones such as average precision or discounted cumulative gain. In this paper, we demonstrate that complex metrics are as good as or better than simple metrics at predicting the performance of the simple metrics on other topics. Therefore, reporting of results from simple metrics alongside complex ones is redundant.

Test collections are growing larger, and relevance data constructed through pooling are suspected of becoming more and more incomplete and biased. Several studies have used evaluation metrics specifically designed to handle this problem, but most of them have only examined the metrics under incomplete but unbiased conditions, using random samples of the original relevance data. This paper examines nine metrics in a more realistic setting, by reducing the number of pooled systems. Even though previous work has shown that metrics based on a condensed list, obtained by removing all unjudged documents from the original ranked list, are effective for handling very incomplete but unbiased relevance data, we show that these results do not hold in the presence of system bias. In our experiments using TREC and NTCIR data, we first show that condensed-list metrics overestimate new systems while traditional metrics underestimate them, and that the overestimation tends to be larger than the underestimation. We then show that, when relevance data is heavily biased towards a single team or a few teams, the condensed-list versions of Average Precision (AP), Q-measure (Q) and normalised Discounted Cumulative Gain (nDCG), which we call AP', Q' and nDCG', are not necessarily superior to the original metrics in terms of discriminative power, i.e., the overall ability to detect pairwise statistical significance. Nevertheless, even under system bias, AP' and Q' are generally more discriminative than bpref and the condensed-list version of Rank-Biased Precision (RBP), which we call RBP'. Copyright 2008 ACM.

This paper compares 14 information retrieval metrics based on graded relevance, together with 10 traditional metrics based on binary relevance, in terms of stability, sensitivity and resemblance of system rankings. More specifically, we compare these metrics using the Buckley/Voorhees stability method, the Voorhees/Buckley swap method and Kendall's rank correlation, with three data sets comprising test collections and submitted runs from NTCIR. Our experiments show that (Average) Normalised Discounted Cumulative Gain at document cut-off I are the best among the rank-based graded-relevance metrics, provided that l is large. On the other hand, if one requires a recall-based graded-relevance metric that is highly correlated with Average Precision, then Q-measure is the best choice. Moreover, these best graded-relevance metrics are at least as stable and sensitive as Average Precision, and are fairly robust to the choice of gain values. (c) 2006 Elsevier Ltd. All rights reserved.

Recently, a number of TREC tracks have adopted a retrieval effectiveness metric called bpref which has been designed for evaluation environments with incomplete relevance data. A graded-relevance version of this metric called rpref has also been proposed. However, we show that the application of Q-measure, normalised Discounted Cumulative Gain (nDCG) or Average Precision (AveP)to condensed lists, obtained by ?ltering out all unjudged documents from the original ranked lists, is actually a better solution to the incompleteness problem than bpref. Furthermore, we show that the use of graded relevance boosts the robustness of IR evaluation to incompleteness and therefore that Q-measure and nDCG based on condensed lists are the best choices. To this end, we use four graded-relevance test collections from NTCIR to compare ten different IR metrics in terms of system ranking stability and pairwise discriminative power. Copyright 2007 ACM.

In our previous work, we developed a prototype of a speech-input help system for home appliances such as digital cameras and microwave ovens. Given a factoid question, the system performs textual question answering using the manuals as the knowledge source. Whereas, given a HOW question, it retrieves and plays a demonstration video. However, our first prototype suffered from speech recognition errors, especially when the Japanese interrogative phrases in factoid questions were misrecognized. We therefore propose a method for solving this problem, which complements a speech query transcript with an interrogative phrase selected from a pre-determined list. The selection process first narrows down candidate phrases based on co-occurrences within the manual text, and then computes the similarity between each candidate and the query transcript in terms of pronunciation. Our method improves the Mean Reciprocal Rank of top three answers from 0.429 to 0.597 for factoid questions.

We introduce a system called Pic-A-Topic, which analyses closed captions of Japanese TV shows on travel to perform topic segmentation and topic sentence selection. Our objective is to provide a table-of-contents interface that enables efficient viewing of desired topical segments within recorded TV shows to users of appliances such as hard disk recorders and digital TVs. According to our experiments using 14.5 hours of recorded travel TV shows, Pic-A-Topic's F1-measure for the topic segmentation task is 82% of manual performance on average. Moreover, a preliminary user evaluation experiment suggests that this level of performance may be indistinguishable from manual performance.

This paper compares the sensitivity of IR metrics designed for the task of finding one relevant document, using a method recently proposed at SIGIR 2006. The metrics are: P+-measure, P-measure, O-measure, Normalised Weighted Reciprocal Rank (NWRR) and Reciprocal Rank (RR). All of them except for RR can handle graded relevance. Unlike the ad hoc (but nevertheless useful) "swap" method proposed by Voorhees and Buckley, the new method derives the sensitivity and the performance difference required to guarantee a given significance level directly from Bootstrap Hypothesis Tests. We use four data sets from NTCIR to show that, according to this method, "P((+))-measure >= O-measure >= NWRR >= RR" generally holds, where ">=" means "is at least as sensitive as". These results generalise and reinforce previously reported ones based on the swap method. Therefore, we recommend the use of P((+))-measure and O-measure for practical tasks such as known-item search where recall is either unimportant or immeasurable.

At NTCIR-4, new retrieval effectiveness metrics called Q-measure and R-measure were proposed for evaluation based on multi-grade relevance. This paper shows that Q-measure inherits both the reliability of noninterpolated Average Precision and the multigrade relevance capability of Average Weighted Precision through a theoretical analysis, and then verify the above claim through experiments by actually ranking the systems submitted to the NTCIR-3 CLIR Task. Our experiments confirm that the Q-measure ranking is very highly correlated with the Average Precision ranking and that it is more reliable than Average Weighted Precision.

This paper compares 14 metrics designed for information retrieval evaluation with graded relevance, together with 10 traditional metrics based on binary relevance, in terms of reliability and resemblance of system rankings. More specifically, we use two test collections with submitted runs from the Chinese IR and English IR tasks in the NTCIR-3 CUR track to examine the metrics using methods proposed by Buckley/Voorhees and Voorhees/Buckley as well as Kendall's rank correlation. Our results show that AnDCC(l) and nDCC(l) ((Average) Normalised Discounted Cumulative Cain at Document cut-off 1) are good metrics, provided that I is large. However, if one wants to avoid the parameter I altogether, or if one requires a metric that closely resembles TREC Average Precision, then Q-measure appears to be the best choice.

This paper describes a method for automatically converting existing English-Japanese and Japanese-English machine translation dictionaries into English-Japanese transliteration rules and Japanese-English back-transliteration rules for cross language information retrieval. An existing English-katakana word alignment module, which is part of our own machine translation system, is exploited in generating probabilistic rewriting rules. If our system is allowed to output 15 candidate spellings, it successfully transliterates more than 75% of a set of out-of-vocabulary English words into katakana, and successfully back-transliterates more than 55% of a set of out-of-vocabulary katakana words into English. Moreover, our preliminary cross-language information retrieval experiments, which treat the candidate spellings as a group of synonyms, suggest that our methods can indeed compensate for the failure of machine translation in some cases.

Internet search engines allow access to online information from all over the world. However, there is currently a general assumption that users are fluent in the languages of all documents that they might search for. This has for historical reasons usually been a choice between English and the locally supported language. Given the rapidly growing size of the Internet, it is likely that future users will need to access information in languages in which they are not fluent or have no knowledge of at all. This paper shows how information retrieval and machine translation can be combined in a cross-language information access framework to help overcome the language barrier. We present encouraging preliminary experimental results using English queries to retrieve documents from the standard Japanese language BMIR-J2 retrieval test collection. We outline the scope and purpose of cross-language information access and provide an example application to suggest that technology already exists to provide effective and potentially useful applications.

The application of relevance feedback techniques has been shown to improve retrieval performance for a number of information retrieval tasks. This paper explores incremental relevance feedback for ad hoc Japanese text retrieval
examining, separately and in combination, the utility of term reweighting and query expansion using a probabilistic retrieval model. Retrieval performance is evaluated in terms of standard precision-recall measures, and also using "number-to-view" graphs. Experimental results, on the standard BMIR-J2 Japanese language retrieval collection, show that both term reweighting and query expansion improve retrieval performance. This is reflected in improvements in both precision and recall, but also a reduction in the average number of documents which must be viewed to find a selected number of relevant items. In particular, using a simple simulation of user searching, incremental application of relevance information is shown to lead to progressively improved retrieval performance and an overall reduction in the number of documents that a user must view to find relevant ones. © 2000 Kluwer Academic Publishers.

In this paper we report results of an investigation into English-Japanese Cross-Language Information Retrieval (CLIR) comparing a number of query translation methods. Results from experiments using the standard BMIR-J2 Japanese collection suggest that full machine translation (MT) can outperform popular dictionary-based query translation methods and further that in this context MT is largely robust to queries with little linguistic structure.