waashk/dblp huggingface dataset:
adasum: an adaptive model for summarization. topic representation mismatch is a key problem in topic-oriented summarization for the specified topic is usually too short to understand/interpret. this paper proposes a novel adaptive model for summarization, adasum, under the assumption that the summary and the topic representation can be mutually boosted. adasum aims to simultaneously optimize the topic representation and extract effective summaries. this model employs a mutual boosting process to minimize the topic representation mismatch for base summarizers. furthermore, a linear combination of base summarizers is proposed to further reduce the topic representation mismatch from the diversity of base summarizers with a general learning framework. we prove that the training process of adasum can enhance the performance measure used. experimental results on duc 2007 dataset show that adasum significantly outperforms the baseline methods for summarization (e.g. mrp, lexrank, and gsps).
using a graph-based ontological user profile for personalizing search. in this poster, we describe a personalized search approach, which involves a graph based user profile issued from ontology and a session boundary recognition mechanism. the user profile refers to the short term user interest and is used for re-ranking the search results of queries in the same search session. the session boundary recognition is based on tracking changes in the dominant concepts held by the query and the user profile. experimental evaluation was carried out using the hard 2003 trec collection and shows that our approach is effective.
investigating external corpus and clickthrough statistics for query expansion in the legal domain. we apply language modeling keyword search augmented with berger and lafferty's (1999) translation model for query expansion to formulate three query expansion methods using word co-occurrence statistics from a large external corpus and user clickthrough data. we study the performance of these methods on a vertical domain (case law documents) using standard metrics and an evaluation framework designed specifically to measure the performance of query expansion under varying degrees of query-document term mismatch.
content-based filtering for efficient online materialized view maintenance. real-time materialized view maintenance has become increasingly popular, especially in real-time data warehousing and data streaming environments. upon updates to base relations, maintaining the corresponding materialized views can bring a heavy burden to the rdbms. a traditional method to mitigate this problem is to use the where clause condition in the materialized view definition to detect whether an update to a base relation is relevant and can affect the materialized view. however, this detection method does not consider the content in the base relations and hence misses a large number of filtering opportunities. in this paper, we propose a content-based method for detecting irrelevant updates to base relations of a materialized view. at the cost of using more space, this method increases the probability of catching irrelevant updates by judiciously designing filtering relations to capture the content in the base relations. based on the content-based method, a prototype real-time data warehouse has been implemented on top of ibm's system s using ibm db2. using an analytical model and our prototype, we show that the content-based method can catch most (or all) irrelevant updates to base relations that are missed by the traditional method. thus, when the fraction of irrelevant updates is non-negligible, the load on the rdbms due to materialized view maintenance can be significantly reduced.
modeling hidden topics on document manifold. topic modeling has been a key problem for document analysis. one of the canonical approaches for topic modeling is probabilistic latent semantic indexing, which maximizes the joint probability of documents and terms in the corpus. the major disadvantage of plsi is that it estimates the probability distribution of each document on the hidden topics independently and the number of parameters in the model grows linearly with the size of the corpus, which leads to serious problems with overfitting. latent dirichlet allocation (lda) is proposed to overcome this problem by treating the probability distribution of each document over topics as a hidden random variable. both of these two methods discover the hidden topics in the euclidean space. however, there is no convincing evidence that the document space is euclidean, or flat. therefore, it is more natural and reasonable to assume that the document space is a manifold, either linear or nonlinear. in this paper, we consider the problem of topic modeling on intrinsic document manifold. specifically, we propose a novel algorithm called laplacian probabilistic latent semantic indexing (lapplsi) for topic modeling. lapplsi models the document space as a submanifold embedded in the ambient space and directly performs the topic modeling on this document manifold in question. we compare the proposed lapplsi approach with plsi and lda on three text data sets. experimental results show that lapplsi provides better representation in the sense of semantic structure.
dynamic faceted search for discovery-driven analysis. we propose a dynamic faceted search system for discovery-driven analysis on data with both textual content and structured attributes. from a keyword query, we want to dynamically select a small set of "interesting" attributes and present aggregates on them to a user. similar to work in olap exploration, we define "interestingness" as how surprising an aggregated value is, based on a given expectation. we make two new contributions by proposing a novel "navigational" expectation that's particularly useful in the context of faceted search, and a novel interestingness measure through judicious application of p-values. through a user survey, we find the new expectation and interestingness metric quite effective. we develop an efficient dynamic faceted search system by improving a popular open source engine, solr. our system exploits compressed bitmaps for caching the posting lists in an inverted index, and a novel directory structure called a bitset tree for fast bitset intersection. we conduct a comprehensive experimental study on large real data sets and show that our engine performs 2 to 3 times faster than solr.
the social (open) workspace. social networking promises individuals new dimensions of freedom to interact, associate, and give expression to their talents. recently, systems such as mechanical turk have started to facilitate self-organizing collaboration on work-related tasks. such developments raise interesting questions. is it possible to create (and sustain) businesses that do not have traditional, formal structure - without traditional "employees"? can we find and organize (and optimize) talent on the web for task-oriented work - spontaneously and efficiently? how do people relate to one another in possibly evanescent workgroups? one aspect of the challenge in the social workspace is understanding and modeling the user behavior and the economic basis for creating, preserving, and exchanging value in the marketplace when workgroup identity, orientations to property, recruiting and managing appropriate talent are not organized under traditional company structures. another aspect is the technology needed to support virtual organizations and work. the panel will discuss trends in social work and the evolving (scientific) basis of our understanding of new models of workers and organizations.
re-considering neighborhood-based collaborative filtering parameters in the context of new data. the movielens dataset and the herlocker et al. study of 1999 have been very influential in collaborative filtering. yet, the age of both invites re-examining their applicability. we use netflix challenge data to re-visit the prior results. in particular, we re-evaluate the parameters of herlocker et al.'s method on two critical factors: measuring similarity between users and normalizing the ratings of the users. we find that normalization plays a significant role and that pearson correlation is not necessarily the best similarity metric.
estimating real-valued characteristics of criminals from their recorded crimes. offender profiling concerns making inferences about a criminal from the crime(s) he has committed. where descriptionsof the crimes are recorded electronically, text mining techniques provide a means by which recorded characteristics of the offenders can be linked with features of his crimes as revealed in the text. past studies have used language modelling to identify characteristics that can be described by a categorical variable e.g. gender. here we adapt the language modelling approach to allow estimation of numerical quantities such as age and distance travelled.
can all tags be used for search? collaborative tagging has become an increasingly popular means for sharing and organizing web resources, leading to a huge amount of user generated metadata. these tags represent quite a few different aspects of the resources they describe and it is not obvious whether and how these tags or subsets of them can be used for search. this paper is the first to present an in-depth study of tagging behavior for very different kinds of resources and systems - web pages (del.icio.us), music (last.fm), and images (flickr) - and compares the results with anchor text characteristics. we analyze and classify sample tags from these systems, to get an insight into what kinds of tags are used for different resources, and provide statistics on tag distributions in all three tagging environments. since even relevant tags may not add new information to the search procedure, we also check overlap of tags with content, with metadata assigned by experts and from other sources. we discuss the potential of different kinds of tags for improving search, comparing them with user queries posted to search engines as well as through a user survey. the results are promising and provide more insight into both the use of different kinds of tags for improving search and possible extensions of tagging systems to support the creation of potentially search-relevant tags.
a sparse gaussian processes classification framework for fast tag suggestions. tagged data is rapidly becoming more available on the world wide web. web sites which populate tagging services offer a good way for internet users to share their knowledge. an interesting problem is how to make tag suggestions when a new resource becomes available. in this paper, we address the issue of efficient tag suggestion. we first propose a multi-class sparse gaussian process classification framework (sgps) which is capable of classifying data with very few training instances. we suggest a novel prototype selection algorithm to select the best subset of points for model learning. the framework is then extended to a novel multi-class multi-label classification algorithm (mmsg) that transforms tag suggestion into the problem of multi-label ranking. experiments on bench-mark data sets and real-world data from del.icio.us and bibsonomy suggest that our model can greatly improve the performance of tag suggestions when compared to the state-of-the-art. overall, our model requires linear time to train and constant time to predict per case. the memory consumption is also significantly less than traditional batch learning algorithms such as svms. in addition, results on tagging digital data also demonstrate that our model is capable of recommending relevant tags to images and videos by using their surrounding textual information.
using tag semantic network for keyphrase extraction in blogs. folksonomies provide a comfortable way to search and browse the blogosphere. as the tags in the blogosphere are sparse, ambiguous and too general, this paper proposes both a supervised and an unsupervised approach that extract tags from posts using a tag semantic network. we evaluate the two methods on a blog dataset and observe an improvement in f1-measure from 0.23 to 0.50 when compared to the baseline system.
retrievability: an evaluation measure for higher order information access tasks. evaluation in information retrieval (ir) has long focused on effectiveness and efficiency. however, new and emerging access tasks now demand alternative evaluation measures which go beyond this traditional view. a retrieval system provides a means of gaining access to documents, therefore intuitively, our view of the collection is shaped by the retrieval system. in this paper, we outline some emerging information access related scenarios that require knowledge about how the retrieval system affects the users' ability to access information. this provides the motivation for the proposed evaluation measures and methodology where the focus is on capturing the behavior of the system, in terms of how retrievable it makes individual documents within the collection. to demonstrate the utility of the proposed methods, we perform an extensive analysis on two trec collections showing how the measures can be applied to evaluate different information access questions. for higher order information access tasks that are inherently dependent on retrievability, our novel evaluation methodology emphasizes that effectiveness is an insufficient characterization of a retrieval system. this paper provides the foundations for the evaluation of higher order access related tasks.
linear time membership in a class of regular expressions with interleaving and counting. the extension of regular expressions (res) with an interleaving (shuffle) operator has been proposed in many occasions, since it would be crucial to deal with unordered data. however, interleaving badly affects the complexity of basic operations, and, expecially, makes membership np-hard [13], which is unacceptable for most uses of res. res form the basis of most xml type languages, such as dtds and xml schema types, and xduce types [16, 11]. in this context, the interleaving operator would be a natural addition to the language of res, as witnessed by the presence of limited forms of interleaving in xsd (the all group), relax-ng, and sgml, provided that the np-hardness of membership could be avoided. we present here a restricted class of res with interleaving and counting which admits a linear membership algorithm, and which is expressive enough to cover the vast majority of real-world xml types. we first present an algorithm for membership of a list of words into a re with interleaving and counting, based on the translation of the re into a set of constraints. we generalize the approach in order to check membership of xml trees into a class of edtds with interleaving and counting, which models the crucial aspects of dtds and xsd schemas.
pruning nested xquery queries. we present in this paper an approach for xquery optimization that exploits minimization opportunities raised in composition-style nesting of queries. more precisely, we consider the simplification of xquery queries in which the intermediate result constructed by a subexpression is queried by another subexpression. based on a large subset of xquery, we describe a rule-based algorithm that recursively prunes query expressions, eliminating useless intermediate results. our algorithm takes as input an xquery expression that may have navigation within its subexpressions and outputs a simplified, equivalent xquery expression, and is thus readily usable as an optimization module in any existing xquery processor. we demonstrate by experiments the impact of our rewriting approach on query evaluation costs and we prove formally its correctness.
siphon++: a hidden-webcrawler for keyword-based interfaces. the hidden web consists of data that is generally hidden behind form interfaces, and as such, it is out of reach for traditional search engines. with the goal of leveraging the high-quality information in this largely unexplored portion of the web, in this paper, we propose a new strategy for automatically retrieving data hidden behind keyword-based form interfaces. unlike previous approaches to this problem, our strategy adapts the query generation and selection by detecting features of the index. we describe an extensive experimental evaluation which shows that: our strategy is able to derive appropriate queries to obtain high coverage while, at the same time, avoiding the retrieval of redundant data; and it obtains higher coverage and is more efficient approaches that use a fixed strategy for query generation.
view and index selection for query-performance improvement: quality-centered algorithms and heuristics. selecting and precomputing indexes and materialized views, with the goal of improving query-processing performance, is an important part of database-performance tuning. the significant complexity of the view- and index-selection problem may result in high total cost of ownership for database systems. in this paper, we develop efficient methods that deliver user-specified quality of the set of selected views and indexes when given view- and index-based plans as problem inputs. here, quality means proximity to the globally optimum performance for the input query workload given the input query plans. our experimental results and comparisons on synthetic and benchmark instances demonstrate the competitiveness of our approach and show that it provides a winning combination with end-to-end view- and index-selection frameworks such as those of [1, 2].
query suggestion using hitting time. generating alternative queries, also known as query suggestion, has long been proved useful to help a user explore and express his information need. in many scenarios, such suggestions can be generated from a large scale graph of queries and other accessory information, such as the clickthrough. however, how to generate suggestions while ensuring their semantic consistency with the original query remains a challenging problem. in this work, we propose a novel query suggestion algorithm based on ranking queries with the hitting time on a large scale bipartite graph. without involvement of twisted heuristics or heavy tuning of parameters, this method clearly captures the semantic consistency between the suggested query and the original query. empirical experiments on a large scale query log of a commercial search engine and a scientific literature collection show that hitting time is effective to generate semantically consistent query suggestions. the proposed algorithm and its variations can successfully boost long tail queries, accommodating personalized query suggestion, as well as finding related authors in research.
a generative retrieval model for structured documents. structured documents contain elements defined by the author(s) and annotations assigned by other people or processes. structured documents pose challenges for probabilistic retrieval models when there are mismatches between the structured query and the actual structure in a relevant document or erroneous structure introduced by an annotator. this paper makes three contributions. first, a new generative retrieval model is proposed to deal with the mismatch problem. this new model extends the basic keyword language model by treating structure as hidden variable during the generation process. second, variations of the model are compared. third, term-level and structure-level smoothing strategies are studied. evaluation was conducted with inex xml retrieval and question-answering retrieval tasks. experimental results indicate that the optimal structured retrieval model is task dependent, two-level dirichlet smoothing significantly outperforms two-level jelinek-mercer smoothing, and with accurate structured queries, the proposed structured retrieval model outperforms keyword retrieval significantly, on both qa and inex datasets.
an approximate string matching approach for handling incorrectly typed urls. in this paper we approach the problem of providing corrections for incorrectly typed urls. this problem is significantly different from the classical spelling correction problem. we describe our contribution - building a custom data structure and a search algorithm that can find approximate matches for incorrect urls. we evaluate the quality of our results through experiments with analysts. our system is now being used in the google search engine.
pbfilter: indexing flash-resident data through partitioned summaries. nand flash has become the most popular persistent data storage medium for mobile and embedded devices. the hardware characteristics of nand flash (e.g. page granularity for read/write with a block-erase-before-rewrite constraint, limited number of erase cycles) preclude in-place updates. in this paper, we propose a new indexing scheme, called pbfilter, designed from the outset to exploit the peculiarities of nand flash.
utilization of navigational queries for result presentation and caching in search engines. we propose result page models with varying granularities for navigational queries and show that this approach provides a better utilization of cache space and reduces bandwidth requirements.
deriving non-redundant approximate association rules from hierarchical datasets. association rule mining plays an important job in knowledge and information discovery. however, there are still shortcomings with the quality of the discovered rules and often the number of discovered rules is huge and contain redundancies, especially in the case of multi-level datasets. previous work has shown that the mining of non-redundant rules is a promising approach to solving this problem, with work by [6,8,9,10] focusing on single level datasets. recent work by shaw et. al. [7] has extended the non-redundant approaches presented in [6,8,9] to include the elimination of redundant exact basis rules from multi-level datasets. here we propose a continuation of the work in [7] that allows for the removal of hierarchically redundant approximate basis rules from multi-level datasets by using a dataset's hierarchy or taxonomy.
valid scope computation for location-dependent spatial query in mobile broadcast environments. wireless data broadcast is an efficient and scalable means to provide information access for a large population of clients in mobile environments. with location-based services (lbss) deployed upon a broadcast channel, mobile clients can collect data from the channel to answer their location-dependent spatial queries (ldsqs). since the results of ldsqs would become invalid when mobile client moves to new locations, the knowledge of valid scopes for ldsq results is necessary to assist clients to determine if their previous ldsq results can be reused after they moved. this effectively improves query response time and client energy consumption. in this paper, we devise efficient algorithms to determine valid scopes for various ldsqs including range, window and nearest neighbor queries along with ldsq processing over a broadcast channel. we conduct an extensive set of experiments to evaluate the performance of our proposed algorithms. while the proposed valid scope algorithm incurs only little extra processing overhead, unnecessary ldsq reevaluation is significantly eliminated, thus providing faster query response and saving client energy.