waashk/dblp huggingface dataset:
model-based feedback in the language modeling approach to information retrieval. the language modeling approach to retrieval has been shown to perform well empirically. one advantage of this new approach is its statistical foundations. however, feedback, as one important component in a retrieval system, has only been dealt with heuristically in this new retrieval approach: the original query is usually literally expanded by adding additional terms to it. such expansion-based feedback creates an inconsistent interpretation of the original and the expanded query. in this paper, we present a more principled approach to feedback in the language modeling approach. specifically, we treat feedback as updating the query language model based on the extra evidence carried by the feedback documents. such a model-based feedback strategy easily fits into an extension of the language modeling approach. we propose and evaluate two different approaches to updating a query language model based on feedback documents, one based on a generative probabilistic model of feedback documents and one based on minimization of the kl-divergence over feedback documents. experiment results show that both approaches are effective and outperform the rocchio feedback approach.
weakly-supervised relation classification for information extraction. this paper approaches the relation classification problem in information extraction framework with bootstrapping on top of support vector machines. a new bootstrapping algorithm is proposed and empirically evaluated on the ace corpus. we show that the supervised svm classifier using various lexical and syntactic features can achieve promising classification accuracy. more importantly, the proposed <i>bootproject</i> algorithm based on random feature projection can significantly reduce the need for labeled training data with only limited sacrifice of performance.
clustering transactions using large items. in traditional data clustering, similarity of a cluster of objects is measured by pairwise similarity of objects in that cluster. we argue that such measures are not appropriate for transactions that are sets of items. we propose the notion of large items, i.e., items contained in some minimum fraction of transactions in a cluster, to measure the similarity of a cluster of transactions. the intuition of our clustering criterion is that there should be many large items within a cluster and little overlapping of such items across clusters. we discuss the rationale behind our approach and its implication on providing a better solution to the clustering problem. we present a clustering algorithm based on the new clustering criterion and evaluate its effectiveness.
intelligent gp fusion from multiple sources for text classification. this paper shows how citation-based information and structural content (e.g., title, abstract) can be combined to improve classification of text documents into predefined categories. we evaluate different measures of similarity -- five derived from the citation information of the collection, and three derived from the structural content -- and determine how they can be fused to improve classification effectiveness. to discover the best fusion framework, we apply genetic programming (gp) techniques. our experiments with the acm computing classification scheme, using documents from the acm digital library, indicate that gp can discover similarity functions superior to those based solely on a single type of evidence. effectiveness of the similarity functions discovered through simple majority voting is better than that of content-based as well as combination-based support vector machine classifiers. experiments also were conducted to compare the performance between gp techniques and other fusion techniques such as genetic algorithms (ga) and linear fusion. empirical results show that gp was able to discover better similarity functions than ga or other fusion techniques.
scalable sequential pattern mining for biological sequences. biosequences typically have a small alphabet, a long length, and patterns containing gaps (i.e., "don't care") of arbitrary size. mining frequent patterns in such sequences faces a different type of explosion than in transaction sequences primarily motivated in market-basket analysis. in this paper, we study how this explosion affects the classic sequential pattern mining, and present a scalable two-phase algorithm to deal with this new explosion. the <i>segment phase</i> first searches for short patterns containing no gaps, called <i>segments</i>. this phase is efficient. the <i>pattern phase</i> searches for long patterns containing multiple segments separated by variable length gaps. this phase is time consuming. the purpose of two phases is to exploit the information obtained from the first phase to speed up the pattern growth and matching and to prune the search space in the second phase. we evaluate this approach on synthetic and real life data sets.
combining structural and citation-based evidence for text classification. this paper discusses how citation-based information and structural content (e.g., title, abstract) can be combined to improve classification of text documents into predefined categories. we evaluate different measures of similarity derived from the citation structure and the structural content of the collection, and determine how they can be fused to improve classification effectiveness. to discover the best fusion framework, we apply genetic programming (gp) techniques. our empirical experiments using documents from the acm digital library and the acm computing classification system show that we can discover similarity functions that work better than using evidence in isolation and whose combined performance through a simple majority voting is comparable to that of support vector machine classifiers.
discovering strong skyline points in high dimensional spaces. current interests in skyline computation arise due to their relation to preference queries. since it is guaraneed that a skyline point will not lose out in all dimensions when compared to any other point in the data set, this means that for each skyline point, there exists a set of weight assignments to the dimensions such that the point will become the top user preference.we believe that the usefulness of skyline points is not limited to such application and can be extended to data analysis and knowledge discovery as well. however, since the skyline of high dimensional datasets (which are common in data analysis applications) can contain too many points, various means must be developed to filter off the less interesting skyline points in high dimensions. in this paper, we will propose algorithms to find a set of interesting skyline points called strong skyline points. extensive experiments show that our proposal is both effective and efficient.
infoanalyzer: a computer-aided tool for building enterprise taxonomies. in this paper we study the problem of collecting training samples for building enterprise taxonomies. we develop a computer-aided tool named infoanalyzer, which can effectively assist the enterprise to prepare large set of samples used for machine learning in text categorization. in our system, the enterprise category tree is initially defined by some keywords, then the google search engine is used to construct a small set of labeled documents, and topic tracking algorithm based on document length normalization is applied to enlarge the training corpus on the bases of the seed stories. furthermore, we design a method to check the consistency of the training corpus. experiments show that the training corpus is good enough for statistical classification methods and meets human's requirements as well.
rule-based query optimization, revisited. we present the architecture and a performance assessment of an extensible query optimizer written in venus. venus is a general-purpose active-database rule language embedded in c++. following the developments in extensible database query optimizers, first in rule-based form, followed by optimizers written as object-oriented programs, the venus-based optimizer avails to the advantages of both. venus' modular structure allows us to go a step further and provide extensibility in search by defining parameterized search components in a declarative form that has the additional effect of integrating heuristic and cost-based optimization. we compare optimizers developed with volcano, opt++ and venus. venus' optimizing compiler yields code whose performance is comparable with volcano and opt++ on smaller queries. the ability to introduce additional pruning heuristics yields better scalability on larger queries. evaluation of the system using quantitative software metrics supports a claim that the venus-based optimizer is more easily maintained and extended than are its predecessors.
architecture of a networked image search and retrieval system. large scale networked image retrieval systems face a number of problems that are not fully satisfied by current systems. on one hand, integrated solutions that store all image data centrally are often limited in terms of scalability and autonomy of data providers. on the other hand, www-based search engines proved to be fairly scalable, and data providers retain their autonomy. however, such engines often confront users with links to servers that are not available or to images that no longer exist, i.e., they are unable to keep their meta-database consistent with the repositories' contents. furthermore, existing solutions often neglect the cost of image delivery. the considerable variations in the effective bandwidth in today's internet lead to highly unpredictable response times, which are often intolerable from the user's point of view.this paper presents the architecture of chariot, a networked image search and retrieval system that tackles these concerns. with respect to scalability and autonomy, chariot follows the approach of www-based search engines by maintaining only the meta-data in a central database. various specialized components (feature extraction, indexes, images servers) are coordinated by a middleware component that employs transactional process management to enforce consistency between the meta-data and all components. moreover, chariot incorporates mechanisms to provide more predictable response times for the image delivery over the internet by employing network-aware image servers. these servers trade off the quality of the images to be delivered with the bandwidth required to transmit the images.
learning cross-document structural relationships using boosting. multi-document discoure analysis has emerged with the potential of improving various information retrieval applications. based on the newly proposed cross-document structure theory (cst), this paper describes an empirical study that uses boosting to classify cst relationships between sentence pairs extracted from topically related documents. we show that the binary classifier for determining existence of structural relationships significantly outperforms the baseline. we also achieve promising results on the multi-class case in which the full taxonomy of relationships are considered.
efficient region-based image retrieval. region-based image retrieval(rbir) was recently proposed as an extension of content-based image retrieval(cbir). an rbir system automatically segments images into a variable number of regions, and extracts for each region a set of features. then, a dissimilarity function determines the distance between a database image and a set of reference regions. unfortunately, the large evaluation costs of the dissimilarity function are restricting rbir to relatively small databases. in this paper, we apply a multi-step approach to enable region-based techniques for large image collections. we provide cheap lower and upper bounding distance functions for a recently proposed dissimilarity measure. as our experiments show, these bounding functions are so tight, that we have to evaluate the expensive distance function for less than 0.5\%of the images. for a typical image database with more than 370,000images, our multi-step approach improved retrieval performance by a factor of more than5 compared to the currently fastest methods.
mining conserved xml query paths for dynamic-conscious caching. existing xml query pattern-based caching strategies focus on extracting the set of frequently issued query pattern trees based on the number of occurrences of the query pattern trees in the history. each occurrence of the same query pattern tree is considered equally important for the caching strategy. however, the same query pattern tree may occur at different timepoints in the history of xml queries. this temporal feature can be used to improve the caching strategy. in this paper, we propose a novel type of query pattern called conserved query paths for efficient caching by integrating the support and temporal features together. conserved query paths are paths in query pattern trees that never change or do not change significantly most of the time (if not always) in terms of their support values during a specific time period. we proposed an algorithm to extract those conserved query paths. by ranking those conserved query paths, a dynamic-conscious caching (dcc) strategy is proposed for efficient xml query processing. experiments show that the dcc caching strategy outperforms the existing xml query pattern tree-based caching strategies.
wam-miner: in the search of web access motifs from historical web log data. existing web usage mining techniques focus only on discovering knowledge based on the statistical measures obtained from the static characteristics of web usage data. they do not consider the dynamic nature of web usage data. in this paper, we focus on discovering novel knowledge by analyzing the change patterns of historical web access sequence data. we present an algorithm called w<small>am</small>-m<small>iner</small> to discover web access motifs (wams). wams are web access patterns that never change or do not change significantly most of the time (if not always) in terms of their support values during a specific time period. wams are useful for many applications, such as intelligent web advertisement, web site restructuring, business intelligence, and intelligent web caching.
an approach for implicitly detecting information needs. searchers can have problems devising queries that accurately express their, often dynamic, information needs. in this paper we describe an adaptive approach that uses unobtrusive monitoring of interaction to help alleviate such problems and support searchers in their seeking. the approach we propose implicitly selects terms to better represent information needs, gathers evidence on potential changes in these needs, and uses this evidence to tailor the result presentation accordingly. a user evaluation of an interface implementing our approach, presented in [7], shows it can select terms that approximate current information needs and provide evidence to track changes in these needs.
discovering frequently changing structures from historical structural deltas of unordered xml. recently, a large amount of work has been done in xml data mining. however, we observed that most of the existing works focus on the snapshot xml data, while xml data is dynamic in real applications. to the best of our knowledge, none of the existing works has addressed the issue of mining the history of changes to xml documents. such mining results can be useful in many applications such as xml change detection, xml indexing, association rule mining, and classification etc. in this paper, we propose a novel approach to discover the <i>frequently changing structures</i> from the sequence of historical <i>structural deltas</i> of unordered xml. to make the structure discovering process efficient, an expressive and compact data model, <b>h</b>istorical-<b>d</b>ocument <b>o</b>bject <b>m</b>odel (<b>h-dom</b>), is proposed. using this model, two basic algorithms, which can discover all the <i>frequently changing structures</i> with only two scans of the xml sequence, are presented. experimental results show that our algorithms, together with the optimization techniques, are efficient and scalable.
using appraisal groups for sentiment analysis. little work to date in sentiment analysis (classifying texts by `positive' or `negative' orientation) has attempted to use fine-grained semantic distinctions in features used for classification. we present a new method for sentiment classification based on extracting and analyzing appraisal groups such as ``very good'' or ``not terribly funny''. an appraisal group is represented as a set of attribute values in several task-independent semantic taxonomies, based on appraisal theory. semi-automated methods were used to build a lexicon of appraising adjectives and their modifiers. we classify movie reviews using features based upon these taxonomies combined with standard ``bag-of-words'' features, and report state-of-the-art accuracy of 90.2%. in addition, we find that some types of appraisal appear to be more significant for sentiment classification than others.
evaluation of hierarchical clustering algorithms for document datasets. fast and high-quality document clustering algorithms play an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters. in particular, hierarchical clustering solutions provide a view of the data at different levels of granularity, making them ideal for people to visualize and interactively explore large document collections.in this paper we evaluate different partitional and agglomerative approaches for hierarchical clustering. our experimental evaluation showed that partitional algorithms always lead to better clustering solutions than agglomerative algorithms, which suggests that partitional clustering algorithms are well-suited for clustering large document datasets due to not only their relatively low computational requirements, but also comparable or even better clustering performance. we present a new class of clustering algorithms called constrained agglomerative algorithms that combine the features of both partitional and agglomerative algorithms. our experimental results showed that they consistently lead to better hierarchical solutions than agglomerative or partitional algorithms alone.
strategies for minimising errors in hierarchical web categorisation. on the web, browsing and searching categories is a popular method of finding documents. two well-known category-based search systems are the yahoo!~and dmoz hierarchies, which are maintained by experts who assign documents to categories. however, manual categorisation by experts is costly, subjective, and not scalable with the increasing volumes of data that must be processed. several methods have been investigated for effective automatic text categorisation. these include selection of categorisation methods, selection of pre-categorised training samples, use of hierachies, and selection of document fragments or features. in this paper, we further investigate categorisation into web hierarchies and the role of hierarchical information in improving categorisation effectiveness. we introduce new strategies to reduce errors in hierarchical categorisation. in particular, we propose novel techniques that shift the assignment into higher level categories when lower level assignment is uncertain. our results show that absolute error rates can be reduced by over 2%.
soft clustering criterion functions for partitional document clustering: a summary of results. recently published studies have shown that partitional clustering algorithms that optimize certain criterion functions, which measure key aspects of inter- and intra-cluster similarity, are very effective in producing hard clustering solutions for document datasets and outperform traditional partitional and agglomerative algorithms. in this paper we study the extent to which these criterion functions can be modified to include soft membership functions and whether or not the resulting soft clustering algorithms can further improve the clustering solutions. specifically, we focus on four of these hard criterion functions, derive their soft-clustering extensions, and present an experimental evaluation involving twelve different datasets. our results show that introducing softness into the criterion functions tends to lead to better clustering results for most datasets.
knowing a web page by the company it keeps. web page classification is important to many tasks in information retrieval and web mining. however, applying traditional textual classifiers on web data often produces unsatisfying results. fortunately, hyperlink information provides important clues to the categorization of a web page. in this paper, an improved method is proposed to enhance web page classification by utilizing the class information from neighboring pages in the link graph. the categories represented by four kinds of neighbors (parents, children, siblings and spouses) are combined to help with the page in question. in experiments to study the effect of these factors on our algorithm, we find that the method proposed is able to boost the classification accuracy of common textual classifiers from around 70% to more than 90% on a large dataset of pages from the open directory project, and outperforms existing algorithms. unlike prior techniques, our approach utilizes same-host links and can improve classification accuracy even when neighboring pages are unlabeled. finally, while all neighbor types can contribute, sibling pages are found to be the most important.
tracking changes in user interests with a few relevance judgments. keeping track of changes in user interests from a document stream with a few relevance judgments is not an easy task. to tackle this problem, we propose a novel method that integrates (1) pseudo-relevance feedback mechanism, (2) assumption about the persistence of user interests and (3) incremental method for data clustering. this approach has been empirically evaluated using reuters-21578 corpus in a setting for information filtering. the experiment results reveal that it significantly improves the performances of existing user-interest-tracking systems without requiring additional, actual relevance judgments.
an adaptive algorithm for learning changes in user interests. in this paper, we describe a new scheme to learn dynamic user's interests in an automated information filtering and gathering system running on the internet. our scheme is aimed to handle multiple domains of long-term and short-term user's interests simultaneously, which is learned through positive and negative user's relevance feedback. we developed a 3-descriptor approach to represent the user's interest categories. using a learning algorithm derived for this representation, our scheme adapts quickly to significant changes in user interest, and is also able to learn exceptions to interest categories.