waashk/dblp huggingface dataset:
incorporating topical support documents into a small training set in text categorization. this paper explores the incorporation of topical support documents into a training set as a means of compensating for a shortage of positive training data in text categorization. to support topical representation, our method applies a simple transformation to documents, i.e., making new documents from existing positive documents by squaring a conventional term weight. the topical support documents thus created not only are expected to preserve the topic, but even improve the topical representation by emphasizing terms with higher weights. experiments with support vector machines showed the effectiveness on rcv1 collection with a small number of positive training data. our topical support representation achieved 52.01% and 8.83% improvements for 33 and 56 categories of rcv1 topic in micro-averaged f1 with less than 100 and 300 positive documents in learning, respectively. result analyses based on robustness indicate that topical support documents contribute to a steady and stable improvement.
combining multiple classifiers for text categorization. a major problem facing online information services is how to index and supplement large document collections with respect to a rich set of categories. we focus upon the routing of case law summaries to various secondary law volumes in which they should be cited. given the large number (> 13,000) of closely related categories, this is a challenging task that is unlikely to succumb to a single algorithmic solution. our fully implemented and recently deployed system shows that a superior classification engine for this task can be constructed from a combination of classifiers. the multi-classifier approach helps us leverage all the relevant textual features and meta data, and appears to generalize to related classification tasks.
query expansion using associated queries. hundreds of millions of users each day use web search engines to meet their information needs. advances in web search effectiveness are therefore perhaps the most significant public outcomes of ir research. query expansion is one such method for improving the effectiveness of ranked retrieval by adding additional terms to a query. in previous approaches to query expansion, the additional terms are selected from highly ranked documents returned from an initial retrieval run. we propose a new method of obtaining expansion terms, based on selecting terms from past user queries that are associated with documents in the collection. our scheme is effective for query expansion for web retrieval: our results show relative improvements over unexpanded full text retrieval of 26%--29%, and 18%--20% over an optimised, conventional expansion approach.
using specification-driven concepts for distributed data management and dissemination. at the mitre corporation-center for advanced aviation system development (caasd), software engineers work closely with both analyst and domain experts to develop software simulations in the air traffic management domain. in this environment, software simulations are applications that take large amounts of real-world operational information, and through calculations, derivations, and display extends the original information to produce some new insight into the domain. this new insight or knowledge typically comes in the form of a pertinent set of data. based on this set of information other research groups can further extend this knowledge. the challenge in this environment is a distributed data management system that will allow a distributed set of researchers to share their extended knowledge. this paper presents the motivation and design of such an architecture to support this collaborative knowledge/data sharing environment. this run-time configurable architecture is implemented using web-based technologies such as the extensible markup language (xml), java servlets, extensible stylesheets (xsl), and a relational database management system (rdbms).
predicting the cost-quality trade-off for information retrieval queries: facilitating database design and query optimization. efficient, flexible, and scalable integration of full text information retrieval (ir) in a dbms is not a trivial case. this holds in particular for query optimization in such a context. to facilitate the bulk-oriented behavior of database query processing, a priori knowledge of how to limit the data efficiently prior to query evaluation is very valuable at optimization time. the usually imprecise nature of ir querying provides an extra opportunity to limit the data by a trade-off with the quality of the answer. in this paper we present a mathematically derived model to predict the quality implications of neglecting information before query execution. in particular we investigate the possibility to predict the retrieval quality for a document collection for which no training information is available, which is usually the case in practice. instead, we construct a model that can be trained on other document collections for which the necessary quality information is available, or can be obtained quite easily. we validate our model for several document collections and present the experimental results. these results show that our model performs quite well, even for the case were we did not train it on the test collection itself.
closure maintenance in an object-oriented query model. an object-algebra is presented as a formal query model for object-oriented data models. the algebra serves not only to access and manipulate the structure and behavior of objects, but it also supports the creation of new objects and the introduction of new relationships into the schema. it provides a more powerful and flexible tool than messages for effectively dealing with complex situations and meeting associative access requirements. operands as well as the results of operations in the proposed algebra are formally characterized as a pair of sets&mdash;a set of objects capturing the states and a set of message expressions comprised of sequences of messages modeling the object behavior. the closure property is achieved in a natural way by letting the results of operations possess the same characteristics as the operands in an algebra expression. some operators of the algebra resemble those of the relational algebra but with different syntax and semantics. additional operators are introduced to complement them. a class is shown to posses the properties of an operand by defining a set of objects and deriving a set of message expressions for it. furthermore, the result of an object algebra expression is shown to have the characteristics of a class whose superclass/subclass relationships with its operand class(es) can be established providing a mechanism to properly and persistently place it in the class lattice (schema).
effects of web document evolution on genre classification. the world wide web is a massive corpus that constantly evolves. classification experiments usually grab a snapshot (temporally and spatially) of the web for a corpus. in this paper, we examine the effects of page evolution on genre classification of web pages. web genre refers to the type of the page characterized by features such as style, form or presentation layout, and meta-content; web genre can be used to tune spider crawling re-visits and inform relevance judgments for search engines. we found that pages in some genres change rarely if at all and can be used in present-day research experiments without requiring an updated version. we show that an old corpus can be used for training when testing on new web pages, with only a marginal drop in accuracy rates on genre classification. we also show that features found to be useful in one corpus do not transfer well to other corpora with different genres.
effective arabic-english cross-language information retrieval via machine-readable dictionaries and machine translation. in cross-language information retrieval (clir), queries in one language retrieve relevant documents in other languages machine-readable dictionary (mrd) and machine translation (mt) are important resources for query translation in clir. we investigate mt and mrd to arabic-english clir. the translation ambiguity associated with these resources is the key problem. we present three methods of query translation using a bilingual dictionary for arabic-english clir. first, we present the every-match (em) method. this method yields ambiguous translations since many extraneous terms are added to the original query. to disambiguate the query translation, we present the first-match (fm) method that considers the first match in the dictionary as the candidate term. finally, we present the two-phase (tp) method. we show that good retrieval effectiveness can be achieved without complex resources using the two-phase method for arabic-english clir. we also empirically evaluate the effectiveness of the mt-based method using short, medium, and long queries from trec. the effects of the query length on the quality of the mt-based clir are investigated.
on arabic search: improving the retrieval effectiveness via a light stemming approach. the inflectional structure of a word impacts the retrieval accuracy of information retrieval systems of latin-based languages. we present two stemming algorithms for arabic information retrieval systems. we empirically investigate the effectiveness of surface-based retrieval. this approach degrades retrieval precision since arabic is a highly inflected language. accordingly, we propose root-based retrieval. we notice a statistically significant improvement over the surface-based approach. many variant word senses are based on an identical root; thus, the root-based algorithm creates invalid conflation classes that result in an ambiguous query which degrades the performance by adding extraneous terms. to resolve ambiguity, we propose a novel light-stemming algorithm for arabic texts. this automatic rule-based stemming algorithm is not as aggressive as the root extraction algorithm. we show that the light stemming algorithm significantly outperforms the root-based algorithm. we also show that a significant improvement in retrieval precision can be achieved with light inflectional analysis of arabic words.
flexible intrinsic evaluation of hierarchical clustering for tdt. the topic detection and tracking (tdt) evaluation program has included a "cluster detection" task since its inception in 1996. systems were required to process a stream of broadcast news stories and partition them into non-overlapping clusters. a system's effectiveness was measured by comparing the generated clusters to "truth" clusters created by human annotators. starting in 2003, tdt is moving to a more realistic model that permits overlapping clusters (stories may be on more than one topic) and encourages the creation of a hierarchy to structure the relationships between clusters (topics). we explore a range of possible evaluation models for this modified tdt clustering task to understand the best approach for mapping between the human-generated "truth" clusters and a much richer hierarchical structure. we demonstrate that some obvious evaluation techniques fail for degenerate cases. for a few others we attempt to develop an intuitive sense of what the evaluation numbers mean. we settle on some approaches that incorporate a strong balance between cluster errors (misses and false alarms) and the distance it takes to travel between stories within the hierarchy.
incremental encoding of multiple inheritance hierarchies. incremental updates to multiple inheritance hierarchies are becoming more prevalent with the increasing number of persistent applications supporting complex objects, making the efficient computation of lattice operations such as greatest lower bound (glb), least upper bound (lub), and subsumption more and more important. general techniques for the compact encoding of a hierarchy are presented. one such method is to plunge the given ordering into a boolean lattice of binary words, leading to an almost constant-time complexity of the lattice operations. the method is based on an inverted version of the encoding of ait-kaci et al. to allow incremental update. simple grouping is used to reduce the code space while keeping the lattice operations efficient. comparisons are made to an incremental version of the range compression scheme of agrawal et al., where each class is assigned an interval, and relationships are based on containment in the interval. the result is two encoding methods which have their relative merits. the former being better for smaller, more structured hierarchies, and the latter for larger, less organized hierarchies.
metadata and data structures for the historical newspaper digital library. we examine metadata and data-structure issues for the historical newspaper digital library. this project proposes to digitize and then do ocr and linguisting processing on several years worth of historical newspapers. newspapers are very complex information objects so developing a rich description of their content is challenging. in addition to frameworks for the logical structure and physical layout, we propose metadata relevant to the image processing and to the historians who will use this collection. finally, we consider how the metadata infrastructure might be managed as it evolves with improved text processing capabilities and how an infrastructure might be developed to support a community of users.
lessons from the implementation of an adaptive parts acquisition eportal. in recent work we have developed a novel approach to the design and implementation of an online portal (eportal) to help application engineers find replacements for electronic parts that have become obsolete (and hence will no longer be produced). our approach makes use of machine learning techniques to improve the performance of a database search function. however, the purpose of this note is not to describe in detail the application nor our technical solution - that has been done elsewhere (see [1,2]). rather, it is our intention to present some of the lessons learned from our project. below, we provide a brief introduction to the technical approach, concentrate on several of the most salient lessons, and conclude with a description of the current state of the project.
improving document representations using relevance feedback: the rfa algorithm. in this paper we present a document representation improvement technique, named the relevance feedback accumulation (rfa) algorithm. using prior relevance feedback assessments and a data mining measure called "support", the algorithm's learning function gradually improves document representations, over time and across users. results show that the modified document representations yield lower dimensionality while improving retrieval effectiveness. the algorithm is efficient and scalable, suited for retrieval systems managing large document collections.
model-guided information discovery for intelligence analysis. intelligence analysis can be aided and guided by models of the analysts' interests and priorities. this paper describes our approach to analyst modeling as part of the ant café project, in which analyst models are used to guide the searching behavior of a swarm of intelligent agents. structural elements of our analyst model include concepts and relations, both of which help to capture the analyst's current interest and concerns. in addition, the concepts and relationships have associated scalar parameters to provide a quantitative measure of the user's level of interest. we have developed algorithms for dynamically adapting the weights and evolving the elements of the model itself. to evaluate these algorithms we have built an analyst modeling environment workbench. we have tested our approach on this workbench using traces generated by human analysts, and have demonstrated improvements over current state of the art search engines.
generating better concept hierarchies using automatic document classification. this paper presents a hybrid concept hierarchy development technique for web returned documents retrieved by a meta-search engine. the aim of the technique is to separate the initial retrieved documents into topical oriented categories, prior to the actual concept hierarchy generation. the topical categories correspond to different semantic aspects of the query. this is done using a 1-of-n automatic document classification, on the initial set of returned documents. then, an individual topical concept hierarchy is automatically generated inside each of the resulted categories. both steps are executed on the fly at retrieval time. due to the efficiency constraints imposed by the web retrieval context, the algorithm only uses document snippets (rather than full web pages) for both document classification and concept hierarchy generation. experimental results show that the algorithm is able to improve the quality of the concept hierarchy presented to the searcher; at the same time, the efficiency parameters are kept within reasonable intervals.
introducing semantics in conceptual schema reuse. although standard components' manufacture and reuse is common practice in many engineering domains (e.g. electrical and mechanical engineering), this is not yet the case with respect to software development. ironically, in such a highly &ldquo;automated&rdquo; domain, users still fail to find available components that match their needs faster than developing them again. the gap between what designers expect from reuse (and how it should be offered), and the actual reuse attempts remains the main barrier. this paper deals with conceptual schema construction in terms of reuse and the respective semantic requirements. it proposes a semantic retrieval mechanism based on imprecise queries for the reuse of conceptual schema components.
extending and inferring functional dependencies in schema transformation. we study the representation, derivation and utilization of a special kind of constraints in multidatabase systems. a major challenge is when component database schemas are <i>schematic discrepant</i> from each other, i.e., data values of one database correspond to schema labels of another. we propose "qualified functional dependencies" (or qualified fds), an extension to conventional fds to formalize integrity constraints in multidatabase systems. we first give inference rules to derive qualified fds in fixed schemas, then study the derivation of qualified fds during the transformations between schematic discrepant schemas. propagation rules are given to derive qualified fds of transformed schemas from qualified fds of original schemas. our work can be used in different stages of building and accessing a multidatabase system, e.g., to detect and resolve value inconsistency in schema integration, to verify lossless schema transformations, to normalize integrated schemas, to verify the integrity of data, and to optimize queries at an integration level. in particular, as an application of our theory, we will use fds to check the validity of schemasql views (schemasql is a powerful multidatabase language).
logical and physical support for heterogeneous data. heterogeneity arises naturally in virtually all real-world data. this paper presents evolutionary extensions to a relational database system for supporting three classes of data heterogeneity: variational, structural and annotational heterogeneities. we define these classes and show the impact of these new features on data storage, data-access mechanisms, and the data-description language. since xml is an important source of heterogeneity, we describe how the system automatically utilizes these new features when storing xml documents.
static score bucketing in inverted indexes. maintaining strict static score order of inverted lists is a heuristic used by search engines to improve the quality of query results when the entire inverted lists cannot be processed. this heuristic, however, increases the cost of index generation and requires complex index build algorithms. in this paper, we study a new index organization based on static score bucketing. we show that this new technique significantly improves in index build performance while having minimal impact on the quality of search results.
learning to summarise xml documents using content and structure. documents formatted in extensible markup language (xml) are becoming increasingly available in collections of various document types. in this paper, we present an approach for the summarisation of xml documents. the novelty of this approach lies in that it is based on features not only from the content of documents, but also from their logical structure. we follow a machine learning like, sentence extraction-based summarisation technique. to find which features are more effective for producing summaries this approach views sentence extraction as an ordering task. we evaluated our summarisation model using the inex dataset. the results demonstrate that the inclusion of features from the logical structure of documents increases the effectiveness of the summariser, and that the learnable system is also effective and well-suited to the task of summarisation in the context of xml documents.
the virgis wfs-based spatial mediation system. the proliferation of spatial data on the internet is beginning to allow a much wider access to data currently available in various geographic information systems (gis). in order to provide effective spatial database integration, we need to provide flexible and powerful gis data integration solutions. indeed, gis are highly heterogeneous: not only they differ by their data representations, but they also offer radically different query languages. a gis mediation approach should provide an integrated view of the data supplied by all sources, and a geographical query language to access and manipulate integrated data.
advances in phonetic word spotting. phonetic speech retrieval is used to augment word based retrieval in spoken document retrieval systems, for in and out of vocabulary words. in this paper, we present a new indexing and ranking scheme using metaphones and a bayesian phonetic edit distance. we conduct an extensive set of experiments using a hundred hours of hub4 data with ground truth transcript and twenty-four thousands query words. we show improvement of up to 15% in precision compare to results obtained speech recognition alone, at a processing time of 0.5 sec per query.
a self-managing data cache for edge-of-network web applications. database caching at proxy servers enables dynamic content to be generated at the edge of the network, thereby improving the scalability and response time of web applications. the scale of deployment of edge servers coupled with the rising costs of their administration demand that such caching middleware be adaptive and self-managing. to achieve this, a cache must be dynamically populated and pruned based on the application query stream and access pattern. in this paper, we describe such a cache which maintains a large number of materialized views of previous query results. cached "views" share physical storage to avoid redundancy, and are usually added and evicted dynamically to adapt to the current workload and to available resources. these two properties of large scale (large number of cached views) and overlapping storage introduce several challenges to query matching and storage management which are not addressed by traditional approaches. in this paper, we describe an edge data cache architecture with a flexible query matching algorithm and a novel storage management policy which work well in such an environment. we perform an evaluation of a prototype of such an architecture using the tpc-w benchmark and find that it reduces query response times by up to 75%, while reducing network and server load.