waashk/dblp huggingface dataset:
terminology mining in social media. the highly variable and dynamic word usage in social media presents serious challenges for both research and those commercial applications that are geared towards blogs or other user-generated non-editorial texts. this paper discusses and exemplifies a terminology mining approach for dealing with the productive character of the textual environment in social media. we explore the challenges of practically acquiring new terminology, and of modeling similarity and relatedness of terms from observing realistic amounts of data. we also discuss semantic evolution and density, and investigate novel measures for characterizing the preconditions for terminology mining.
progressive skyline query evaluation and maintenance in wireless sensor networks. skyline query has been received much attention due to its wide application backgrounds for multi-preference and decision making. in this paper we consider skyline query evaluation and maintenance in wireless sensor networks. we devise an evaluation algorithm for finding skyline points progressively and a maintenance algorithm for skyline maintenance incrementally. we also conduct extensive experiments by simulations to evaluate the performance of the proposed algorithms on various datasets. the experimental results show that the proposed algorithms significantly outperform existing algorithms in terms of network lifetime prolongation.
supporting context-based query in personal dataspace. many users need to refer to content in existing files (pictures, tables, emails, web pages and etc.) when they write documents(programs, presentations, proposals and etc.), and often need to revisit these referenced files for review, revision or reconfirmation. therefore it is meaningful to discover an approach to help users revisit these references effectively. traditional approaches (file explorer, desktop search, and etc.) fail to work in this case. in this paper, we propose an efficient solution for this problem. we firstly define a new personal data relationship: context-based reference (cr), which is generated by user behaviors. we also propose efficient methods to identify cr relationship and present a new type of query based on it: context-based query(c-query), which helps users efficiently revisit personal documents based on cr relationship. our experiments validate the effectiveness and efficiency of our methods.
ipog: fast interactive proximity querying on graphs. given an author-conference graph, how do we answer proximity queries (e.g., what are the most related conferences for john smith?); how can we tailor the search result if the user provides additional yes/no type of feedback (e.g., what are the most related conferences for john smith given that he does not like icml?)? given the potential computational complexity, we mainly devote ourselves to addressing the computational issues in this paper by proposing an efficient solution (referred to as ipog-b) for bipartite graphs. our experimental results show that the proposed fast solution (ipogb) achieves significant speedup, while leading to the same ranking result.
interactive, topic-based visual text summarization and analysis. we are building an interactive, visual text analysis tool that aids users in analyzing a large collection of text. unlike existing work in text analysis, which focuses either on developing sophisticated text analytic techniques or inventing novel visualization metaphors, ours is tightly integrating state-of-the-art text analytics with interactive visualization to maximize the value of both. in this paper, we focus on describing our work from two aspects. first, we present the design and development of a time-based, visual text summary that effectively conveys complex text summarization results produced by the latent dirichlet allocation (lda) model. second, we describe a set of rich interaction tools that allow users to work with a created visual text summary to further interpret the summarization results in context and examine the text collection from multiple perspectives. as a result, our work offers two unique contributions. first, we provide an effective visual metaphor that transforms complex and even imperfect text summarization results into a comprehensible visual summary of texts. second, we offer users a set of flexible visual interaction tools as the alternatives to compensate for the deficiencies of current text summarization techniques. we have applied our work to a number of text corpora and our evaluation shows the promise of the work, especially in support of complex text analyses.
learning to rank using evolutionary computation: immune programming or genetic programming? nowadays ranking function discovery approaches using evolutionary computation (ec), especially genetic programming (gp), have become an important branch in the learning to rank for information retrieval (lr4ir) field. inspired by the gp based learning to rank approaches, we provide a series of generalized definitions and a common framework for the application of ec in learning to rank research. besides, according to the introduced framework, we propose rankip, a ranking function discovery approach using immune programming (ip). experimental results demonstrate that rankip evidently outperforms the baselines. in addition, we study the differences between ip and gp in theory and experiments. results show that ip is more suitable for lr4ir due to its high diversity.
computational community interest for ranking. ranking documents with respect to users' information needs is a challenging task, due, in part, to the dynamic nature of users' interest with respect to a query, which can change over time. in this paper, we propose an innovative method for characterizing the interests of a community of users at a specific point in time and for using this characterization to alter the ranking of documents retrieved for a query. by generating a community interest vector (civ) for a given query, we measure the community interest by computing a score in a specific document or web page retrieved by the query. this score is based on a continuously updated set of recent (daily or past few hours) user-oriented text data. when applying our method in ranking yahoo! buzz results, the civ score improves relevant results by 16% as determined by real-world user evaluation.
expected reciprocal rank for graded relevance. while numerous metrics for information retrieval are available in the case of binary relevance, there is only one commonly used metric for graded relevance, namely the discounted cumulative gain (dcg). a drawback of dcg is its additive nature and the underlying independence assumption: a document in a given position has always the same gain and discount independently of the documents shown above it. inspired by the "cascade" user model, we present a new editorial metric for graded relevance which overcomes this difficulty and implicitly discounts documents which are shown below very relevant documents. more precisely, this new metric is defined as the expected reciprocal length of time that the user will take to find a relevant document. this can be seen as an extension of the classical reciprocal rank to the graded relevance case and we call this metric expected reciprocal rank (err). we conduct an extensive evaluation on the query logs of a commercial search engine and show that err correlates better with clicks metrics than other editorial metrics.
text segmentation via topic modeling: an analytical study. in this paper, the task of text segmentation is approached from a topic modeling perspective. we investigate the use of latent dirichlet allocation (lda) topic model to segment a text into semantically coherent segments. a major benefit of the proposed approach is that along with the segment boundaries, it outputs the topic distribution associated with each segment. this information is of potential use in applications like segment retrieval and discourse analysis. the new approach outperforms a standard baseline method and yields significantly better performance than most of the available unsupervised methods on a benchmark dataset.
mining data streams with periodically changing distributions. dynamic data streams are those whose underlying distribution changes over time. they occur in a number of application domains, and mining them is important for these applications. coupled with the unboundedness and high arrival rates of data streams, the dynamism of the underlying distribution makes data mining challenging. in this paper, we focus on a large class of dynamic streams that exhibit periodicity in distribution changes. we propose a framework, called dmm, for mining this class of streams that includes a new change detection technique and a novel match-and-reuse approach. once a distribution change is detected, we compare the new distribution with a set of historically observed distribution patterns and use the mining results from the past if a match is detected. since, for two highly similar distributions, their mining results should also present high similarity, by matching and reusing existing mining results, the overall stream mining efficiency is improved while the accuracy is maintained. our experimental results confirm this conjecture.
a flexible simulation environment for flash-aware algorithms. in this paper, we present a flexible simulation environment for the performance evaluation of flash-aware algorithms, which is called flash-dbsim. the main purpose of flash-dbsim is to provide a configurable virtual flash disk for upper systems, such as file system and dbms, so that the algorithms in those systems can be easily evaluated on different types of flash disks. moreover, it also offers a prototyping environment for those algorithms inside flash disk, e.g. the algorithms for garbage collection or wear-leveling. after an overview of the general features of flash-dbsim, we discuss the architecture of flash-dbsim. and finally, a case study of flash-dbsim's demonstration is presented.
learning better transliterations. we introduce a new probabilistic model for transliteration that performs significantly better than previous approaches, is language-agnostic, requiring no knowledge of the source or target languages, and is capable of both generation (creating the most likely transliteration of a source word) and discovery (selecting the most likely transliteration from a list of candidate words). our experimental results demonstrate improved accuracy over the existing state-of-the-art by more than 10% in chinese, hebrew and russian. while past work has commonly made use of fixed-size n-gram features along with more traditional models such as hmm or perceptron, we utilize an intuitive notion of "productions", where each source word can be segmented into a series of contiguous, non-overlapping substrings of any size, each of which independently transliterates to a substring in the target language with a given probability. to learn these parameters, we employ expectation-maximization (em), with the alignment between substrings in the source and target word training pairs as our latent data. despite the size of the parameter space and the 2(|w|-1) possible segmentations to consider for each word, by using dynamic programming each iteration of em takes o(m^6 * n) time, where m is the length of the longest word in the data and n is the number of word pairs, and is very fast in practice. furthermore, discovering transliterations takes only o(m^4 * w) time, where w is the number of candidate words to choose from, and generating a transliteration takes o(m2 * k2) time, where k is a pruning constant (we used a value of 100). additionally, we are able to obtain training examples in an unsupervised fashion from wikipedia by using a relatively simple algorithm to filter potential word pairs.
characterizing and predicting search engine switching behavior. search engine switching describes the voluntarily transition from one web search engine to another. in this paper we present a study of search engine switching behavior that combines large-scale log-based analysis and survey data. we characterize aspects of switching behavior, and develop and evaluate predictive models of switching behavior using features of the active query, the current session, and user search history. our findings provide insight into the decision-making processes of search engine users and demonstrate the relationship between switching and factors such as dissatisfaction with the quality of the results, the desire for broader topic coverage or verification of encountered information, and user preferences. the findings also reveal sufficient consistency in users' search behavior prior to engine switching to afford accurate prediction of switching events. predictive models may be useful for search engines who may want to modify the search experience if they can accurately anticipate a switch.
adaptive web mining of bilingual lexicons for cross language information retrieval. bilingual web pages contain abundant term translation knowledge which is crucial for query translation in cross language information retrieval systems. but it is a challenging task to extract term translations from bilingual web pages due to the variation in web page layouts and writing styles. in this paper, based on the observation that translation pairs on the same web page tend to appear following similar patterns, a new extraction model is proposed to adaptively learn extraction patterns and exploit them to facilitate term translation mining from bilingual web pages. experiments reflect that this model can significantly improve extraction coverage while maintaining high accuracy. it improves query translation in cross-language information retrieval, leading to significantly higher retrieval effectiveness on trec collections.
improving web page classification by label-propagation over click graphs. in this paper, we present a semi-supervised learning method for web page classification, leveraging click logs to augment training data by propagating class labels to unlabeled similar documents. current state-of-the-art classifiers are supervised and require large amounts of manually labeled data. we hypothesize that unlabeled documents similar to our positive and negative labeled documents tend to be clicked through by the same user queries. our proposed method leverages this hypothesis and augments our training set by modeling the similarity between documents in a click graph. we experiment with three different web page classifiers and show empirical evidence that our proposed approach outperforms state-of-the-art methods and reduces the amount of human effort to label training data.
an effective model of using negative relevance feedback for information filtering. over the years, people have often held the hypothesis that negative feedback should be very useful for largely improving the performance of information filtering systems; however, we have not obtained very effective models to support this hypothesis. this paper, proposes an effective model that use negative relevance feedback based on a pattern mining approach to improve extracted features. this study focuses on two main issues of using negative relevance feedback: the selection of constructive negative examples to reduce the space of negative examples; and the revision of existing features based on the selected negative examples. the former selects some offender documents, where offender documents are negative documents that are most likely to be classified in the positive group. the later groups the extracted features into three groups: the positive specific category, general category and negative specific category to easily update the weight. an iterative algorithm is also proposed to implement this approach on rcv1 data collections, and substantial experiments show that the proposed approach achieves encouraging performance.
db-ir integration and its application to a massively-parallel search engine. nowadays, as there is an increasing need to integrate the dbms (for structured data) with information retrieval (ir) features (for unstructured data), db-ir integration is becoming one of major challenges in the database area[1,2]. extensible architectures provided by commercial object-relational dbms(ordbms) vendors can be used for db-ir integration. here, extensions are implemented using a high-level (typically, sql-level) interface. we call this architecture loose-coupling. the advantage of loose-coupling is ease of implementation. but, loose-coupling is not preferable for implementing new data types and operations in large databases when high performance is required. in this talk, we present a new dbms architecture applicable to db-ir integration, which we call tight-coupling. in tight-coupling, new data types and operations are integrated into the core of the dbms engine in the extensible type layer. thus, they are incorporated as the "first-class citizens"[1] within the dbms architecture and are supported in a consistent manner with high performance. this tight-coupling architecture is being used to incorporate ir features and spatial database features into the odysseus ordbms that has been under development at kaist/aitrc for over 19 years. in this talk, we introduce odysseus and explain its tightly-coupled ir features (u.s. patented in 2002[2]). then, we demonstrate excellence in performance of tight-coupling by showing benchmark results. we have built a web search engine that is capable of managing 100 million web pages per node in a non-parallel configuration using odysseus. this engine has been successfully tested in many commercial environments. this work won the best demonstration award from the ieee icde conference held in tokyo, japan, in april 2005[3]. last, we present a design of a massively-parallel search engine using odysseus. recently, parallel search engines have been implemented based on scalable distributed file systems (e.g., gfs). nevertheless, building a massively-parallel search engine using a dbms can be an attractive alternative since it supports a higher-level (i.e., sql-level) interface than that of a distributed file system while providing scalability. the parallel search engine designed is capable of indexing 30 billion web pages with a performance comparable to or better than those of state-of-the-art search engines.
mining and ranking streams of news stories using cross-stream sequential patterns. we present a new method for mining and ranking streams of news stories using cross-stream sequential patterns and content similarity. in particular, we focus on stories reporting the same event across the streams within a given time window, where an event is defined as a specific thing that happens at a specific time and place. for every discovered cluster of stories reporting the same event we create an itemset-sequence consisting of stream identifiers of the stories in the cluster, where the sequence is ordered according to the timestamps of the stories. furthermore, we record exact timestamps and content similarities between the respective stories. given such a collection of itemset-sequences we use it for two tasks: (i) to discover recurrent temporal publishing patterns between the news streams in terms of frequent sequential patterns and content similarity and (ii) to rank the streams of news stories with respect to timeliness of reporting important events and content authority. we demonstrate the applicability of the presented method on a multi-stream of news stories was gathered from rss feeds of major world news agencies.
anchorwoman: top-k structured mobile web search engine. with advances in technology, mobile handheld devices-such as pdas-have become very popular. in many real-life situations, users want to find structuring information using these mobile devices, which are convenient to use but have relatively limited resources. in this paper, we present a top-k structured mobile web search engine. it uses a top-k adaptable search-tree method that utilizes hierarchical structure of hypermedia objects to effectively look for structuring information from the mobile web model. the engine, which is implemented in the mobile environment, provides users with top-k adaptive web search recommendations for mobile handheld devices.
a system for detecting xml similarity in content and structure using relational database. in this paper, we describe a system incorporating an improved technique that detects the similarity of two xml documents based on content and structure similarity using keys. the technique consists of three major components: a subtree generator and validator, a key generator, and similarity components that compare content and structure of the xml documents. first, an xml document is stored in a relational database and extracted into small subtrees using leaf-node parents. the leaf-node parents are considered as a root of a subtree which is then recursively traversed bottom-up for matching. second, a possible key(s) is identified in order to match xml subtrees from two documents efficiently. key matchings help in reducing the number of comparisons dramatically. in addition, the number of subtrees to be processed is reduced in the subtree validation phase using instance statistics and taxonomic analyzer. the subtrees are matched by the key(s) first and the remaining subtrees are matched by finding degrees of similarity in content and structure. to obtain improved similarity comparison results, xml element names are transformed according to their semantic similarity. the results show that the clustering points are selected appropriately and the overall execution time is reduced dramatically.
joint sentiment/topic model for sentiment analysis. sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. this paper proposes a novel probabilistic modeling framework based on latent dirichlet allocation (lda), called joint sentiment/topic model (jst), which detects sentiment and topic simultaneously from text. unlike other machine learning approaches to sentiment classification which often require labeled corpora for classifier training, the proposed jst model is fully unsupervised. the model has been evaluated on the movie review dataset to classify the review sentiment polarity and minimum prior information have also been explored to further improve the sentiment classification accuracy. preliminary experiments have shown promising results achieved by jst.